October 2019 Volume 34 Number 10...
56
Photometric Standards for Spectroscopy Substrate Design and Signal Variation in SERS Lowering Detection Limits in ICP-MS Laser Bioimaging for Light Sheet Microscopy October 2019 Volume 34 Number 10 www.spectroscopyonline.com 2019 Emerging Leader in Molecular Spectroscopy Emerging Leader Ishan Barman
Transcript of October 2019 Volume 34 Number 10...
Photometric Standards for Spectroscopy
Substrate Design and Signal Variation in SERS
Lowering Detection Limits in ICP-MS
Laser Bioimaging for Light Sheet Microscopy
October 2019 Volume 34 Number 10 www.spectroscopyonline.com
2019 Emerging Leader in Molecular Spectroscopy
Emerging Leader Ishan Barman
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CONTENTS
Spectroscopy (ISSN 0887-6703 [print], ISSN 1939-1900 [digital]) is published monthly by MultiMedia Healthcare LLC 325 W 1st St STE 300 Duluth MN 55802. Spectroscopy is distributed free of charge to users and specifiers of spectroscopic equipment in the United States. Spectroscopy is available on a paid subscription basis to nonqualified readers at the rate of: U.S. and possessions: 1 year (12 issues), $74.95; 2 years (24 issues), $134.50. Canada/Mexico: 1 year, $95; 2 years, $150. International: 1 year (12 issues), $140; 2 years (24 issues), $250. Periodicals postage paid at Duluth, MN 55806 and at additional mailing of fices. POSTMASTER: Send address changes to Spec-troscopy, P.O. Box 6196, Duluth, MN 55806-6196. PUBLICATIONS MAIL AGREEMENT NO. 40612608, Return Undeliverable Canadian Addresses to: IMEX Global Solutions, P. O. Box 25542, London, ON N6C 6B2, CANADA. Canadian GST number: R-124213133RT001. Printed in the U.S.A .
COLUMNS
Atomic Perspectives . . . . . . . . . . . . . . . . . . . . . . . 12Strategies for Achieving the Lowest Possible Detection Limits in ICP-MS
Rene ChemnitzerA laminar flow box for sampling is combined with a positive voltage on the skimmer cone to reduce detection limits to pg/L concentrations in ICP-MS. These improvements reduce the risk of contamination and increase the efficiency of ion sampling and focusing.
Chemometrics in Spectroscopy . . . . . . . . . . . . . . . . . 18Using Reference Materials, Part II: Photometric Standards
Jerome Workman, Jr. and Howard MarkAlignment of the instrument y-axis is a critical step for quantitative and qualitative measurements using spectroscopy. Here, we explain in detail how to use photometric standards for ultraviolet, visible, near infrared, infrared, and Raman spectroscopy.
Lasers and Optics Interface . . . . . . . . . . . . . . . . . . . 30Advances in the Applications of Lasers for Bioimaging: Light Sheet Microscopy
Steve BuckleyLasers allow advances for investigation of biological samples. Discussed here are some of the most interesting recent developments in light sheet microscopy for bioimaging, including a technique that allows for unique viewing of large, intact samples including biopsies.
Spectroscopy Spotlight . . . . . . . . . . . . . . . . . . . . . . 34Detecting and Identifying Food Colorants with SERS
John ChasseSERS is a method that is receiving new attention in the detection, analysis, and identification of both natural and artificial food colorants. Lili He, at the University of Massachusetts, Amherst, recently spoke to Spectroscopy about this important analytical work.
PEER-REVIEWED ARTICLE
Adsorbate-Induced Morphological Changes of PVD-Deposited Nano-Island Film
SERS Substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Daniel T. Kwasnieski and Zachary D. Schultz
The morphology and gap spacing of nano-island film SERS substrates are key factors defining the properties of analyte‒substrate interactions. These results of the study described here have implications for understanding signal variation in SERS and in designing future SERS assays.
FEATURE ARTICLE
The 2019 Emerging Leader in Molecular Spectroscopy Award . . . . . . . . . . . 45 Jerome Workman, Jr.
Ishan Barman, this year’s Emerging Leader in Molecular Spectroscopy award recipient, leads a research team combining spectroscopy, imaging, and chemometrics to seek greater understanding of the pathological changes of human cells and tissues.
v����� 34 n���� 10
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October 2019
Volume 34 Number 10
ON THE WEBWEB SEMINARS
Workflow Breakthroughs That Are Improving Data Quality and Efficiency and Changing Pharmaceutical AnalysisDarren Andrews and Ursula TemsAgilent Technologues
DSOI: Better than Dual-View ICPOlaf Schulz, Spectro Analytical Instruments
Learn How Routine Laboratories Can Benefit from Triple Quadrupole ICP-MS TechnologyDaniel Kutscher, Thermo Fisher ScientificKerstin Vogel, The Dow Chemical CompanyAdrian Seaton, Johnson Matthey
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DEPARTMENTSNews Spectrum . . . . . . . . . . . . . . . . . . . 10Products & Resources . . . . . . . . . . . . . . 52
Calendar . . . . . . . . . . . . . . . . . . . . . . . . . 51Ad Index . . . . . . . . . . . . . . . . . . . . . . . . . 54
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Editorial Advisory Board
www.spec troscopyonl ine .com
Fran Adar Horiba Scientific
Russ Algar University of British Columbia
Matthew J. Baker University of Strathclyde
Ramon M. Barnes University of Massachusetts
Matthieu Baudelet University of Central Florida
Rohit Bhargava University of Illinois at Urbana-Champaign
Paul N. Bourassa Blue Moon Inc.
Michael S. Bradley Thermo Fisher Scientific
Deborah Bradshaw Consultant
Lora L. Brehm The Dow Chemical Company
George Chan Lawrence Berkeley National Laboratory
John Cottle University of California Santa Barbara
David Lankin University of Illinois at Chicago,
College of Pharmacy
Barbara S. Larsen DuPont Central Research and Development
Bernhard Lendl Vienna University of Technology (TU Wien)
Ian R. Lewis Kaiser Optical Systems
Howard Mark Mark Electronics
R.D. McDowall McDowall Consulting
Gary McGeorge Bristol-Myers Squibb
Linda Baine McGown Rensselaer Polytechnic Institute
Francis M. Mirabella Jr. Mirabella Practical Consulting Solutions, Inc.
Ellen V. Miseo Illuminate
Michael L. Myrick University of South Carolina
John W. Olesik The Ohio State University
Steven Ray State University of New York at Buffalo
Jim Rydzak Specere Consulting
Jerome Workman, Jr. Unity Scientific
Lu Yang National Research Council Canada
Spectroscopy ’s Editorial Advisory Board is a group of distinguished individuals assembled to help the publication fulfill its editorial mission to promote the effec-tive use of spectroscopic technology as a practical research and measurement tool. With recognized expertise in a wide range of technique and application areas, board members perform a range of functions, such as reviewing manuscripts, suggesting authors and topics for coverage, and providing the editor with general direction and feedback. We are indebted to these scientists for their contributions to the publica-tion and to the spectroscopy community as a whole.
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www.spec t roscopyonl ine .com10 Spectroscopy 34(10) October 2019
News SpectrumWinner of Spectroscopy’s 2020 Emerging Leader in Atomic Spectroscopy Award Announced
Jacob T. Shelley, the Alan Paul Schulz
Career Development Professor of
Chemistry at Rensselaer Polytechnic
Institute, in Troy, New York, has won
the 2020 Emerging Leader in Atomic
Spectroscopy Award, which is presented
by Spectroscopy magazine. This annual award, recognizes
the achievements and aspirations of a talented young
atomic spectroscopist, selected by an independent
scientific committee. The award will be presented
to Shelley at the 2020 Winter Conference on Plasma
Spectrochemistry (Tucson, Arizona, January 12–18,
2020), where he will give a plenary lecture.
Shelley’s work currently focuses on the development
of plasma-based tools for mass spectrometry, to enable
rapid and sensitive detection and identification of a
broad range of analytes from complex matrices.
One area of focus within this work has been the use of
solution-cathode glow discharge (SCGD) as an ionization
source for mass spectrometry. Shelley showed that SCGD
could be used for the detection of elemental, small
molecule, and biopolymeric species, with detection limits
for elemental species in the low-to-sub-ppb range.
He has also demonstrated the tunable ionization
chemistry possible with the flowing atmospheric-
pressure afterglow plasma ionization source when
coupled with mass spectrometry.
Shelley has published 36 papers in peer-reviewed
journals, and has been an author on multiple oral or
poster presentations at scientific conferences.
He has received several other awards, including the
Bunsen-Kirchhoff Award from the German Working
Group for Applied Spectroscopy (DAAS) of the German
Chemical Society (GDCh), the Tagungsstipendium
Analytische Chemie from the German Chemical Society,
a Humboldt Research Fellowship from the Alexander von
Humboldt Foundation, and a Gordon F. Kirkbright Bursary
Award from the Association of British Spectroscopists. ◾
Microvolume ultraviolet-visible
(UV-vis) spectrophotometers
have been a fairly recent develop-
ment starting from the early 2000s
that allows measurement of sam-
ples down to 0.5 μL. Operation is
nearly the same as a traditional
spectrophotometer, except that
the sample is loaded onto an op-
tic measurement surface, instead
of a cuvette or capillary, and held
in place by surface tension of a
top mirror and the surface. The
light source then passes through
the sample, and the refl ected light from the top mirror is directed
toward the detector, from which transmittance and absorbance
in the ultraviolet and visible spectrum are determined. Diffracting
grating plays an important role in this mechanism by splitting the
light source and allowing the transmittance and absorbance data
to be captured at different wavelengths.
Among the more popular applications of microvolume UV-
vis spectrophotometers are nucleic acid analysis and protein
concentration measurement in biotech, clinical, and academic
laboratories. They can also measure particulates such as
gold nanoparticles, which play a role in molecular diagnostics,
targeted drug delivery, and photothermal therapies. More
importantly, many microvolume UV-vis spectrophotometers are
bundled with a library of contaminate absorbance spectrums to
detect impurities, such as proteins or phenols.
Prior to microvolume analysis, samples were diluted into
large buffer volumes and measured using cuvettes. In 2005,
the fi rst microvolume UV-vis spectrophotometer was the ND-
1000 from the former NanoDrop Technologies (later acquired
by Thermo Scientifi c), which could detect samples micro sam-
ples as tiny as 1 μL. Microvolume methods removed the re-
quirement for dilution by directly
assaying the sample. Microvol-
ume UV-vis spectrophotometer
are more robust, and handle
sample sizes ranging between
0.5 μl and 5 μl. It has been more
common to see even more ver-
satile microvolume UV-vis spec-
trophotometers that have an
optic measurement surface and
cuvette slot for larger samples.
Another important development
has been adjustable mirror arms
to vary the pathlength of a sam-
ple to yield wide dynamic ranges, as seen by Mettler Toledo’s
UV5Nano spectrophotometer.
The entire market for microvolume UV-vis spectrophotometers,
estimated to be more than $150 million in 2018, will grow by
nearly 5% year over year. This growth exceeds traditional spec-
trophotometers and other UV-vis spectrum instruments due to
the low profi le and versatility of their microvolume counterparts.
Pharmaceutical and academia represent the lion’s share of
the market. Microvolume UV-vis spectrophotometers have low
industrial and environmental demand and combined only ac-
counted for 14% of the market share in 2018.
Market size and growth estimates were adopted from TDA’s
Industry Data, a database of technology market profi les and
benchmarks covering laboratory and process analytical
instrumentation that are updated quarterly. It also includes
data from the 2019 Instrument Industry Outlook report from
independent market research fi rm Top-Down Analytics (TDA).
For more information, contact Glenn Cudiamat, general manager,
at (310) 871-3768 or [email protected]. Glenn is
a market research expert who has been covering the analytical
instrumentation industry for more than two decades.
MARKET PROFILE: MICROVOLUME UV-VIS SPECTROSCOPY
Microvolume spectrophotometer demand by application sector for 2018.
Jacob T. Shelley
www.spec t roscopyonl ine .com12 Spectroscopy 34(10) October 2019
Atomic Perspectives
Strategies for Achieving the Lowest
Possible Detection Limits in ICP-MS
This column describes ways of reducing the risk of contamination during sample handling in inductively cou-
pled plasma‒mass spectrometry (ICP-MS) by using a laminar flow box for sample preparation. In addition,
a novel instrument design, such as the ability to apply a positive voltage on the skimmer cone, enhances the
efficiency of ion sampling and focusing. This opens up the capability of ICP-MS to extremely high sensitivity
and the ability to quantify elements at pg/L concentrations, which is exemplified by the determination of a
suite of transition metals and rare earth elements.
Rene Chemnitzer
Mass spectrometry coupled with inductively coupled plasma (ICP-MS) is capable of analyzing almost the entire periodic table of elements in many different
sample matrices. The current generation of instruments offers detection limits for some elements at the picograms per liter (pg/L) level. However, in the determination of trace and ultra-trace elemental concentrations, careful consideration must be given to sample handling, in addition to how the sample solu-tion is presented to the instrument, to fully exploit the detection potential of ICP-MS technology.
The limit of detection (LOD) is defined as the lowest quantity that can be distinguished from a blank with a certain confidence level (typically 99%). It is typically defined by many standards organiza-tions as the minimum measured concentration of a substance that can be reported with 99% confidence that the measured concen-tration is distinguishable from the blank results (1). This means that there is a probability of about 1% for a false positive error (α), meaning a 1% chance of assigning a signal originating from the blank to an analyte. However, at this concentration, there is a 50% chance of assigning an analyte signal to the noise (false negative error (β) = 50%). For that reason, and to minimize the false nega-tive errors, the limit of quantification (LOQ) is often estimated as the concentration, which gives a signal as high as 10x noise of the blank solution measurement above the blank intensity. Hereby the factor 10 is historically established, and commonly chosen in ana-lytical chemistry. The relationship between noise, LOD, and LOQ is
schematically shown in Figure 1. This shows that at an equal blank contamination, a sensitivity advantage of a factor of 10 provided by the ICP-MS instrument, enables significantly lower method LODs to be achieved as compared to lower sensitivity instruments. This is especially important because a major source of contamination is the sample preparation and the reagent chemicals used, as well as the laboratory environment in general. These sources of contami-nation are independent from the ICP-MS instrument used, and can significantly degrade the LOD if not minimized.
Influence of Laboratory Environment, Purity of
Reagents, and Cleanliness of Equipment on the LOD
The contamination of the blank solution has a significant impact on the resulting LOD. Sources for contamination include leaching of elements from flask and container walls and caps, the purity of the reagent chemicals or acids used, and dust particles from the laboratory environment. Common ways to minimize these effects include conditioning the flasks with an acid solution (for example, 1% HNO3), and the use of a laminar flow box for sample prepara-tion, as well as autosamplers covered with a dust box including a HEPA filter. The principles of laminar flow are shown in Figure 2.
A laminar box draws the ambient air and pushes it through a filter and flow straightener. Because the air streams in parallel flow lines, particles are picked up and transported out of the box through the perforated bottom. This lowers the particle concen-tration by factors of 10,000, minimizing the risk of sample con-
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 13
tamination by ambient particles in the laboratory environment. The flow box can be placed onto a common laboratory bench for easy setup of a clean preparation area in an already setup laboratory. Stand-alone flow boxes are an attractive solution if a laboratory is built-up or renewed.
If the lowest possible detection limits are required, high pu-rity chemicals and acids should be used. Acids can be further purified in dedicated sub-boiling distillation systems. Com-mercially available high purity acids provide less than 10 ppt of contamination, but are relatively expensive. A careful sub-boiled acid originating from a more affordable reagent grade quality will have equal or even lower contamination levels.
The tubes used for sample collection, storage, and preparation require special attention as well. The acidic sample matrix can leach elements from the colored caps, resulting in contamination for cer-tain elements like chromium, cobalt, barium, or lead. The tubes
should always be washed and conditioned with the acidified blank to remove minor contaminations.
For automated sample introduction via autosampler, the tubes should be kept closed as long as possible before analysis. Placing the autosampler into a cover with filtered air using a HEPA filter drasti-cally reduces the particle concentration inside the sampler area, and thus minimizes the risk of contamination while waiting for analysis. The sample-containing tubes should only be opened once placed in the autosampler, and the cover subsequently should be closed.
Materials and Methods
Samples and ReagentsAll sample preparation steps were performed in a laminar flow box (Cleanboy, Spetec GmbH) (2).
Sample tubes and containers were conditioned with 1% HNO3
overnight, and carefully rinsed with deionized water before use. For
Figure 1: Relationship between noise, limit of detection (LOD), and limit of
quantification (LOQ). The parameter α denotes the concentration at which
the probability of assigning a blank signal (black) falsely to an analyte equals
1% (false positive error). At this concentration, there is a 50% chance of
assigning an analyte signal (red) to the noise (β, false negative error). Figure 2: Principles of a laminar flow box used in this study.
Figure 3: Four representative calibration graphs for transition metals
(iron, cobalt, silver, and thallium) at sub ng/L (ppt) concentrations.
Figure 4: Four representative calibration graphs for rare earth elements
(cerium, europium, terbium, and holmium) at pg/L (ppq) concentrations.
www.spec t roscopyonl ine .com14 Spectroscopy 34(10) October 2019
this study, all samples and standards were prepared using high purity reagents. The standards and blanks were made with de-ionized water < 0.055 mS (ELGA Lab) and 1% HNO3 (ultrapure quality, Merck), as this is frequently used to stabilize the elements. A diluent solution was prepared into a 1 L PFA bottle, and used to prepare all dilu-tions and blanks. This procedure ensures a constant trace contamination level for all solutions analyzed in this study.
The limits of quantification were de-termined in the first step. Calibration solutions for calibrations with at least five levels were prepared from a multi-element stock solution for transition metals and rare earth elements, with the lowest stan-dard having a concentration close to the limit of quantification.
Instrument Settings and Method ParametersThe ICP-MS instrument used in this study (PlasmaQuant MS, Analytik Jena) (3) was carefully optimized to provide best sensitiv-ity and lowest background levels, such opti-mization as this is crucial for achieving the best detection limits.
Plasma OptimizationThe inductively coupled plasma generates the analyte ions using an argon plasma as the ionization source. Therefore, the efficiency of coupling the high frequency into the plasma is crucial to maximize the degree of ionization. The plasma system used in this technology achieves a cou-pling efficiency of more than 85%. A pro-prietary configuration of coil and plasma reduces unnecessary overheating of the torch, hence decreasing the required coolant gas flow significantly. As a result, this allows the plasma system to run at 7.5 to 10 L/min coolant gas, compared to 16–18 L/min on other ICP systems, with-out affecting plasma robustness and effi-ciency. The forward radio frequency (RF) power is permanently monitored, and the frequency is automatically adapted if the impedance changes due to variations in the sample matrix.
A common performance criterion for plasma systems is the efficiency of ion-ization at the formation of plasma-based molecular ions, like oxides and double-charged species, which is typically evalu-ated by measuring the formation of cerium
Table I: Method parameters used
Parameter Specification
Plasma gas flow 7.5 L/min
Auxiliary gas flow 1.50 L/min
Nebulizer gas flow 1.08 L/min
Spray chamber temperature 3 °C
Rf power 1220 W
sampling depth 5.0 mm
Dwell time 50 ms
Scans per replicate 20 (peak hopping, 1 pt/peak)
No. of replicates per sample 10
iCRC gas He, H2
Detector attenuation None
BOOST voltage 10 V
Table II: Instrument detection limits (IDL) in ng/L for a 1% HNO3 matrix (n.m. = not mea-
sured); these are representative of modern instrumentation IDLs
Instrument Detection Limits [ng/L] in 1% HNO3
Element No Gas Mode H2 He
Li7 0.5 2.5 3.0
Be9 0.4 0.7 1.2
B11 6.9 17 21
Na23 25 13 25
Mg24 1.1 2.0 2.3
Al27 0.9 2.4 3.8
Ca44 n.m. 23 79
Sc45 3.5 n.m. n.m.
Ti49 0.6 n.m. n.m.
V51 2.3 0.8 2.6
Cr52 6.1 0.5 6.1
Mn55 0.7 0.8 0.9
Fe56 n.m. 1.5 n.m.
Fe57 571 76 91
Co59 0.09 0.4 0.1
Ni60 22 10 18
Cu63 0.4 0.7 0.5
Zn66 1.6 2.1 3.2
Ga69 0.1 0.4 0.4
As75 5.4 1.9 n.m.
Se78 15 13 n.m.
Rb85 0.1 0.9 0.3
Sr88 0.04 0.05 0.06
Y89 0.01 n.m. n.m.
Zr90 0.05 n.m. n.m.
Nb93 0.02 n.m. n.m.
Mo95 0.1 n.m. n.m.
Mo98 0.1 n.m. n.m.
Ru101 0.06 n.m. n.m.
Rh103 0.008 n.m. n.m.
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 15
oxide to the cerium ion (CeO+/Ce+), or the barium doubly charged to singly charged ions (Ba++/Ba+). All ratios should be well below 3%, although much lower levels can be achieved by sacrificing sensitivity. The plasma system used in this study has an oxide performance (CeO+/Ce+) of 0.5%; however, maximum sensitivity is obtained at approximately 2% oxides.
Ion Optical SystemSensitivity in ICP-MS also depends on the efficiency of ion sampling from the plasma and ion focusing into the mass analyzer and detector. Therefore, the ICP-MS ion optical system is a key component that determines the performance of the entire system. The central part of the ion optics system in the technology used in this study is an ion mirror that generates a parabolic electrostatic field which reflects analyte ions around 90°. All neutrals pass the elec-trostatic field unaffected, and are removed by the vacuum system. The reflecting sys-tem allows for an optimization of the focus point of the analyte ions into the entrance lens to the quadrupole mass analyzer. This makes it possible to influence the overall sensitivity of the mass spectrometer, as well as the sensitivity for certain regions of the mass spectrum. In particular, the lower or upper mass range can be selected, and the method optimized for special applica-tions, such as the determination of isotope ratios of lithium or boron at the low mass end or lead, uranium, and other elements at the high mass range.
The method used for this study was opti-mized for high sensitivity across the whole mass range to achieve best possible detec-tion capability for all elements. A sensitiv-ity of more than 1.2 million cps for 1 μg/L (ppb) for indium was obtained, while the formation of oxides and doubly charged ions was well below 2% and 3%, respectively.
Interference ManagementThe ion beam entering the mass spectrom-eter does not only contain single positively charged analyte ions or neutral species, but also polyatomic ions that interfere with the analyte elements and masses. Collision−re-action cells (CRCs) are commonly used to eliminate these interferences. The technol-ogy used in this study uses the integrated collision reaction cell (iCRC) (4) to elimi-nate polyatomic ions by collisions with he-
lium. The helium injected into the cell that is integrated into the skimmer cone reduces the kinetic energy of the polyatomic species, which are subsequently separated from the ion beam by kinetic energy discrimination. In addition, hydrogen is used as a reaction gas that converts the polyatomic molecules in other species that are not interfering with the analytes. This mode can be combined
with the “boost” mode, which applies a positive voltage to the skimmer cone to re-tain the sensitivity in reaction cell mode by refocusing the ion beam (5).
Elements that are not subject to poly-atomic interferences were measured in no-gas mode. Argon-based interferences were removed by injecting hydrogen via the iCRC, while helium was used for the
Table II: Instrument detection limits (IDL) in ng/L for a 1% HNO3 matrix (n.m. = not
measured); these are representative of modern instrumentation IDLs (continued)
Instrument Detection Limits [ng/L] in 1% HNO3
Element No Gas Mode H2 He
Pd105 0.05 n.m. n.m.
Ag107 0.03 0.1 0.1
Cd112 0.06 0.1 0.1
In115 0.01 0.03 0.05
Sn120 0.1 n.m. n.m.
Sb121 0.04 n.m. n.m.
Te125 0.4 1.4 0.9
Cs133 0.05 0.09 0.2
Ba138 0.07 0.07 0.10
La139 0.02 n.m. n.m.
Ce140 0.01 n.m. n.m.
Pr141 0.006 n.m. n.m.
Nd146 0.03 n.m. n.m.
Sm147 0.03 n.m. n.m.
Eu153 0.01 n.m. n.m.
Gd157 0.03 n.m. n.m.
Tb159 0.004 n.m. n.m.
Dy163 0.02 n.m. n.m.
Ho165 0.003 n.m. n.m.
Er166 0.009 n.m. n.m.
Tm169 0.004 n.m. n.m.
Yb172 0.02 n.m. n.m.
Lu175 0.003 n.m. n.m.
Hf178 0.02 n.m. n.m.
Ta181 0.005 n.m. n.m.
W182 0.02 n.m. n.m.
Re185 0.01 n.m. n.m.
Ir193 0.01 n.m. n.m.
Pt195 0.04 n.m. n.m.
Au197 0.03 n.m. n.m.
Hg202 0.4 n.m. n.m.
Tl205 0.02 0.03 0.05
Pb206,207,208 0.04 0.03 0.07
Bi209 0.01 0.01 0.02
Th232 0.02 n.m. n.m.
U238 0.01 n.m. n.m.
www.spec t roscopyonl ine .com16 Spectroscopy 34(10) October 2019
removal of all other polyatomic inter-ferences. This approach was used to maintain high sensitivity when injecting hydrogen into the iCRC for interference removal, resulting in very low limits of detection for a suite of multielements. In particular, the 40Ar16O interference was successfully removed in the determina-tion of 56Fe by the iCRC using hydro-gen gas coupled with the boost mode, leading to an iron-detection limit of 1.5 ng/L (ppt). Table I lists the multielement method parameters used.
Results and Discussion
The instrument detection limits (IDL) ob-tained from the calibration curves were calculated by performing a noise analysis of the measured blank solution (3-sigma method), as described earlier (and listed in Table II). The detection limits in the sub ng/L [ppt]) range are the prerequi-site for quantifying elements at ultratrace levels. Therefore, calibration graphs were generated for a suite of multielements, with the lowest calibration point close to the determined detection limit.
CalibrationMany regulated procedures require the first calibration standard to be near the LOQ to prove the linearity and correct-ness of the calibration function in this low concentration range. Calibration curves for 0−100 ng/L (ppt) of iron, co-balt, silver, and thallium, with the lowest standard at 3 or 5 ppt, are exemplified in Figure 3. The low standard devia-tions (from the regression functions) of the individual standards clearly shows that ultra high sensitivity and low noise results in a very high precision, which minimizes uncertainties and enables ac-curate calibration at very low parts per trillion (ppt) concentrations.
The capability of high sensitivity and low background noise also allows for the ultra-trace determination of rare earth elements. This is exemplified in Figure 4, which shows representative calibration curves for cerium, europium, terbium, and holmium using calibration stan-dards from 0-1000 pg/L or parts per quadrillion (ppq) (6).
Conclusion
The instrument detection limits listed
in this study are the concentrations that can be detected in a standard, non-cleanroom environment. For that reason, the contamination of the blank solution is the determining factor for the LOD for elements having a relatively high abun-dance. This highlights the necessity for high purity reagents, acid-washed con-tainers and flasks, together with a clean and dust-free laboratory environment by using a laminar flow box to minimize contamination.
In addition, instrument design and performance optimization are equally important, because ultrahigh sensitiv-ity and low background improve the signal-to-noise ratio, leading to lower detection limits. This is especially the case with low abundance elements, such as rare earth elements, where high sen-sitivity is achieved by careful optimiza-tion of the reflecting ion mirror, leading to pg/L (ppq) detection levels. This kind of performance is ideally suited for geo-chemical and other similar applications that require a precise and accurate quan-titation of rare earth elements in mineral and rock samples. The study has there-fore shown that the current generation of ICP-MS instrumentation can realistically achieve quantitation at the pg/L to ng/L concentration range in many varied and diverse sample matrices (7).
References
(1) “Def init ion and Procedure for the
Determinat ion of the Method De-
tection Limit,” EPA Of fice of Water,
Dec , 2016, ht tps://www.epa.gov/
sites/production/files/2016-12/doc-
uments/mdl-procedure_rev2_12-13-
2016.pdf
(2) “C leanboy c lean room s t a t ion , ”
Spetec GmbH Webs i te: h t tps://
trends .direc t industr y.com/spetec-
gmbh/project-174749-147824.html
(3) “PlasmaQuant MS product details ,”
Analytik Jena Website: https://www.
analytik-jena.com/products/elemen-
tal -analys is/ icp -ms/plasmaquant-
ms/
(4) “PlasmaQuant MS Series: iCRC Tech-
nical Note,” Analy tik Jena Website,
h t tps : //w w w.ana l y t ik- jena .com/
f i leadmin/content/produc ts/Plas-
maQuant_MS/ TechNote_ ICP_MS_
iCRC_en.pdf
(5) “ Improv ing L imi t s o f De tec t ion
Us ing BOOST Technolog y,” Ana -
ly t ik Jena Websi te: ht tps://w w w.
analy t ik- jena .com/f i leadmin/con-
tent/applications/ICP-MS/AppNote_
ICPMS_0024_en_01.pdf
(6) “Analysis of Geological Materials for
Rare Ear th Elements on the Plas-
maQuant® MS Eli te ,” Applicat ion
Note, Analytik Jena Website: https://
www.analy tik-jena.com/fileadmin/
content/appl icat ions/ ICP-MS/Ap-
pNote_ICPMS_0011_en.pdf
(7) “PlasmaQuant applicat ions page,”
Analytik Jena Website: https://www.
analytik-jena.com/products/elemen-
tal-analysis/icp-ms/plasmaquant-ms/
For more information on this topic, please visit our homepage at: www.spectroscopyonline.com
Robert Thomas i s
pr inc ipal o f Sc ient i f ic
Solutions, a consult ing
company t ha t s e r ve s
t h e a p p l i c a t i o n a n d
w r i t ing needs o f t he
t r a c e e l e m e n t u s e r
community. He has worked in the field
of atomic and mass spectroscopy for
more than 40 years and has written over
100 technical publications including a
15-par t tutorial series on ICP-MS. He
recently completed his fourth textbook
entitled Measuring Elemental Impurities
in Pharmaceuticals: A Practical Guide.
He has an advanced degree in analytical
chemistry from the University of Wales,
UK, and is also a Fellow of the Royal
Soc ie t y o f Chemis t r y (FRSC) and a
Chartered Chemist (CChem).
Rene Chemnitzer is an ICP-MS Produc t Manger, with over 15 years experience in the f ield of trace element analys is , for Analy t ik Jena, in Jena, Germany.
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www.spec t roscopyonl ine .com18 Spectroscopy 34(10) October 2019
Chemometrics in Spectroscopy
Using Reference Materials,
Part II: Photometric Standards
Alignment of the y-axis (photometric axis) is a helpful tool for all spectra when used for
both quantitative and qualitative measurements using spectroscopy. The assumptions
of regression are that the data channels or x-axis of data is aligned, and that only ampli-
tude in the y-axis changes with concentration in some mathematically defined manner.
An aligned y-axis provides an improved method to compare spectra, and to relate
amplitude to concentration. The correction of y-axis (amplitude) variation provides a
minimum bias between instruments, and provides a platform for improved calibration
or method transfer between instruments. This article provides a discussion for using
photometric reference materials to align the y-axis for ultraviolet, visible, near infrared,
infrared, and Raman intensity spectroscopic methods. A general method for correcting
each type of absorption-based spectrophotometer is given, as well as the types of refer-
ence standards used for calibration or verification of the photometric scale. For Raman
spectroscopy, a method is described for intensity correction.
Jerome Workman, Jr. and Howard Mark
Photometric accuracy is a formal term used to de-scribe how well a measuring device (spectropho-tometer or illuminance meter) is able to determine
the total energy f lux transmitted through, ref lected from, or scattered from a standard device or standard reference material (SRM). Part I of this series addressed the use of wavelength or wavenumber standards to align the x-axis for spectroscopic data (1); this Part II article addresses the photometric (or y-) axis alignment. The various references to National Institute of Standards and Technolog y (NIST) documents and SR Ms are given throughout this article. Note that equations are described as referenced in (2,3) and that SRM data may be found at the NIST website (https://www.nist.gov/srm).
Volume 14.01 of the American Society for Testing and Materials (ASTM) International document (4,5), describes the measurement of the photometric accuracy for an instru-ment as follows (Sections 20.1 and 20.3):
“Select the appropriate Standard Reference Material and obtain ten successive readings of the apparent absor-bance or transmittance at the specified wavelength. Av-erage the ten readings. The photometric accuracy is the difference between the true absorbance or transmittance value and the average observed value.”
It is further stated that the following apply to the report-ing of photometric accuracy:
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 19
“Report the photometric accuracy in the following order: reference material, wavelength, true absor-bance or transmittance, observed absorbance or transmittance plus or minus the standard deviation.”
Example of Reporting Photometric AccuracyAs an example the stated specifications for photometric accuracy for a test in-strument might be written as:
Photometric Accuracy:±0.002 Au (for range of 0.0 to 0.5 Absorbance units)±0.004 Au (for range of 0.5 to 1.0 Absorbance units)±0.3% TSRM: Measured with NIST 930d filter
Here is an evaluation of these speci-fications in greater detail. A photomet-ric accuracy of “0.002 Au at 0 to 0.5 Au” indicates that, after the instru-ment has been set to zero, a standard sample that transmits between 30.1%
T (0.5 Au) and 100% T (0.0 Au) mea-sures within ±0.002 Au, as compared to the specified value for that reference standard material at any wavelength position (we note that A and Au are used synonymously).
The tolerance for each of the 930d filters has been stated by NIST as ±0.5 percent relative. Thus, a 10% T filter, which corresponds to a measurement of 0.1 T or 1.0 Au for neutral density 930d-type reference standard, would be expected to be within the range of 9.95 to 10.05% T. These values are equivalent to 1.0022 Au to 0.9978 Au. The difference in Au is 1.0022 Au minus 0.9978 Au equals 0.0044 Au. The standard tolerance in Au is 1.0000 ±0.0022 Au, rounded to 1.000 ±0.002 Au. Conservatively, for transmittance, we state 10.00 ±0.03% T; and related to 0.0 Au or 100% T, we specify 100.0 ±0.3% T.
If the stated tolerance for the NIST 930d filter is ±0.002 Au at 1.0 Au, we are stating that our maximum devia-tion in photometric accuracy for our
instrument at 1.0 Au is also ±0.002 Au. For the worst case measurement for the NIST filter, we would have the filter error of ±0.002 added to the in-strument error of ±0.002, to result in a total maximum variation ±0.004 Au error (this is our stated specification for the example given).
And one can readily calculate the ref lectance or transmittance of any measurement given the absorbance of that measurement as follows. For con-version of absorbance (A) to transmit-tance (T), we use equations 1–4. (Note that the relationships between trans-mittance and absorbance are given in Table I.)
A = –log10
= log10
I
I0
( ) ( )I0I [1]
= 10–A =I
I0
I0
I
1
10–A� [2]
= 10–A T=I
I0
[3]
www.spec t roscopyonl ine .com20 Spectroscopy 34(10) October 2019
%T = T x 100 [4]
To convert absorbance (A) to re-f lectance or ref lection (R) we use equations 5–8:
A = –log10
= log10
I
I0
( ) ( )I0I [5]
= 10–A =I
I0
I0
I
1
10–A� [6]
= 10–A R=I
I0
[7]
%R = R x 100 [8]
Photometric Correction
for Absorbance-based
Spectrophotometers
For making photometric calibrations using known reference materials, the ratioed replicate spectral data are retained, along with the replicate spectra of a reference sample and dark background, or dark sample. Equation 9 represents Beer’s law for taking measurements using a spectro-photometer. To convert the light re-f lected or transmitted from a sample (I) ratioed to the incident energy (I0) to Absorbance (A), as a linearized estimate of the spectral response as related to analyte concentration, the following equations 9−13 may be ap-plied.
If only an internal reference is used for spectral collection, then the ab-sorbance spectrum with respect to wavelength is computed as equation 9:
A = –log10
= log10
I
I0
( ) ( )I0I = log10 ( )
RI – D
RI
S – DS
[9]
If an external standard material is used to calibrate an automated in-ternal reference material, then the absorbance spectrum with respect to wavelength is computed as equa-tions 10 and 11:
A = –log10
= log10
I
I0
( ) ( )I0I = log10 ( )
RI+R
Δ−D
RI
S – DS
[10]where:
RΔ=(R
E – D
RE)/(R
I – D
RI) [11]
and where for equations 9−11, S is
Table I: Relationship of transmittance (T) and absorbance unit (Au) values
T (as ppm) %T T 1/T Au
10000000 1000 10 0.1 = 101 –1.0
1000000 100 1 1.0 = 100 0.0
301000 30.1 0.301 3.01 0.5
100000 10.0 0.1 10 = 101 1.0
10000 1 0.01 100 = 102 2.0
1000 0.1 0.001 1000 = 103 3.0
100 0.01 0.0001 10000 = 104 4.0
10 0.001 0.00001 100000 = 105 5.0
1 0.0001 0.000001 1000000 = 106 6.0
Figure 1: Potassium dichromate absorbance and linearity standard traceable to SRM 935a. (Data and
spectrum provided with permission by Starna Ltd., Hainault, IG6 3UT, UK. www.starna.com).
Figure 2: Potassium dichromate absorbance linearity as a photometric standard using twelve concentrations
of standard and four specific wavelength measurements (257, 235, 350, and 313 nm) (Table II from 20–200
mg/L). This is a test for photometric linearity throughout the ultraviolet region. (Data and spectrum provided
with permission by Starna Ltd., Hainault, IG6 3UT, UK. www.starna.com).
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 21
Figure 3: Transmittance of SRM-930d standard set photometric standards; Filter 10-1225, Filter 20-
1255, and Filter 30-1255; data in Table III. (Data and spectrum used with permission from Avian
Technologies, New London, NH).
Figure 4: Russian opal glass glossy reflectance standard measured using 8°/hemispherical spectral
reflectance factor geometry. Mean reflectance over the spectral region shown is 0.967. Measured
photometric data is given in the Table IV. (Data and spectrum used with permission from Avian
Technologies, New London, NH).
Figure 5: R50 Fluorilon and carbon black mixture as a ~50% reflectance standard; data in Table V.
(Spectrum used with permission from Avian Technologies, New London, NH).
the sample measurement; DS is the dark measurement for the sample, RI is the internal reference measure-ment, DR1 is the dark measurement for the internal reference sample, RE is the measurement of the external standard reference material for cal-ibration, DRE is the dark measure-ment for the external standard ref-erence material, and RΔ is the ratio between the dark corrected RE and RI. The final spectrum is simplified to the ratio of (internal reference corrected by the external reference minus dark) d iv ided by (sample minus dark). Note that dark is a measurement where no energy from the source is al lowed to the detec-tor (the source energy is blocked), or where a totally dark sample or light trap is measured. The dark mea-surement represents the dark cur-rent changes within the instrument during a measurement that relate to electronic noise where the detection electronics, computational electron-ics, and control electronics perform basic measurement functions with no imputed energy reaching the de-tector (the source is turned off or blocked using a shutter closure dur-ing the dark measurement).
When a comprehensive method of photometric correction includes a light trap measurement to deter-mine the dark response to correct for optical and window scatter, we use equation 12:
S−DS
A = log10
(RI−D
RI) •( )( )R
E−D
RE− ( )I
W−D
W
R′
I – D′
RI [12]
where the variable terms used include R’I as the photometric correction cali-bration measurement of the internal reference sample, D’RI
is the photo-metric correction calibration mea-surement of the dark signal intensity for the internal reference sample, IW is the intensity measurement of the light trap to determine the window scatter, and DW is the measurement of the dark signal intensity for the light trap measurement.
www.spec t roscopyonl ine .com22 Spectroscopy 34(10) October 2019
Equation 12 is simplif ied for un-derstanding by removing the dark correction terms as equation 13:
[13]S
A = log10
RI • ( )R
E−I
W
R′
I
Ultraviolet (UV)
Photometric Standards
There are several available standards used for calibrating or verifying pho-tometric correction when using ultra-violet spectrophotometers, with potas-sium dichromate demonstrated here. The ultraviolet region is generally considered to include from 190 nm to
380 nm (non-vacuum UV region). The photometric values for UV standards are illustrated in Figures 1−2, and the reference absorbance values are given in the accompanying Table II (refer-ence spectra and data provided with permission by Starna Ltd.).
Visible (Vis)
Photometric Standards
The visible spectral region is gener-ally considered over the range of 380 nm to 750 nm. Two types of available standards are shown, namely NIST SRM 930d and Russian opal glass, which are used for ca librating or verifying photometric alignment of visible spectrophotometers. Photo-metric values for these standards are illustrated in Figures 3−4, and the reference absorbance values are given in the accompanying Tables III and IV.
Near Infrared (NIR)
Reflectance Photometric
Standards
The near infrared spectral region is generally considered to be 780 nm to 2500 nm. Two types of available ref lectance standards are shown, namely R50 (~50% ref lectance), and sintered Fluorilon (PTFE) (R99 or
~99% ref lectance), which are used for calibrating or verifying photometric alignment of NIR spectrophotom-eters. Photometric values for these standards are illustrated in Figures 5 and 6, and the reference ref lectance values are given in the accompany-ing Tables V and VI. (materials noted provided by courtesy by Avian Tech-nologies). Note that in transmittance measurement mode various solvents or dry air may be used to calibrate or verify the transmittance accuracy to zero (0% T).
Figure 6: R99 Fluorilon as a ~99% reflectance standard; data in Table VI. (Spectrum used with
permission from Avian Technologies, New London, NH).
Table III: Measurements for SRM930d at different concentrations (1 nm bandpass)
Transmittance at Wavelength
Sample 440.0 nm 465.0 nm 546.1 nm 590.0 nm 635.0 nm
10-1225 0.0948 0.1106 0.1034 0.0920 0.1007
20-1225 0.1871 0.2084 0.1989 0.1833 0.1953
30-1225 0.3070 0.3411 0.3319 0.3085 0.3166
Absorbance at Wavelength
Sample 440.0 nm 465.0 nm 546.1 nm 590.0 nm 635.0 nm
10-1225 1.0232 0.9562 0.9855 1.0362 0.9970
20-1225 0.7279 0.6811 0.7014 0.7368 0.7093
30-1225 0.5129 0.4671 0.4790 0.5107 0.4996
Averages of 16 measurements: all nominally ±0.0002 T
Table II: Data table for potassium dichromate absorbance and linearity standard traceable to SRM 935a. Values shown
are the absorbance values for each measured wavelength at the designated concentration of potassium dichromate.
(Data and spectrum provided with permission by Starna Ltd., Hainault, IG6 3UT, UK. www.starna.com)
Wavelength
(nm)
20 mg/L
Abs
40 mg/L
Abs
60 mg/L
Abs
80 mg/L
Abs
100 mg/L
Abs
120 mg/L
Abs
140mg/L
Abs
160 mg/L
Abs
180 mg/L
Abs
200 mg/L
Abs
350.0 0.2104 0.4137 0.6289 0.8497 1.0591 1.2713 1.4834 1.6956 1.9078 2.1199
313.0 0.0948 0.1858 0.2834 0.3827 0.4764 0.5718 0.6673 0.7627 0.8581 0.9535
257.0 0.2807 0.5550 0.8469 1.1468 1.4303 1.7177 2.0051 2.2925 2.5799 2.8673
235.0 0.2404 0.4766 0.7280 0.9860 1.2271 1.4738 1.7205 1.9672 2.2139 2.4605
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 23
Table IV: Russian opal glass glossy
reflectance values
Wavelength
(nm)
Glossy Reflectance
Factor
360 0.882
370 0.903
380 0.929
390 0.943
400 0.954
410 0.960
420 0.966
430 0.968
440 0.970
450 0.973
460 0.975
470 0.976
480 0.978
490 0.978
500 0.978
510 0.979
520 0.979
530 0.979
540 0.979
550 0.978
560 0.977
570 0.976
580 0.975
590 0.975
600 0.974
610 0.974
620 0.972
630 0.973
640 0.972
650 0.973
660 0.972
670 0.972
680 0.972
690 0.971
700 0.970
710 0.970
720 0.969
730 0.969
740 0.968
750 0.968
760 0.968
770 0.965
780 0.964
Infrared (NIR) Reflectance
Photometric Standards
The infrared spectral region is gen-erally considered to be def ined as 2500 nm to 25,000 nm (4000 cm-1 to 400 cm-1). Diffuse gold is a primary surface material used for calibrating or verifying ref lectance photomet-ric a lignment of infrared spectro-photometers. Photometric values for this standard are illustrated in Fig-ures 7−8, and the reference ref lec-tance values are given in the accom-
panying Table VII. In transmittance measurement mode dry, purged air may be used to calibrate or verify the transmittance accuracy.
Raman Intensity
Correction Standards
The Raman spectrum correspond-ing to the infrared vibrational re-gion includes 4000 cm-1 to 400 cm-1, with Raman spectra referred to as the Stokes shift region defined from 3500 cm-1 to 50 cm-1. A variety of
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materials are used for calibrating or verifying the intensity scale for Raman spectrometers. Materia ls for this purpose are listed in Table VIII. Materials noted are provided by NIST.
NIST, previously known as the National Bureau of Standards (NBS), provides materials designed as ref-erence sources to verify the perfor-mance character ist ics of Raman
spectrometers of typical instrument design. The main standards are cur-rently used for intensity standard-ization. Table VIII lists the typical materia ls avai lable for Raman in-tensity calibration. In the case of Raman, there are f ive active NIST SRMs for measuring Raman inten-sity, depending upon the excitation laser wavelength used (6–11).
The Raman intensity standards
are certif ied SRMs useful for the correction of the relative intensity of Raman spectra obta ined with inst ruments employ ing speci f ic laser excitation sources. Individual intensity standards have been pro-vided for Raman excitation wave-lengths of 488 nm, 532 nm, 633 nm, 785 nm, 830 nm, and 1064 nm. For these standards, the relative inten-sity of the glass luminescence has been ca l ibrated at NIST using a uniform- white-light-source, with an integrat ing sphere col lect ion design. The shape of the mean lu-minescence spectrum of the SRM glass is described using a polyno-mial expression for relative spectral intensity versus Raman shift wave-number (cm-1) based on the specific excitation laser wavelength used (in nanometers).
The spectral correction for any Raman spectrometer is determined by measur ing t he luminescence spectrum of the SRM, applying the polynomial model, and computing the spectral intensity-response cor-rection for any Raman instrument. The application of the spectral in-tensity correct ion el iminates the inst rument induced spectra l a r-tifacts to provide a more uniform calibrated Raman spectrum.
In order for a Raman spectrom-eter to be calibrated for intensity t he Ra ma n wavenu mber a x is i s corrected using the ASTM E1840-96 protocol (12). The laser excitation is aimed at the frosted surface of the glass, to minimize variation in the scattering response. The intensity correction must be completed over the same Raman shift range as that intended for sample measurement.
T he re lat ive i ntens i t y of t he measured Raman spectrum of the sample can be corrected for the instrument-specif ic response by a computational procedure that uses a correction curve. This curve is generated using the certified model and the measured luminescence spectrum of the SRM glass. For the spectral range of certif ication, Δυ = 200 cm-1 to 3500 cm-1, the ele-ments of the certified relative mean
Table V: R50 Fluorilon reflection data from NIST traceable instrument
Wavelength (nm) R50 Refl. Wavelength (nm) R50 Refl.
800 0.464 1625 0.446
825 0.463 1650 0.445
850 0.461 1675 0.444
875 0.460 1700 0.444
900 0.461 1725 0.444
925 0.461 1750 0.443
950 0.462 1775 0.443
975 0.461 1800 0.442
1000 0.459 1825 0.442
1025 0.460 1850 0.441
1050 0.458 1875 0.442
1075 0.458 1900 0.439
1100 0.457 1925 0.439
1125 0.457 1950 0.440
1150 0.456 1975 0.439
1175 0.456 2000 0.441
1200 0.454 2025 0.441
1225 0.454 2050 0.441
1250 0.453 2075 0.441
1275 0.452 2100 0.441
1300 0.452 2125 0.442
1325 0.450 2150 0.438
1350 0.450 2175 0.437
1375 0.449 2200 0.437
1400 0.448 2225 0.434
1425 0.448 2250 0.434
1450 0.449 2275 0.436
1475 0.448 2300 0.436
1500 0.447 2325 0.436
1525 0.447 2350 0.436
1550 0.447 2375 0.436
1575 0.446 2400 0.438
1600 0.447 2425 0.438
2450 0.436
2475 0.437
2500 0.435
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 25
Table VI: R99 Fluorilon reflection data from NIST traceable instrument (3 samples)
Wavelength (nm)Calibrated (#1) R99
Refl.
Calibrated (#2) R99
Refl.
Calibrated (#3)
R99 Refl.
1000 0.981 0.981 0.981
1025 0.980 0.983 0.980
1050 0.982 0.981 0.982
1075 0.982 0.982 0.981
1100 0.980 0.983 0.981
1125 0.980 0.981 0.980
1150 0.978 0.984 0.980
1175 0.980 0.980 0.982
1200 0.979 0.981 0.980
1225 0.980 0.981 0.981
1250 0.979 0.980 0.979
1275 0.979 0.981 0.980
1300 0.978 0.981 0.980
1325 0.978 0.978 0.979
1350 0.977 0.979 0.978
1375 0.976 0.979 0.978
1400 0.975 0.979 0.977
1425 0.978 0.979 0.979
1450 0.977 0.978 0.978
1475 0.977 0.980 0.977
1500 0.976 0.978 0.978
1525 0.976 0.978 0.977
1550 0.977 0.978 0.977
1575 0.977 0.979 0.977
1600 0.977 0.979 0.978
1625 0.975 0.977 0.976
1650 0.975 0.977 0.977
1675 0.975 0.976 0.975
1700 0.974 0.975 0.976
1725 0.974 0.975 0.976
1750 0.972 0.976 0.973
1775 0.974 0.976 0.976
1800 0.975 0.976 0.976
1825 0.972 0.971 0.973
1850 0.971 0.969 0.971
1875 0.969 0.971 0.970
1900 0.967 0.970 0.969
1925 0.967 0.969 0.968
1950 0.967 0.968 0.968
1975 0.964 0.966 0.966
2000 0.962 0.965 0.964
2025 0.957 0.959 0.961
2050 0.948 0.954 0.950
2075 0.945 0.949 0.949
2100 0.941 0.948 0.944
2125 0.934 0.940 0.938
spec t ra l intensit y of SR M 2241, ISRM(Δυ), are computed according to equation 14:
ISRM (Δυ) = A0 + A
1 × (Δυ)1
+ A
2 × (Δυ)2
+ A3 × (Δυ)3 + A
4 × (Δυ)4 + A
5 × (Δυ)5 [14]
where (Δυ) is the wavenumber value in units of Raman shift (cm–1) and the An’s are the coeff icients listed in Table IX (14–20). The elements of ISRM(Δυ) are obtained by evalu-ating equation 14 at the same data point spacing used for the acquisi-tion of the luminescence spectrum of the SRM and of the Raman spec-trum of the sample. ISRM(Δυ) has been normalized to unity and is a relative unit expressed in terms of photons. The data sets that are the measured glass luminescence spec-t r u m, SSR M , a nd t he measured Ra ma n spect rum of t he sa mple , SMEAS, and are in units of Raman shif t (cm–1). The elements of the correct ion cur ve ICORR(Δυ), de-fined by equation 15, are obtained from ISRM(Δυ) and the elements of the glass luminescence spectrum, SSRM(Δυ), by:
ICORR(Δυ) = ISRM(Δυ) / SSRM(Δυ) [15]
T h e e l e m e n t s o f t h e i n t e n -sit y-corrected Raman spectrum, SCOR R(Δυ), a re der ived by mu l-t iplication of the elements of the measured Raman spectrum of the sa mple , SMEAS (Δυ), by t he e le-ments of the correct ion curve as equation 16 (13).
SCORR(Δυ) = SMEAS(Δυ) × ICORR(Δυ) [16]
The cer t i f ied model, equat ion 14, is certified for use between 200 cm–1 and 3500 cm–1. The certif ied model is intended as a simple nu-merical descriptor of the spectral response observed over the wave-number range studied. It is not in-tended as a physically meaningful model. The model coefficients listed in Table IX cannot be used to ex-trapolate the limits of certification without incurring significant error. Extrapolation of the model outside the certification limits of 200 cm–1
www.spec t roscopyonl ine .com26 Spectroscopy 34(10) October 2019
and 3500 cm–1 is not a supported by use of this SRM. This SRM is not intended for use as a standard for the determination of absolute spec-tral irradiance or radiance.
T he e qu at ion de s c r ibi ng t he mean luminescence spectrum of the glass SRM is given in equation 14, where (Δυ) is the wavenumber in units of Raman shift (cm–1). For
correction of spectra where the x-axis is in wavelength with units of nanometers, the same model coef-f icients can be used to ca lcu late ISRM(λ) through the following co-ordinate transformation as equa-tions 17 and 18:
Figure 7: Diffuse gold photometric reflectance standard measured using 8°/hemispherical
spectral reflectance factor geometry with a calibrated FT-IR instrument. Mean reflectance
over the spectral wavelength region (in micrometers) shown is 0.941. Measured
photometric data is given in Table VII. (Data and spectrum used with permission from
Avian Technologies, New London, NH).
Table VI: R99 Fluorilon reflection data from NIST traceable instrument (3 samples)
(continued)
Wavelength (nm)Calibrated (#1) R99
Refl.
Calibrated (#2) R99
Refl.
Calibrated (#3)
R99 Refl.
2150 0.935 0.940 0.939
2175 0.946 0.951 0.948
2200 0.954 0.958 0.956
2225 0.954 0.960 0.957
2250 0.955 0.959 0.958
2275 0.954 0.954 0.956
2300 0.948 0.950 0.952
2325 0.943 0.943 0.946
2350 0.937 0.940 0.940
2375 0.934 0.943 0.939
2400 0.929 0.935 0.935
2425 0.933 0.933 0.935
2450 0.933 0.938 0.934
2475 0.930 0.936 0.932
2500 0.939 0.931 0.937
Table VII: Diffuse gold reflectance
values in wavenumber and wavelength
space for specimen measured
in Figures 7 and 8
Wavenumber
(cm–1)
Wavelength
(μm)Reflectance
5000 2.00 0.944
4000 2.50 0.928
3333 3.00 0.936
2857 3.50 0.947
2500 4.00 0.940
2222 4.50 0.941
2000 5.00 0.944
1818 5.50 0.937
1667 6.00 0.954
1538 6.50 0.957
1429 7.00 0.953
1333 7.50 0.940
1250 8.00 0.942
1176 8.50 0.954
1111 9.00 0.954
1053 9.50 0.947
1000 10.00 0.954
952 10.50 0.942
909 11.00 0.947
870 11.50 0.947
833 12.00 0.948
800 12.50 0.944
769 13.00 0.943
741 13.50 0.939
714 14.00 0.943
690 14.50 0.939
667 15.00 0.942
645 15.50 0.928
625 16.00 0.931
606 16.50 0.937
588 17.00 0.943
571 17.50 0.951
556 18.00 0.946
541 18.50 0.929
526 19.00 0.916
513 19.50 0.919
500 20.00 0.922
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 27
ISRM(λ) = [107/λ2]
×
[A
0+A
1 × Z1+A
2 ×
Z2+A3 × Z3+A
4 × Z4+A
5 × Z5]
[17]
Z = 107 ×
[(1.0/λ
L)−(1.0/λ)] [18]
where λL is the wavelength of the laser in nanometers and λ is the spectral wavelength in nanometers. The prefactor of 107 in the first term of equation 17 is needed only if it is desired to preserve the numeri-cal value of spectral areas computed relative to the two x-axis coordinate systems. Definition of this is from reference (7), with the NIST basic applicat ion of the intensity stan-dard.
A comprehensive paper describ-ing the complete process of ca l i-brating Raman instruments for in-tensity using various laser sources and instrument types a long with the applicat ion of NIST SR Ms is given in reference 20 as an Applied Spectroscopy feature article. This paper is recommended for anyone that would practice Raman intensity correction for analytical measure-ments, and for understanding the details of this process.
References
(1) J. Workman, Jr. and H. Mark, Spec-
troscopy 34(2), 36–52 (2019).
(2) J . Workman, The Concise Hand-
book of Analy tical Spectroscopy:
Physical Foundations, Techniques,
Instrumentation and Data Analysis,
J. Workman, Ed. (World Scientif ic
Publishing-Imperial College Press,
Singapore, 2016). In 5 Volumes, UV,
Vis, NIR, IR, and Raman Volumes 1,
pp. 163-214; Volume 2, pp. 217-278;
Volume 3, pp. 363-424; Volume 4,
pp. 323-360; Volume 5, pp. 229-
250. ISBN-13: 978-9814508056,
2016). (Permission from NIST for
data and spectra related to SRM
materials).
(3) Each table refers to specif ic NIST
SRM datasheets and published ref-
erences and as referred to in refer-
ence (2).
(4) Standard Practice for Describing
and Measuring Performance of Ul-
traviolet, Visible, and Near-Infrared
Spec t rophotometers . Amer ican
Society for Testing and Materials,
ASTM Internat ional designat ion
E 275-08(2013), Philadelphia, PA
(2013).
(5) Standard Terminology Relating to
molecular Spectroscopy; American
Society for Testing and Materials,
ASTM International designation
E 131-10, 2010, Philadelphia, PA
(2010).
(6) NIST Raman SRM catalog website:
ht tp://nvlpubs.nist .gov/nistpubs/
SpecialPublications/NIST.SP.260-
176.pdf, pp. 162–163 (accessed
Sept. 9, 2017).
(7) Standard Reference Material (SRM)
2241 Certificate, Relative Intensity
Correc t ion Standard for Raman
Spectroscopy: 785 nm Excitation,
Figure 8: Diffuse gold photometric reflectance standard measured using 8°/hemispherical
spectral reflectance factor geometry with a calibrated FT-IR instrument. Mean reflectance
over the spectral region (in wavenumbers) shown is 0.941. Measured photometric data
is given in Table VII. (Data and spectrum used with permission from Avian Technologies,
New London, NH).
Table VIII: NIST SRMs for Raman spectrometer calibration (6)
SRM No.Laser Excitation
(nm)Material SRM Dimensions
2241 785 nm
Chromium-doped (mole fraction of 0.02% Cr2O3)
sodium borosilicate matrix glass
10 mm in width × 10 mm in length × 1.65
mm in thickness
2242 532 nmManganese-doped (0.15
wt % MnO2) borate matrix glass
10.7 mm in width × 30.4 mm in length × 2.0 mm in thickness
2243 488 and 514.5 nm Discontinued NA
2244 1064 nm Doped (mass fraction 0.7% Cr2O3) oxide in a borosili-
cate matrix glass
0.7 mm in width × 30.4 mm in length × 2.0 mm in thickness
2245 633 nmBismuth-doped (0.11%
mole fraction) oxide in a phosphate matrix glass.
10 mm in width × 10 mm in length × 1.65
mm in thickness
2246 830 nmChromium-doped (0.30% mole fraction) oxide in a borosilicate-matrix glass.
10 mm in width × 10 mm in length × 1.65
mm in thickness
www.spec t roscopyonl ine .com28 Spectroscopy 34(10) October 2019
Nat ional Ins t i tute of Standards
and Technology, Gaithersburg, MD
20899, 6 pages (28 February 2014).
(8) Standard Reference Material (SRM)
2242 Certificate, Relative Intensity
Correc t ion Standard for Raman
Spectroscopy: 532 nm Excitation,
Nat ional Ins t i tute of Standards
and Technology, Gaithersburg, MD
20899, 6 pages (22 October 2013).
(9) Standard Reference Material (SRM)
2244 Certificate, Relative Intensity
Correc t ion Standard for Raman
Spectroscopy: 1064 nm Excitation,
Nat ional Ins t i tute of Standards
and Technology, Gaithersburg, MD
20899, 6 pages (03 December
2009).
(10) Standard Reference Material (SRM)
2245 Certificate, Relative Intensity
Correc t ion Standard for Raman
Spectroscopy: 633 nm Excitation,
Nat ional Ins t i tute of Standards
and Technology, Gaithersburg, MD
20899, 6 pages (27 September
2011).
(11) Standard Reference Material (SRM)
2246 Certificate, Relative Intensity
Correc t ion Standard for Raman
Spectroscopy: 830 nm Excitation,
Nat ional Ins t i tu te of Standards
and Technology, Gaithersburg, MD
20899, 7 pages (31 August 2012).
(12) ASTM E1840-96(2007) Standard
Guide for Raman Shif t Standards
for Spectrometer Calibration (ASTM
International, West Conshohocken,
Pennsylvania, 2007).
(13) K. J. Frost and R.L . McCreery, Appl.
Spec t rosc . 52 (12) , 1614 –1618
(1998).
(14) W. May, R. Parris, C. Beck II, J. Fas-
sett, R. Greenberg, F. Guenther, G.
Kramer, S. Wise, T. Gills, J. Colbert,
R. Gettings, and B. MacDonald, Defi-
nition of Terms and Modes Used at
NIST for Value-Assignment of Refer-
ence Materials for Chemical Mea-
surements; NIST Special Publication
260-136 (2000); http://www.nist.
gov/srm/publications.cfm (accessed
Sept. 23, 2019).
(15) A.L. Rukhin, Metrologia 46, 323–331
(2009).
(16) R. Dersimonian and N. Laird, Con-
trol. Clin. Trials 7(3) 177–188 (1986).
(17) S.D. Horn, R.A. Horn, and D.B. Dun-
can, J . Am. Stat . Assoc . 70(350),
380–385 (1975).
(18) JCGM 100:2008; Evalua t ion of
Measurement Data — Guide to the
Expression of Uncertainty in Mea-
surement (GUM 1995 with Minor
Corrections); Joint Committee for
Guides in Metrology (2008); http://
www.bipm.org/utils/common/doc-
uments/jcgm/JCGM_100_2008_E.
pdf (accessed Sept. 23, 2019).
(19) B.N. Taylor, C.E. Kuyatt, Guidelines
for Evaluating and Expressing the
Uncertainty of NIST Measurement
Results; NIST Technical Note 1297
(U.S. Government Printing Office:
Washing ton, DC ,1994); ht tps://
www.nist .gov/pml/nist-technical-
note-1297 (accessed Sept. 23, 2019).
(20) S.L. Choquette, E.S. Etz, W.S. Hurst,
D.H . Blackburn , and S .D. Leigh ,
Appl . Spec trosc . 61(2), 117–129
(20 07) . h t tps : //w w w.n i s t . gov/
publications/relative-intensity-cor-
rection-raman-spectrometers-nist-
srms-2241-through-2243-785-nm
(accessed Sept. 23, 2019).
For more information on this topic, please visit:
www.spectroscopyonline.com
Jerome Workman Jr.
serves on the Editorial
Adv i so r y Board o f
Spectroscopy and is
the Senior Technical
Editor for LCGC and
S p e c t r o s c o py . H e
is also a Cer t i f ied Core Adjunc t
Professor at U.S. National University
in L a Jo l la , Ca l i fo rn ia . He was
formerly the Executive Vice President
of Research and Engineer ing for
Unity Scientific and Process Sensors
Corporation.
H o w a r d M a r k
serves on the Editorial
Adv i sor y Board o f
Spec t roscopy, and
r uns a consu l t i ng
s e r v i c e , M a r k
Electronics, in Suffern,
New York. Direct correspondence to:
Table IX: Coefficients of the certified polynomial (a) and of the 95 % confi-
dence curves (b) describing the mean luminescence spectrum of SRM 2241 for
785 nm excitation. (valid for temperatures of 20 °C to 25 °C) (14–19).
Polynomial
Coefficient
Certified Value
Polynomial Coefficient
( c )
Polynomial Coefficient ( c ) of the ± 95%
Confidence Curves ( b )
±95 % CC ±99 % CC
A0 9.71 937 E–02 1.04 276 E–01 9.01 111 E–02
A1 2.28 325 E–04 2.39 131 E–04 2.17 519 E–04
A2 –5.86 762 E–08 –7.81 489 E–08 –3.92 035 E–08
A3 2.16 023 E–10 2.32 243 E–10 1.99 803 E–10
A4 –9.77 171 E–14 –1.03 769 E–13 –9.16 653 E–14
A5 1.15 596 E–17 1.23 774 E–17 1.07 417 E–17
(a) A NIST certified value represents data, reported in an SRM Certificate, for which NIST has the highest confidence in its accuracy in that all known or suspected sources of bias have been fully investigated or taken into account (14).
(b) The consensus curve is a point-by-point weighted mean of the average responses of three instruments (15,16), fitted by the polynomial model. The uncertainty curves are polynomial models of point-by-point expanded uncertainties, with coverage factor k = t0.975,2 = 4.303 (95% confidence), calculated by combining a between-instrument variance incorporating inter instrumental Type B uncertainties with a pooled within-method variance, following the ISO/JCGM Guide (18,19).
(c) Where ISRM(Δυ) = A0 + A1 × (Δυ)1 + A2 × (Δυ)2 + A3 × (Δυ)3 + A4 × (Δυ)4 + A5 × (Δυ)5, for Δυ = 200 cm-1 to 3500 cm-1 Raman Shift relative to 785 nm excitation. Raman shift is expressed in cm-1.
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www.spec t roscopyonl ine .com30 Spectroscopy 34(10) October 2019
Lasers and Optics Interface
Advances in the Applications
of Lasers for Bioimaging:
Light Sheet Microscopy
The use of laser light for investigation of biological samples is continuing to advance rapidly.
Here, we briefly discuss some of the most interesting recent developments in light sheet
microscopy. New methods allow for large, intact samples to be measured, as miniature
designs, modular systems, and optical clearing techniques are allowing new, unique views
into samples and biopsies.
Steve Buckley
Spectral imaging is valuable in applications from industrial controls to agronomy. Perhaps nowhere is there more excit-ing and new application of spectral imaging than in biol-
ogy and medicine. Problems range from in vivo and in vitro studies of living organisms, to developmental studies of whole organisms and of sampled tissue. Pathology is rapidly adopting experimental methods, including laser excitation.
For high-resolution images, confocal microscopy is the gold standard. A simple schematic representation of confocal micros-copy is shown in Figure 1. Because the light source and the detec-tor are spatially focused, with apertures blocking out-of-focus light, confocal microscopy can yield very high spatial resolution information. However, this information comes at a cost—getting information about the entire sample requires time-consuming scanning of the focal volume through the entire volume of the sample. Although there are numerous, continually improving methods for speeding up the information transfer, when focal volumes are small, it is still common for a moderate-sized vol-ume to take 12−18 h to scan.
To speed up data transfer, a logical thought would be to il-luminate a plane of the sample and to detect orthogonally to the illuminated plane with an imaging device. This takes the single-
point detection method of confocal microscopy and translates it into a many (illuminated) to many (detected) method. So-called
“light sheet microscopy” has been around in concept for a century or more, but it took the advent of lasers and digital imagers to make it practical.
Figure 2 shows a simple schematic representation of a light sheet microscope (LSM). Commercial versions of LSMs have been available for some time, most notably the Zeiss Lightsheet Z.1 and the Luxendo (now owned by Bruker) series of selective plane illumination microscope (SPIM) systems. These advanced systems have proven successful for measurements of many types of biological samples. Figure 3 shows an image of first cell di-vision in a mammalian zygote, using the system that was the precursor of the Luxendo InVi SPIM at the European Molecular Biology Laboratory (Luxendo is a spin-out of that laboratory). This image, taken from a mouse embryo, shows the formation of two spindles during the cell division and replication, and indi-cates that male and female genetic material is still separated. This publication in 2018 overturned the then current understanding, and was subsequently published in Science (1).
Two prerequisites for successful measurement are 1) that the sample needs to be optically clear at the illumination laser
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 31
wavelength and at any imaging or fluo-rescence wavelength, and 2) that the sample needs to be held in such a way that it is optically accessible in the laser plane and for imaging viewing. In the early 20th century, German doctor Wer-ner Spalteholz first discovered a method for making human tissue transparent by treating the samples using liquids with a similar index of refraction as the tissue. He displayed a large collection of “cleared” specimens at an exhibition in Dresden in 1911 as part of his study of anatomy. Sophisticated technology for sample clearing has been developed over the last decade, parallel with and driven by the rise of LSM technology. Some of the more popular protocols include CLARITY (2), which was developed in the Stanford lab of Karl Deisseroth, clear, unobstructed brain/body imaging cocktails and com-putational analysis (CUBIC), and 3D im-aging of solvent-cleared organs (3DISCO). It is possible now to clear not only soft tissue but also even teeth and bones with methods such as polyethylene glycol-as-sociated solvent systems (PEGASOS) (3).
Holding the sample has been another challenge for LSM. Typical methods of sample holding include hanging the sample from the top or supporting the sample from below. Samples are typically illuminated from the top or bottom as well. Physically getting the sample into the sample holder and mounting it can be a time-consuming challenge, and can sometimes cause sample damage.
One of the biggest drivers of the rise of LSM technology has been anatomical neuroscience, where a clear view of brain structure and neuronal connections in healthy and diseased brains has given researchers a clearer understanding of brain anatomy and aided computational neurobiologists in their modeling of brain function. As resolution and speed have continued to improve, LSM has been increasingly considered for pathology. Pathology is highly regulated, because it involves a medical diagnosis from human tissue. For this reason, the methods in pathology change very slowly; in fact, the basic procedure of sample handling, in which samples are embedded in paraffin wax, sliced into thin sections, stained, and mounted on slides has not changed very much in the last century.
A barrier to using LSM in pathology is the sample preparation that has heretofore been required for mounting a sample in the LSM. Because biopsies are one-of-a-kind and associated with a particular pa-tient for which a determination of health or sickness must be made, keeping the samples intact so that they will be useful is paramount. The mounting procedures for traditional LSMs are generally too de-structive. Thus, up until now, digital mi-croscopy has had limited use in pathology. One company, 3Scan, which was acquired by Strateos in June 2019, has developed the “Knife Edge Scanning Microscope” patented by Bruce McCormick of Texas A&M University for rapid robotic slicing of samples and digitizing stained images. However, at its core, this is simply an ex-tension of current methods with the addi-tion of digital storage and archiving.
An innovation extending the LSM to pathology is the “open top” LSM by Jon
Liu’s group at the University of Washing-ton (6). In this embodiment of the LSM, the sample sits on top of a glass stage, and the laser and camera view the sample from un-derneath. The cleared sample rides on an oil layer, and the objectives are immersed in oil; thus, there is no change in refrac-tive index for the laser beam or the imaged sample plane as light moves through the system. Because the open-top LSM can ac-commodate samples of unlimited size with no need for sample mounting, it is perfect for pathology applications. Biopsies of es-sentially any size can sit atop the glass plate, and with optical clearing, a depth of up to 3−5 mm can be scanned in 3 dimensions (3D). This could revolutionize the diagno-sis of some cancers, which critically depend on an understanding of the 3D structure of the biopsied tissue. Figure 4 illustrates an image of a “slice” through a prostate needle biopsy, which is approximately 1 mm in di-ameter and 20 mm in length (only a small
Figure 1: A simple schematic representation of a confocal microscope.
Figure 2: A simple schematic representation of a light sheet microscope, in which a laser beam
is formed into a thin sheet to intersect a cleared sample. The image is extracted orthogonally to
the plane of the light sheet.
www.spec t roscopyonl ine .com32 Spectroscopy 34(10) October 2019
section is shown). The image is computer-enhanced to resemble the staining typically used by pathologists on their two-dimen-sional slices of tissue.
Another recent development in LSM has been an impressive effort to miniatur-ize the light sheet and combine the sheet with a traditional microscope equipped with optical tweezers and cell tracking. Live cell analysis optimally involves fol-lowing dynamic changes in the cell with millisecond-resolution. Alexander Rohr-bach’s group at the University of Freiberg in Germany incorporated a light sheet within a traditional microscope setup, in the 2 mm space between the objective lens (below the sample) and the tracking lens (above the sample). They form a Bes-sel (nondiffractive) beam with an axicon combined with standard optics, and use a scanning mirror and additional optics, passing the light sheet through the objec-tive, to scan the laser in the horizontal direction between the objective and the tracking lens. Notably, the final turning mirror is only 0.12 mm diameter. The light sheet passes through the sample and is reflected back through the sample on a second pass by a micro-mirror. Fluores-cence is collected through the objective lens using a dichroic mirror to separate out the excitation laser. This system can achieve an axial resolution of 0.8 μm. This work was first published in Biomedical Optics Express in 2018 (8).
Finally, Jan Huisken and colleagues at the Morgridge Institute for Research in Madison, Wisconsin, have made it a goal to make LSM portable and affordable. It was Huisken’s 2004 paper that helped to popularize LSM, and subsequent papers have discussed an open version of the LSM. Their project “Flamingo,” sup-ported by the Chan Zuckerberg Founda-tion, intends to supply modular and con-figurable LSM components and systems to researchers at an affordable price. The systems they are building are intended to be portable and fit into a moderate-sized suitcase. Figure 5 illustrates a zebrafish image from the very first Fla-mingo system. Plans currently exist for LSM systems with illumination from the top and bottom, and with single or dual illumination and detection arms.
Figure 5: An image of a zebrafish from the very first Flamingo system (9).
Figure 3: An illustration of dual-spindle formation during first cell division in a mammalian
zygote, indicating that male and female genetic material remains separated during this initial
division (4).
Figure 4: A two-dimensional section taken from a “movie” scanning through a prostate
biopsy. The coloration nearly perfectly matches the H&E stain used in pathology. Scanning
through the three-dimensional image allows an understanding of the biopsy structure (Private
communication, J. Liu). For more detailed images, see their most recent publication (7).
Continued on page 50
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www.spec t roscopyonl ine .com34 Spectroscopy 34(10) October 2019
SPECTROSCOPY SPOTLIGHT
TO
M F
ULLU
M/G
ET
TY
IM
AG
ES
DETECTING AND IDENTIFYING FOOD COLORANTS WITH SERS
In recent years, consumers are increasingly opting for natural and organic food. This has led many food producers to switch
from using artificial ingredients to using natural ones. In some cases, however, questions arise about the authenticity of
those ingredients. Lili He, an associate professor in the Department of Food Science at the University of Massachusetts,
Amherst, focuses on developing and applying advanced analytical techniques to solve critical and emerging issues in food
science. Recently, that focus has turned to using surface enhanced Raman spectroscopy (SERS) in the detection, analysis,
and identification of both natural and artificial food colorants. Dr. He recently spoke to Spectroscopy about that work.
John Chasse
You have developed a SERS-based method that is capable of detecting and identifying common food colo-rants (1). What motivated you to un-dertake this work?Currently in the United States, there are seven main artificial colorants, and about 25 natural colorants, that are ap-proved for use in food. There has been growing interest in the food industry in replacing artificial colorants with natural alternatives, mainly because consumers are increasingly demand-ing products with natural ingredients. However, the authenticity of colorants used in food products has raised in-creasing concerns. Natural and arti-ficial coloring agents can be visually identical, so any detection method would need to be able to provide infor-mation about the chemical composition of the colorants that are present. We believed that SERS could be an effec-tive method for colorant detection and identification.
Why is SERS a good technique for analyzing food colorants?Colorants with conjugated bond systems are inherently Raman active, and given that Raman spectroscopy mea-sures molecular signatures as a basis to identify the components of a mixture,
it can be used to differentiate between visually similar colorants with differ-ent molecular structures. In SERS, the detection sensitivity of Raman spec-troscopy is enhanced by a nanopar-ticle substrate. In addition, since each colorant produces a unique spectrum, it is feasible to detect multiple colorants in food. Detecting compounds in food matrices with SERS also has the advan-tage of being simple and rapid, with minimum sample preparation.
What factors did you consider in your choice of SERS substrate?In developing the method, we consid-ering the perspective of potential end-users of the method—people who work in the food industry or in food regula-tion. Therefore, we chose a simple sil-ver nanoparticle substrate that is com-mercially available, so that the method and database can be easily transferred to the end users, who should not have any difficulty obtaining the substrate.
What results have you achieved so far?We successfully demonstrated the feasi-bility of SERS to rapidly detect colorant adulteration, as well as to identify colo-rants in real commercial food products. Also, we have established a database of all the artificial colorants, and most of
the natural colorants, and even some banned colorants. We believe that it is important to include banned colorants in the database, because banned colo-rants pose great safety concerns to pub-lic health. Each colorant has a unique SERS pattern based on its molecular vibrations.
How well did the technique perform on actual food products, compared with its use on aqueous solutions of colorants?We successfully detected and identified colorants in several real food matrices, including breakfast cereals, candies, crackers, and juices which used arti-ficial or natural colorants. This tech-nique works very well on actual food products, due to its high sensitivity to colorants; there is no significant inter-ference from the background matrices.
What are your next steps in this work?We will further expand and validate the database to cover all FDA-approved food colorants and banned colorants of concern. We will also explore the use of SERS to study interactions between natural colorants and food ingredients that can affect the stability of colo-rants. In addition, we will carry out a market survey of a variety of commer-
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 35
cial food products claiming to contain natural colorants to identify possible cases of adulteration and mislabeling of colorants and to assess the prevalence of such cases.
We hope that, after successful com-pletion of that study, we will be able to establish a complete SERS spectral database for certified and exempt colo-rants regulated by the FDA, as well as for selected banned or unapproved col-orants. We also hope that we will gain knowledge about the stability of natural colorants in different food matrices by studying interactions between colo-rants and food ingredients. In addition, we hope to generate information about the current levels and nature of colo-rant adulteration and misleading label-ing in the food market. The developed method and database could be used by industry to assess the quality and au-thenticity of purchased colorants, as well as to study their degradation ki-netics in food and beverage products. Government agencies could also use the methods for rapid screening of any
illegal use of food colorants in food products, whether that screening oc-curs on site or in a laboratory. Informa-tion obtained from this project would help industry improve food quality and safety, as well as assist government agencies in making decisions about regulatory considerations and actions regarding food colorants. These actions would lead to a sustainable food colo-rant market of high quality, reliability, and safety. Therefore, the outcome of this project will benefit the long-term stability of the U.S. food system.
Reference(1) J.C. Gukowsky, T. Xie, S. Gao, Y. Qu, and L.
He, Food Control 92, 267–275 (2018).
Dr. Lili He is Associate
Professor of Food
Science at University of
Massachusetts, Amherst.
She received her PhD
degree from University
of Missouri-Columbia
in 2009 and did her
postdoctorate training in University of
Minnesota between 2009 and 2012.
Then she joined the faculty in the Food
Science Department at the University of
Massachusetts, Amherst as an Assistant
Professor in 2012. Dr. He’s major research
focus is to develop and apply the
most advanced and innovative ana-
lytical techniques to help solve critical
and emerging issues in food science.
Her group has developed various sur-
face enhanced Raman scattering based
techniques for food safety and food
chemistry applications. Dr. He has suc-
cessfully obtained external funding (over
$1.8 M as PI) from federal and industry
sources, and published 82 papers. Her
excellence has been recognized by
receipt of the 2012 Young Scientist
Award from the International Union
of Food Science and Technology, 2015
ACS-AGFD Young Scientist Award, 2016
Young Investigator Award from Eastern
Analytical Symposium, 2016 IFT Samuel
Cate Prescott Award for Research, and
was selected as one of the Talented 12
by C&EN, the ACS magazine in 2016. Dr. Lili He
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36 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
Adsorbate-Induced
Morphological Changes of
PVD-Deposited Nano-Island
Film SERS Substrates
Thin films of noble metals deposited by physical vapor deposition (PVD) are a simple
substrate commonly used in surface-enhanced spectroscopies. The morphology and
gap spacing of these plasmonic substrates are key factors in the Raman enhance-
ments observed from molecules in the optical near field. The nanometer gaps
between nanostructured features of the substrate are associated with the strongest
fields and the greatest enhancement of light-driven processes (scattering, absorption,
fluorescence) that occur near the surface. Results shown here illustrate that chemi-
cal adsorbates and solvents interacting with the substrate can induce changes in the
atomic, nanoscale, and mesoscale morphology of the plasmonic substrate, possibly
leading to in situ species that play a role in the enhancement mechanisms associated
with plasmonic nanostructures. In this work, we report on the morphology changes
induced by functionalizing nano-island films (NIF) of silver and gold (film thickness =
4–30 nm) in thiol solutions (thiophenol and 1-hexanethiol). The adsorbate‒substrate
interaction is hypothesized to cause aggregation or annealing-like surface rearrange-
ments. NIF substrates of thickness <10 nm exhibit more significant changes than
thicker films, as evidenced by SEM and UV-vis data. Insights into substrate design to
account for analyte‒substrate interactions for surface‒enhanced Raman spectroscopy
(SERS) are discussed in the context of strongly and weakly adsorbing analytes.
Daniel T. Kwasnieski and Zachary D. Schultz
The enhancement of Raman scattering from mole-cules on the surface of plasmonic nanostructures (so-called surface–enhanced Raman scattering, or
SERS) has been studied extensively for trace detection of analytes in a wide variety of applications (1–5). The localized surface plasmon resonance (LSPR) supported on nanostructured metal surfaces (the most commonly
studied are silver, gold, and copper) under irradiation at optical frequencies is a result of the strong inter-action between the conduction electrons confined in a sub-wavelength geometry and the incident electro-magnetic wave. The oscillating electromagnetic f ield polarizes the electron cloud in the metal, and induces a resonant oscillation of the conduction electrons, giv-
October 2019 Spectroscopy 34(10) 37www.spec t roscopyonl ine .com
ing rise to a new field concentrated around the nanostructure. Mole-cules within these enhanced fields exhibit increased Raman scattering. The strongest fields are found at the tip of nanoscale features with high aspect rat io or atomica l ly sharp edges due to the “ lightning rod ef-fect,” or in the nanometer gaps of neighboring nanoparticles, termed hotspots, due to field enhancements from interparticle coupling (6–9).
To capture these enhancements for trace chemical detection, con-siderable ef fort has been put into fabricating noble metal nanoparti-cle systems and studying their plas-monic propert ies. The structure, composit ion, and loca l dielectric environment of a plasmonic nano-structure inf luence the LSPR fre-quency and spatial intensity distri-bution of the excited f ield around the nanostructure. The choice of these parameters will vary depend-ing on the application, and many fabr ic at ion met hod s have been developed to explore a vast design space for pla smon ic subs t r ates (10–12). These fabrication methods, ranging in complexity and perfor-mance, include nanol ithography, physica l vapor deposit ion (PVD), electrochemica l roughening, col-loidal nanoparticle immobilization, wet chemical reduction, and others.
One of t he f i r s t- s t ud ied a nd s i mple s t met hod s of prepa r i ng a SERS substrate is t he physica l vapor deposition of si lver or gold onto a glassy substrate to produce nano-island f i lms (NIFs). Within the category of PVD, f i lms can be produced from sputtering, thermal evaporation, electron beam evapo-rat ion, and laser ablat ion, which largely exhibit similar properties. Thin f i lms of these metals do not deposit conforma l ly on smoot h, glassy surfaces, due to low surface energ y a nd poor wet t ing of t he meta l on the surface, but instead form isolated nanostructures with gaps between particles of the order of the particle diameter or smaller, thus faci l itat ing plasmonic act iv-ity (13–19). The experimenta l pa-
rameters governing the resultant morphology of as-deposited f i lms (and t y pica l va lues found in the literature) are nominal f i lm thick-ness (1−15 nm), deposit ion rate (0.0 01−0.1 nm/s), subst rate tem-perature (100−500 °K), and vacuum pressure (<10-6 torr) (20−24). The mor pholog y a nd optoelec t ronic propert ies of as–deposited f i lms and those subject to various post-deposition treatments (thermal an-nealing [25−39], solvent annealing [31–35], chemical functionalization [21,31,36–46]) have been intensely invest igated. The ident ity of the underlying substrate (glass [20–23, 25 –27, 31– 40], quar tz [42 ,45,46], ca lcium f luoride [16], si l icon and geranium [28,41], formvar [47], and carbon [48,49]) and cleaning proce-
dures also factor into the NIF mor-pholog y. For as–deposited f i lms, t he morpholog y is tunable f rom isolated el l ipsoida l pa r t ic les to randomly shaped particles coined nano-islands to semi-continuous f i lm. St i l l, due to the random na-t u re of t he deposit ion process , nanopar t icle size and shape d is-tributions are rather broad, lead-ing to some variation in the opti-cal properties across the substrate. Above a certain thickness thresh-old (~20–30 nm), the f i lm wil l be-come fully continuous and acquire a mirror-like appearance, and the plasmonic activity is signif icantly diminished as the average particle s i z e i s la rger t ha n t he i nc ident wavelength. Thick films (thickness >50 nm) can be prepared that dis-
Figure 1: Scanning electron micrographs of silver nano-island films, demonstrating morphological changes upon functionalization. Images (a) and (b) correspond to two different 6 nm samples, and (b) was functionalized with benzene thiols (BT). Images (c) and (d) correspond to a 8 nm sample that was imaged before and after functionalization with BT. The scale bar for the inset image in (d) is 1 μm. Images (e) and (f) correspond to different 14 nm samples, (f) was functionalized with HT.
38 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
play plasmonic activity given care-ful considerations of the deposition parameters, but these films will not be discussed here (50).
Although the physics giving rise to the enhanced electric f ields is now wel l understood, SERS has been plagued by circumstances and effects that created a perception of irreproducibi l ity. Some of this ir-reproducibi l ity arises from large variations in the optical properties associated with differences in the nanostructure preparation. Other variations have been attributed to chemica l ef fects , some of which date back to the initial discovery of SERS (51,52). It is also understood that changes in the nanostructures can a lter the enhancements and, in some cases, quench the electric f ield (53).
The topic of this repor t is the much-s t ud ied t her ma l ly evapo-r a t e d t h i n f i l m s of s i l ve r a nd
gold (nominal f i lm thickness <30 nm). The results of this work de-scribe signif icant changes to the f i l m mor pholog y, a nd t hus t he corresponding optica l properties, upon functionalizat ion with sel f-assembled monolayers (SAMs) of thiols . Character izat ion by SEM and ultraviolet (UV)-vis spectros-copy indicates the formation of the thiol SAM perturbs the interparti-cle van der Waals forces, leading to a structural rearrangement of the film that mimics coalescence or ag-gregation of the nanoparticles. The observed structural rearrangement signif icant ly a lters the obser ved SERS enhancements.
Experimental
Nano-island Film FabricationSilver and gold NIF substrates were prepared by PVD. White glass mi-croscope slides (Globe) were soaked in a Nochromix acid bath (Godax
Laboratories) for >12 h. The slides were removed from the cleaning bath, r insed thoroughly with u l-trapure water (Thermo Fisher Sci-entific GenPure, 18.2 MΩ cm), and subsequently dried under nitrogen. T he c lea ned s l ides were loaded into the commercial PVD vacuum chamber (PVD Nano 36, Kur t J. Lesker Co.), t he meta l shot (si l-ver, 99.999%, Sigma-Aldrich, and gold, 99.999%, Kurt J. Lesker Co.) to be resistively heated was placed in a tungsten boat, and the cham-ber was evacuated to a pressure of <10-6 torr. A quartz crystal micro-balance (QCM) located near to the deposition surface monitored the deposit ion rate of the metal. Dur-ing the deposition, the substrates were rotated at 10 rev/s. The pre-deposit ion procedure for heating the metal shot to the point of evap-oration involved ramping the out-put power set point in two stages to ~80% and ~90% of the working deposit ion set point (ramp rate = 0.5 units/s, soak time = 45 s), after which a proportional-integral-de-rivatice (PID) rate control function was enabled to stabilize the rate at the working set point (rate = 0.003 nm/s). Then, the substrate shutter was opened to begin the deposition of metal onto the substrates, which proceeded unti l the nominal f i lm thickness set point was reached as determined by the QCM (thickness = 4–30 nm). The temperature of the substrate was not monitored during the deposition.
Substrate Functionalization Self-assembled monolayers of thiol (benzenethiol, benzene thiol [BT], Sigma-Aldrich, 99.9%; 1-hexane-t h iol , hyd rox y t r y ptopha n [HT], Sig ma-A ld r ich, 95%; chem ic a l s used as received) were deposited onto the as-prepared nano-island f ilms by two methods: (1) soaking in a 10 mM ethanolic solution of the respective thiol for >12 h, after which the f i lms were rinsed with ethanol for 10 s and subsequently dried under nitrogen. To study the impact of thiol concentration, some
Figure 2: Color changes in the NIF are observed when exposed to different chemicals. (a) Photograph of colored silver 8 nm films diced from the same microscope slide at different stages of treatment (wavelength of extinction maximum indicated). (Lef t) The as-prepared film appears yellow colored to the eye. (Center) Af ter soaking for 12 h in ethanol and drying under nitrogen, the film exhibits a slight discoloration. The appearance nearly returns to the as-prepared state over the course of a day. (Right) After soaking for 12 h in BT, the film becomes blue-colored, and the change is permanent. All samples exhibit small shifts in color over the course of a week when exposed to ambient atmosphere. (b) UV-vis spectra of the silver 8 nm films from (a).
October 2019 Spectroscopy 34(10) 39www.spec t roscopyonl ine .com
f ilms were soaked in lower concen-tration BT solutions: 5 nM, 50 nM, 1 μM, and 50 μM; (2) posit ioning the substrate above a 10 μL drop-let of the respect ive thiol inside a closed container to expose the surface to thiol vapor for 1 h; after removal no further treatment was performed.
UV-visible SpectroscopyUV-vis transmission measurements of t he t hin f i lm substrates were carried out by placing the substrate into the optical path of a VWR UV-1600PC spectrophotometer just in front of the sample cuvette holder that could not be modif ied to f it the microscope slides or cover slips. A blank slide or cover slip was used to obtain baseline reference spec-trum. The extinction spectra were recorded using scan rate of 1 nm/s. The data were ana lyzed in MAT-LAB to determine LSPR wavelength and extinction maxima.
Raman SpectroscopyR a ma n mea su rements were col-le c te d u s i ng a Ren i shaw i nVi a Raman microscope. The Renishaw mapping software was used to raster over approximately 20 μm x 20 μm areas, collecting a spectrum every 3 μ m; t he mapped a rea s va r ied from sample to sample but at least 50 spectra were recorded per map. The acquisit ion time was 1 s, and the microscope objective used had a 50x magnification and a numeri-ca l aperture (NA) of 0.75 (Olym-pus). T he exc itat ion power a nd laser was ~1 mW from a 632.8 nm HeNe laser (Thorlabs). The spectral windows collected for BT and HT were 700−1275 and 800−1350 cm-1 Raman shift, respectively. The data was analyzed using MATLAB and an open-source peak-f it t ing rou-t ine (54) to ca lcu late peak areas based on Gaussian fits. The number of Gaussian line shapes included in the f it was 3 for BT and 1 for HT for the selected spectral windows 9 0 0 −12 0 0 , a nd 10 8 0 −114 0 c m -1 Raman shi f t , respect ively. A l in-ear baseline was subtracted from
the spectrum before f itting to the Gaussian line shape.
Scanning Electron MicroscopyThe nanoscale morphology of thin f i lm substrates was characterized
by imaging in the f ield emission mode of a FEI Apreo LoVac SEM. Imaging condit ions were 1−2 kV, 6.3 pA, working distance 3−4 mm, stage bias -4000 V. The in-lens T1 component of t he Tr i n it y su ite
Figure 3: UV-vis characterization of NIF. (a) UV-vis extinction spectra acquired in transmission mode of silver (Ag) films of thickness = 2, 6, 8, 10, 12, 14, and 20 nm, indicating general trends of increased extinction, peak broadening, and a red shift of the LSPR wavelength for thicker films. (b) Plot of LSPR peak wavelength, and (c) extinction for silver (Ag) (black circles) and gold (Au) (red squares) films of various nominal thickness.
40 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
backscatter detectors of the SEM was used to acquire the signal.
Results
Figure 1 shows scanning electron m icrog raphs of t he s i lver N IFs obta i ned f rom d i f ferent deposi-t ion thicknesses before and af ter functionalizat ion with thiol. Iso-lated nanoparticles were observed in the 6 and 8 nm f i lms (nominal f i lm t hick ness measured during the deposition by QCM) that were roughly el l ipsoidal, while a more heterogeneous mixture of shapes was found for the 14 nm film. After f unct iona l izat ion w it h t hiols by soaking in ethanolic solutions, the observed particles appear closer to-gether, suggesting smaller gap sizes between the islands formed.
This change in spacing is appar-ent upon visual inspection. Figure 2 shows the color of the f i lms are a lso observed to change in dif fer-ent chemical environments. In Fig-ure 2, the pristine NIF is observed to have a yel low hue. The ext inc-tion max of the pristine f ilm is ob-served at 470 nm. When immersing
the f ilm in ethanol, a slight discol-oration is observed and a shif t in the extinction maxima to 490 nm is measured. When the f i lms are allowed to dry over the course of a day, the change observed from ex-posure to ethanol is observed to be reversible, with the LSPR returning to near the same value as observed from the prist ine f i lm. However, upon functionalization with BT, a dramatic color change is observed visually that corresponds to change in the ext inct ion ma xima to 665 nm. The measured extinction spec-trum is consistent with coupled or aggregated nanoparticles reported in the literature (55–57).
The data in Figures 1 and 2 sug-gest a signi f icant change in t he NIFs upon adsorpt ion of BT. To understand this ef fect , we inves-t igated how di f ferent deposit ion condit ions and functionalization concentrations affect the change in optical properties. Figure 3 shows that the single peak obser ved in t he ex t i nc t ion spec t r u m broad-ens and red-shifts as the thickness of the metal deposition increases.
Wavelength (Figure 3b) and peak absorbance va lue (Figure 3c) are shown to increase with f i lm thick-ness. Similar trends were observed for both silver and gold f ilms. The observed trends agree with previ-ous reports of changes in the LSPR of NIFs with changes in deposition thickness (15,17,22,23).
The broadening of the extinction spectrum with increasing nominal f i lm t h ick ness show n in Fig ure 3a is commonly observed as f i lms transit ion from nanomateria ls to mesoscale materia ls (15,17,19,26). At a deposit ion t h ick ness of 20 nm, the width of the feature in the extinction spectrum is consistent w it h a heterogeneous col lec t ion of par t icles with var y ing shapes and sizes. Analysis of ext inct ion spec t ra obta ined f rom mu lt iple f i lms indicates that above a depo-sit ion of 15 nm, the nano-islands percolate into larger st ructures ; this increased interconnectedness has been observed in this regime of f i lm thick ness (15,17,26). The t rends in t he ex t inct ion spectra agree with the observations from the electron micrographs (Figure 1). Increased f i lm thickness gives r ise to a larger size nanopart icle consistent with increased and red-shifted extinction.
Figure 4 i l lustrates the changes in ext inct ion spectrum observed f rom di f ferent deposit ion t h ick-ne s s e s a f t e r f u nc t ion a l i z a t ion with either BT or HT. In all cases, a ma rked cha nge i n t he ex t i nc-t ion spectrum is observed as the thiols adsorb onto the NIFs. It is wel l understood that the change in the dielectric environment will shift the frequency of the LSPR (the basis of surface plasmon resonance sensing [10,42,44]). The change in l ine shape observed from smaller thickness f ilms (Figures 4a, b, and d, thickness < 10 nm) show split-ting more consistent with plasmon hybridization from the aggregation of discrete particles. At thicker de-posit ions (Figure 4c, 14 nm), the change in the extinction spectrum is best described by a shif t in fre-
Figure 4: Characterization of NIF LSPR by UV-vis before and after functionalization. Spectra of silver (Ag) and gold (Au) films of thickness (a) Ag 6 nm, (b) Ag 8 nm, (c) Ag 14 nm, and (d) Au 4 nm. Solid curves correspond to measurements of as-prepared films, dash-dotted curves correspond to the same film after soaking in a 10 mM ethanolic thiol solution overnight (black = BT, red = HT).
October 2019 Spectroscopy 34(10) 41www.spec t roscopyonl ine .com
quency but not line shape. The changes in line shape for the different thickness f ilm is observed from both solution and gas phase adsorption (data not shown) of the thiols onto the NIFs. This indicates that the deposition thickness inf luences the morphological and optical changes resulting from molecular adsorption, but that adsorption is a key event.
Given the known relationship between nanostruc-ture and the plasmonic f ields responsible for surface-enhancement ef fects, we sought to understand the impact of this adsorption-driven change in film struc-ture on the observed SERS signals.
Figure 5 plots the results obtained from the Raman mapping experiments of BT and HT-functionalized f i lms. SERS spectra of the two molecules are shown in Figure 5b, demonstrating that BT has a much larger Raman cross-section compared to HT. A representa-tive map analysis of a silver 8 nm NIF functionalized with BT is displayed in Figure 5a and 5c. In general, the SERS response from similarly prepared f i lms is quite reproducible. In the map shown, the step size of approximately 3 μm, which is larger than the laser spot size (dif fraction l imited at approximately 500 nm), ensures that each spectrum is obtained without possible photodamage from the previous acquisition. With a 1 s integration, a large signal of approximately 30,000 counts is observed. The spectra show excellent signal-to-noise ratios. When examining the variation in intensity across the silver NIF substrate, the peak area of the 1000 cm-1 band in BT changes by about 15% from the brightest to dimmest spectra.
Comparing across different films, the average SERS signal obtained from the NIF substrates shows litt le dependence on the deposition thickness below 15 nm (Figure 5d). Once the thickness of the deposited metal exceeds 15 nm, the SERS intensity is observed to de-crease for both BT and HT. As discussed above, NIFs obtained from thicker depositions exhibit larger in-terconnected particles. These interconnected features decrease the density of hot spots (nanometer gaps be-tween particles) and therefore these f ilms exhibit less SERS. Beyond the change in observed SERS intensity with thicker deposits (thickness > 15 nm), the trends associated with the as-deposited f i lm properties are less discernible. The correlation between the topogra-phy and SERS intensity suggests the reorganization is observed for smaller features and that once the as-de-posited features coalesce into larger islands, the ability to reorganize and boost the SERS signal is diminished.
As noted above, the identity of the adsorbate inf lu-enced the reorganization of the nano-islands in the film, where ethanol did not produce the same effect as BT or HT. In Figure 6, we examined how the concen-tration of BT in solution alters the extinction (indica-tive of the island reorganization) and the observed SERS signal. In Figure 6a, the extinction is observed to remain largely unchanged at adsorbate concentrations
below 50 μM (spectra for f ilms soaked in 5 nM and 1 μM not shown). When a f ilm was initially soaked at a 50 nM concentration of BT, the extinction was largely unchanged. Upon resoaking the same film in 10 mM of BT, the extinction was observed to change consistent with the reorganization of the NIFs described above. The observed change with re-soaking was the same as soaking a second pristine f ilm in a 50 μM solution. This indicates that a critical coverage of BT (or other adsorbate) is necessary to induce the reorganization of the metal islands.
In Figure 6b, the SERS spectrum obser ved from the NIFs is shown to change as function of adsorbate concentration. Solutions of BT below 1 μM did not evince a significant SERS signal, suggesting this con-centration is below the limit of detection. Peaks are sporadically observed at frequencies consistent with BT, but do not increase appreciably in intensity until higher concentrations. At a 50 μM concentration of BT, a markedly dif ferent spectrum is observed. The peaks are observed with signif icant ly greater inten-sity. The change in intensity at 50 μM is dramatically larger than the signals observed from 50 nM and 1 μM adsorbate concentrations. Additionally, the change in intensity correlates with a dramatic change in the extinction spectrum (Figure 6a) suggesting that this change in the spectrum is related to the reorganiza-tion of the NIFs.
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42 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
Discussion
The characterization of silver and gold NIFs was accomplished with SEM, UV-vis, and SERS measure-ments. Our analysis of as-deposited f i lms from our work agrees wel l with the literature with respect to the investigated deposition param-eters (17,21–23,26,36). The plas-monic properties of these thin films are apparent upon v isua l inspec-tion, since the films are colored and semi-transparent, as opposed to bulk metal. Similar to the Lycurgus cup, the films show differences in ref lec-tion and transmission (15,20). What has not been directly correlated in the literature is the increased SERS response that arises from the struc-tura l reorganizat ion of the plas-monic metal islands and the analyte dependence of this effect.
Previous studies of the morpho-logica l and opt ica l proper t ies of PVD thin f i lms suggest the NIFs c a n be cha r ac ter i z ed a s oblate spheroids with an aspect ratio (R) bet ween 0.1 a nd 0.5, ref lec t i ng t he is land diameters are ~2−10x their height (17). Thus, with suff i-cient interparticle spacing (greater than a few nanometers, as is the case for thicknesses <15 nm), the LSPR of NIFs is obser ved to be a single peak ref lecting the in-plane mode a long the major axis of the isolated ellipsoids. Changes in the LSPR have been described when the f ilms are annealed, which causes a reorganization of the islands into larger, more spheroida l part icles (17, 25 –30), a nd when molecu lar adsorption changes the dielectric environment of the film. A number of studies have noted the change in the local dielectric environment from adsorbates such as solvents (31–35), dye molec u les (36 –39), si lanes (58), thiols (33,42–44,46), and others (21,31,45); however, a structural rearrangement has not been widely implicated (21,45–46). Indeed, to facilitate LSPR sensing, the substrates for the deposition of NIFs were pre-functionalized with mercaptopropyltr imethoxysi lane (MPTS) to improve f i lm stabiliza-
Figure 6: Characterization of BT-functionalized silver NIF at low soaking concentrations. (a) UV-vis transmission spectra series of two silver 8 nm samples after different BT treatment steps. (b) SERS spectra for four silver samples soaked in various concentrations of BT. SERS spectra were recorded with 633 nm laser excitation for 10 s at laser power 3, 0.6, 0.6, and 0.05 mW for the respective films. The inset magnifies the spectral region 950‒1150 cm-1 where the prominent BT modes are present in the 5 nM, 50 nM, and 50 μM samples, but not the 1 μM sample.
Figure 5: SERS mapping of silver 8 nm NIF functionalized with BT. (a) Optical image of the mapped area at 50x magnification. (b) Representative SERS spectrum of BT (black) and HT (red). The bands marked with an asterisk are used for calculating the peak area from Gaussian fits in (d). (c) Colormap of the peak area of the 1000 cm-1 band of BT. The map was obtained by rastering in 3 μm steps, acquiring a spectrum at each pixel for 1 s (laser power = 0.7 mW). (d) Map-averaged peak area of the 1000 cm-1 band of BT (black) and the 1110 cm-1 band of HT (red) vs. nominal film thickness. Error bars indicate the standard deviation for a mapped area of 50-100 pixels.
October 2019 Spectroscopy 34(10) 43www.spec t roscopyonl ine .com
t ion (44). In that case, when the NIFs deposited on MPTS-treated glass were funct iona l ized with a di-thiol, only a smal l red-shif t of the LSPR (< 50 nm) was observed, typical of a change in the local dielectric environment with increasing coverage (thickness) of the adsorbed layer. This change indicates stabilized f ilms provide morphological stability which may im-pact utility as SERS sensors. On the other hand, struc-tural rearrangements of NIF on unmodif ied quartz substrates were described upon functionalization with two different molecules by measurements of the LSPR and atomic force microscopy (45,46).
The change in morphology we claim suggests that intermolecular forces, such as increased van der Waals forces, between the nano-islands can drive aggregation. The change in the intermolecular forces derives from the coverage of the adsorbate and produces a marked change in the observed SERS signal. While not a classi-cal “chemical effect,” here the electromagnetic enhance-ment clearly derives from chemical functionalization, in this case changing the spacing and the local electric fields experienced by the adsorbate. Indeed, there are now SERS substrates commercially available that take advantage of this effect, using molecular adsorption to pull nano-pillars together (Silmeco, reference [59]). Our results illustrate how these aggregation affects alter the observed signals and likely affect selectivity for differ-ent analytes. Ethanol did not drive rearrangement in the same manner as the alkane thiols. There is clearly a balance between coverage and chemical properties of the adsorbate that drive the rearrangement. The changes we report in the UV-vis spectrum provide an easy experiment to assess if these effects are involved in experiments with other adsorbates and applications. Understanding these effects may enable selective sen-sors for future applications.
Conclusion
We have shown that molecular adsorption can drive structura l rearrangements in si lver and gold NIFs commonly used as SERS substrates. Adsorption of a significant portion of a monolayer changes the struc-ture of small NIFs, typically derived from deposition thicknesses less than 15 nm, that are evident in SEM, UV-vis, and SERS measurements. At low analyte cov-erages, the SERS spectrum is significantly weaker and does not change with concentration, suggesting sto-chastic effects. Above a critical coverage, changes are observed that correlated with SEM and UV-vis mea-surements to suggest the nano-islands are closer and coupling more strongly. These results have implica-tions for understanding signal variation in SERS and in designing future SERS assays.
Acknowledgments
This work was supported by National Science Foun-dation awards CHE-1830994 and DBI-1830153 and
funding from the Ohio State University. Electron mi-croscopy was performed at the Center for Electron Microscopy and Analysis (CEMAS) at The Ohio State University.
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Ishan Barman is an associate professor at Johns Hopkins University in Baltimore, Maryland. He is the 2019 winner of the
Emerging Leader in Molecular Spectros-copy Award, which is presented by Spec-troscopy magazine. This annual award rec-ognizes the achievements and aspirations of a talented young molecular spectroscopist who has made strides early in his or her ca-reer toward the advancement of molecular spectroscopy techniques or applications. The winner must be within 10 years of receiving his or her highest academic degree in the year the award is presented. The award will be presented to Barman at the SciX Conference, which will be held October 13−18, 2019, in Palm Springs, California, where he will give a plenary lecture and be honored in an award symposium.
Barman received his PhD in 2011 from the Massachu-setts Institute of Technology, in Cambridge, Massachusetts. Following post-doctoral research at the Laser Biomedical Research Center (LBRC) at MIT from 2011 to 2013, he then became an assistant professor at Johns Hopkins University, in mechanical engineering with a joint appointment in the Department of Oncology in 2014. In 2018, he added an-other joint appointment, in the Department of Radiology and Radiological Science. He has since been promoted to associate professor.
Barman has published more than 75 peer-reviewed pa-pers, holds 9 patents and patent applications, and has pre-sented more than 150 papers and talks at scientific confer-ences. His work has been extensively featured in leading scientific media (Technology Review, Physics Today, Phys-ics World, C&E News) and in popular media outlets (Wall Street Journal, CNN Newsroom with Ali Velshi). He has re-ceived several awards and scholarships, including the Na-tional Institute of Health (NIH) Director’s New Innovator
Award; the Outstanding Young Engineer award from the Maryland Academy of Sciences; an Emerging Investigator award from the Royal Society of Chemistry; the Dr. Horace Furumoto Innovations Young Investigator Award from the American Society for Laser Medicine and Surgery; and the Tomas A. Hirschfeld Award from the Federation of Ana-lytical Chemistry and Spectroscopy Societies (FACSS). In total, he has received more than 30 grants, fellowships, and awards. With an impressive h-index of 29, Barman’s re-search encompasses medical and pharmaceutical applica-tions by exploring new interdisciplinary research questions at the interface of biomedicine and engineering.
When he was a student at MIT, Barman’s efforts fo-cused on addressing the many challenges that impede optical spectroscopy-based noninvasive blood glucose detection. As a faculty member at Johns Hopkins, his re-search program has expanded to include the development of spectroscopic imaging for tissue analysis, novel sur-face-enhanced Raman scattering (SERS) probes for liquid biopsy and high-precision in vivo cancer imaging, and new single-cell analysis platforms with combined capture, manipulation and sensing capabilities. In recent years, his group has published peer-reviewed papers in Cancer Re-search, Nature Materials, Nano Letters, Angewandte Che-mie Intl. Ed., Chemical Science, Small, Nanoscale, ACS Sen-sors, ACS Photonics, Analytical Chemistry, and Accounts of Chemical Research.
Professor Rohit Bhargava, the director of the Cancer Cen-ter at Illinois and the Founder endowed professor in engi-neering at the University of Illinois at Urbana-Champaign, has known Barman for more than a decade, since Barman was a graduate student at MIT. “Ishan is simply brilliant,” said Bhargava. “He has such a great understanding of spec-troscopy and science, and is always so wonderful to talk to and discuss new ideas.”
Jerome Workman, Jr.
This year’s molecular spectroscopy award recipient, Ishan Barman, is an energetic and accomplished
young researcher in biomedical Raman spectroscopy. With a combination of spectroscopy, imaging,
and chemometrics, his work unites structural and molecular information to provide greater detail to
our understanding of pathological changes in cells and tissues.
The 2019 Emerging Leader in
Molecular Spectroscopy Award
Ishan Barman
46 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
Manoharan Ramasamy, the Global Lead for the Process Analytics Cen-ter of Excellence for Small Molecules at Merck, has also been impressed by Barman’s work. “In a very short span of a few years and at this very young age, Ishan has significantly advanced the field of science in many areas,” he said. “His work has been published in highly cited, major refereed journals, and he has been awarded with many patents and recognized with a number of awards.” Ramasamy also described Barman’s developments as “ground-breaking.”
Even with a crowded research schedule, Barman continues volunteer work at a prolific pace. He is a volun-
teer in scientific societies and groups, a conference session organizer (FACSS/SciX), and an active technical reviewer for more than 50 scholarly journals.
Designing Optical
Pathophysiologic Sensing Tools
Barman’s work in Raman spectroscopy focuses on translating new photonic tools from conceptualization through each aspect of design and fabrication, typically for in vivo measurements. Once measurements are obtained, he uses machine learning to determine differential molecular markers that enable objective disease detection and outcome prediction. Yukihiro Ozaki, professor emeritus at Kwansei Gakuin
University in Japan, commented that in the rapidly expanding field of mo-lecular spectroscopy, Barman stands out in terms of ingenuity and schol-arly depth. “I am deeply impressed by Dr. Barman’s creativity, his broad skill set, and his willingness to explore new interdisciplinary research questions at the boundaries of biomedicine and en-gineering,” Ozaki wrote in a letter sup-porting Barman’s nomination for the award. “His work is innovative and inventive, and has transcended tradi-tional disciplinary boundaries.”
As an MIT doctoral student, Bar-man was guided by the late Professor Michael Feld and by Dr. Ramachandra R. Dasari, the Associate Director of the Laser Biomedical Research Cen-ter (LBRC) and the George R. Harri-son Spectroscopy Laboratory at MIT. There, he developed techniques for sensitivity and specificity improve-ments in clinical Raman measure-ments by designing a turbidity correc-tion framework to mitigate sampling differences between patients. He also developed a spectroscopic concentra-tion correction model to compensate for the difference in concentration of metabolites between blood and inter-stitial f luid compartments. Noninva-sive glucose concentration measure-ments are plagued by the physiological time lag between interstitial fluid (ISF) glucose and blood glucose. This lag is problematic for spectroscopic tech-niques, which measure the glucose in the ISF, not the blood. Because blood glucose is used for calibrating spec-troscopic instruments and is the im-portant glucose level for clinical deci-sion-making, there are major problems associated with the lag. To resolve this problem, Barman and colleagues de-veloped a dynamic concentration cor-rection (DCC) scheme; using the mass transfer of glucose between ISF and blood, they were able to ensure greater consistency between blood measure-ments and spectral measurements (1,2). This correction allows one to compute the expected ISF glucose level from the blood glucose levels, to provide more accurate spectroscopic calibration.
Barman also developed innova-tive data processing improvements
Figure 1: At Johns Hopkins University. Left to right: Soumik Siddhanta, a post-doctoral researcher;
Santosh Paidi, a doctoral student; Professor Yukihiro Ozaki, and Barman (photo courtesy of Y. Ozaki).
Figure 2: Barman making optical bench adjustments for Raman spectroscopic measurements (photo
courtesy of I. Barman).
October 2019 Spectroscopy 34(10) 47www.spec t roscopyonl ine .com
for Raman, such as support vector machines (SVM), to account for non-linear effects in the spectra−concen-tration relationship. Dasari was very impressed with the work Barman did with him at MIT, and the work he has done since. “His research output has been staggering with numerous qual-ity publications,” Dasari said. “His work is highly valued in the field and is well cited.”
Decoding the Molecular
Pathology of Cancers
Barman has maintained a principal focus on Raman spectroscopy-based tissue analysis that will permit dis-ease detection prior to morphologic manifestation, focus target searches in pharmacological research, and en-able patient stratifications for more effective therapy. His recent work has led to increased understanding of can-cer etiology relating to metastasis. His work to integrate spectroscopic mea-surement with biopsy needles provides a new tool with the potential for real time cancer detection (3). This optical sensor is designed to be used to simul-taneously identify microcalcification status and diagnose any underlying breast lesions in real time.
Barman has also designed and pro-duced customized SERS probes exhib-iting near single-molecule sensitivity for use in liquid biopsy platforms for diagnosis as well as for therapy re-sponse monitoring. His laboratory is further engineering targeted nano-probes for high-precision in vivo im-aging of cancers to provide real-time assessment of localized tumor burden and micrometastatic satellite lesions. In what has been referred to as a mile-stone paper written in 2017, Barman and colleagues introduced Raman spectroscopy and chemometric tech-niques for detection of changes in or-gans of future metastasis that are ac-tively modified by the primary tumor before metastatic spread has even oc-curred. The study reported the use of these techniques in identifying premetastatic molecular adaptations in lungs—prior to observation of corre-sponding morphological changes—in response to primary breast tumors in
mouse models (4). This approach shows the potential for revealing new drugable targets to block metastasis, and may be used to guide treatment regimens that achieve maximum cancer cell kill and arrest premetastatic adaptations.
In related work that year, label-free Raman spectroscopy was used to iden-tify spectral bands for defining or-gan-specific cancer cell metastases. Decision algorithms applied to the Raman spectra were able to discrim-
Figure 3: Barman homing-in on cancer cell detection using his custom Raman microscope (photo
courtesy of I. Barman).
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48 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
inate the unique biological cell types despite their isogenic profile. The findings of this work provided strong evidence that metastatic spread gener-ates tissue-specific adaptations at the molecular level within cancer cells (5).
“His contributions to biomedical optics and spectroscopy have been fundamental and substantial, and he has led the technological development and clinical translation of several non-invasive or minimally invasive opti-cal tools,” commented Dasari of MIT, who added that Barman exhibits a rare combination of creativity, diligence, and maturity. “These characteristics place Barman as a top individual in his peer group in biophotonics and molec-ular spectroscopy,” he said.
Raman Spectroscopic
Determination of Long-Term
Glycemic Markers for
Diabetes Monitoring
Another area of focus for Barman has been diabetes research. In 2012, Barman was able to demonstrate glycated albu-min detection and quantification using Raman spectroscopy without the addi-tion of reagents (6). Glycated albumin is an important marker for monitoring the short-term glycemic history of diabetics.
Barman and coworkers also demon-strated the potential of Raman spec-troscopy for quantitative detection of hemoglobin A1c (HbA1c), without using any external molecular probes or dyes, a test that reveals the average level of blood sugar in a patient over the preceding few months (7). The technique harnessed for this test is called drop coating deposition Raman (DCDR) spectroscopy. The causal-ity behind making this measurement is a series of subtle but highly repro-ducible changes in the Raman blood spectra related to nonenzymatic glyco-sylation (glycation) of the hemoglobin molecule. These measurable spectral changes provide accurate determina-tion of glycated and nonglycated he-moglobin when Raman spectra are an-alyzed using chemometric methods.
Fusion of Raman Spectroscopy
and Optical Microscopy:
A Multimodality Paradigm
for Cell and Tissue Analysis
Chemical imaging of cancer cells is another challenge that Barman has taken on. For this work, Barman and coworkers have developed a multi-modal microscopy system (MMS), in-corporating confocal Raman, confocal
reflectance, and quantitative phase mi-croscopy (QPM) into a single imaging instrument (8). Confocal ref lectance and quantitative phase microscopy show detailed spatial morphology as images, and confocal Raman micros-copy provides the molecular details. By combining these different micro-scopic imaging modes, it is possible to acquire detailed morphological and chemical information without using external stains or molecular probes.
This work was extended in the development of a portable, optical fiber probe−based spectroscopic tis-sue scanner for quantitative diagnos-tic imaging of tissue during surgical procedures (9). The scanner proto-type was tested in human specimen for intraoperative breast cancer mar-gin assessment. Spectroscopic tech-niques used for the tissue scanner included both diffuse ref lectance spectroscopy (DRS) and intrinsic f lu-orescence spectroscopy (IFS). The in-strument was designed for hyperspec-tral imaging capabilities such that the DRS and IFS spectra are collected for each scanned image pixel.
Other cancer detection research of Barman’s group has involved the de-velopment of a plasmon-enhanced Raman spectroscopic assay featuring nanostructured biomolecular probes and spectroscopic imaging (10). This system is tailored for multiplexed de-tection of breast cancer biomarkers, such as cancer antigen (CA) 15-3, CA 27-29, and cancer embryonic antigen (CEA). The tags used for the SERS measurements are functionalized with monoclonal antibodies to enable de-tection of CA15-3, CA27-29, and CEA. These biomarkers are detected using antibody–antigen interactions, result-ing in a functional sandwich spec-tro-immunoassay device.
Michael Walsh, an assistant pro-fessor at the University of Illinois at Chicago, is among those impressed by Barman’s work in cancer diagnos-tics. “The paper that Ishan published this year in Cancer Research (11) is a landmark paper that brings a signifi-cant amount of attention to the spec-troscopy field from the wider can-cer community,” he said. “This paper
Figure 4: Left to Right: Igor Lednev of the University at Albany SUNY, Valery Tuchin of Saratov State
University, in the Russian Federation, Ishan Barman, Hemanth Noothalapati, of Shimane University
in Japan., and Ms. Miyuki Yamamoto. Photo taken at the Japan-Taiwan Medical Spectroscopy
International Symposium and 14th Annual Meeting of the Japan Association of Medical Spectroscopy,
on Awaji Island, Japan, December 4–7, 2016. The photo was taken following a traditional Japanese tea
ceremony conducted by Ms. Yamamoto (photo courtesy of I. Lednev).
October 2019 Spectroscopy 34(10) 49www.spec t roscopyonl ine .com
demonstrates the important role that spectroscopy will have in the future of precision medicine.”
Translational Endeavors
and Broader Impact
Barman has also worked to develop simple and cost-effective substrates for SERS detection that are synthe-sized from silver ink on paper (12). Paper-f luidics assays have emerged as promising biodetection platforms owing to their salient advantages in terms of ease of manufacture, stor-age, transport and biodegradability. The reported ink‐based coating tech-nique ensures uniform absorption for strong and reproducible plasmonic en-hancement for SERS measurements. The simple fabrication process com-bined with the control over analyte f low patterns offers significant boost for development of paper-based SERS analytical devices, especially for use in resource‐limited settings.
Success in SERS applications de-pends on the specific nanomaterial substrates (or tags) used to induce the enhancement effect. Barman and his team leveraged silver-core, gold-shell nanoparticles to differentiate between closely related human and murine antibody drugs in solution with very high specificity and accu-racy (13). Notably, the biologic sample set included antibodies belonging to the same isotypes that have the same amino acid sequence in their constant region, which constitutes the major part of the structure.
Quantitative determination of ag-gregation and particle formation for antibody–drug conjugate (ADC) thera-peutics with label-free Raman spectros-copy also has been recently addressed by Barman (14). Characterization of protein aggregation is of particular significance, as aggregates may lose the intrinsic pharmaceutical proper-ties as well as engage with the immune system instigating undesirable down-stream immunogenicity. In this work, label-free Raman spectroscopy is used in combination with a support vec-tor machine (SVM)-based regression model to provide fast and accurate analysis (via chemometric prediction)
for a wide range of protein aggregates. The proposed Raman spectrosco-py-based method shows strong poten-tial for stability testing and lot release as part of a quality control and manufac-turing strategy of these ADCs to ensure safe and efficacious therapeutic mate-rial for human patients. It is essential that the chemical subtleties be known so that the end products are precisely consistent, both chemically and phys-ically; this level of quality control en-ables production of biopharmaceutical products exhibiting consistent clinical and toxicological activity.
Specialized Data Analysis
and Chemometrics
An underlying enabling development in Barman’s work has been the applica-tion of a variety of chemometric tech-niques; these techniques have allowed Barman to advance the field of spec-troscopic measurements. His group has also used chemometric methods to analyze laser-induced breakdown spectroscopy (LIBS) data for material identification including development of a genetic algorithm-derived classi-fier that demonstrates statistically sig-nificant improvement in classification accuracy over more traditional ap-proaches with an order of magnitude fewer discrimination feature (factor) requirements (15).
The Characteristics and
Fruit of Enthusiasm
Taking on the challenges of biomed-ical applications of Raman spectros-copy and achieving such valuable ad-vances doesn’t just require intellect; other personal qualities play an es-sential role. One such quality is en-thusiasm. “In my numerous interac-tions with [Barman], what stands out is his clarity of thought and energy in taking on the challenging problems,” said Ozaki. “He brings a special level of enthusiasm to everything he does.” Ozaki recalled that when he and Bar-man first met, Barman had just read one of Ozaki’s articles on identifying primary algebraic structures that de-scribe the chemical reaction system in terms of spectroscopic observables. “Within ten minutes, he came up with
so many new ideas, and he was pep-pering me with many questions,” said Ozaki. “It was amazing and exciting. We wrote an article a couple of years later that was based on this conver-sation. I always feel energized after speaking with him!”
Another valuable quality for such work is having a mindset for interdis-ciplinary study. “What I have come to appreciate most is Ishan’s ability to seamlessly work across multiple dis-ciplines and make fundamental con-tributions at each step,” said Igor K. Lednev, a professor at the University at Albany, SUNY. As examples, Led-nev described other less noted, yet extremely important, contributions that Barman and his research group have made, such as the development of real-time chemical imaging meth-ods for the diagnosis of middle ear pathology, and the development by Barman and collaborators of an in-expensive multiwavelength f luores-cence imaging otoscope using readily available components. “His excep-tional productivity, coupled with the quality of his work in next generation diagnostic oncology, has been widely recognized,” Lednev concluded.
Another valuable quality is the ability to work with others—both col-leagues and students. “Ishan has ex-cellent interpersonal skills,” noted Dasari. “Ishan’s infectious personality and people skills bring together groups of any size and help them work effec-tively together. He also displays great patience and understanding in mento-ring junior researchers.”
Future Prospects
Given his success to date, Barman’s prospects for the future are especially bright. Barman focuses his work at the interface of medicine and vibra-tional spectroscopy, and he is ready to make substantial contributions that directly address patient quality of care in cancer and other diseases. “I am confident Barman will provide some truly practical and robust solu-tions that are actually helpful to can-cer patients as well as making im-portant advancements in the field of spectroscopy,” said Bhargava.
50 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
Ozaki agreed. “Given his record of diverse and impactful contribu-tions, he is well poised to be a leader in biomedical optics and spectros-copy for years to come,” he said, “I also expect that Barman will be instrumental in nurturing the next generation of biomedical engineers and scholars.”
Dasari echoed those comments. “I simply do not know any limits to his ability to enter a f ield, master its frontier, and roll those frontiers back to open new applied domains,” he said.
References
(1) I. Barman, C.R. Kong, G.P. Singh, R.R.
Dasari, and M.S. Feld, Anal. Chem.
82(14) 6104–6114 (2010).
(2) N.C. Dingari, I. Barman, G.P. Singh,
J.W. Kang, R.R. Dasari, and M.S. Feld,
Anal. Bioanal. Chem. 400(9), 2871–
2880 (2011).
(3) I. Barman, N.C. Dingari, A. Saha, S.
McGee, L.H. Galindo, W. Liu, D. Plecha,
N. Klein, R.R. Dasari, and M. Fitzmaurice,
Cancer Res. 73(11), 3206–3215 (2013).
(4) S.K. Paidi, A . Rizwan, C. Zheng, M.
Cheng, K . Glunde, and I . Barman,
Cancer Res. 77(2), 247–256 (2017).
(5) P.T. Winnard Jr, C. Zhang, F. Vesuna,
J.W. Kang, J. Garry, R.R. Dasari, I. Bar-
man, and V. Raman, Oncotarget 8(12),
20266 (2017).
(6) N.C. Dingari, G.L. Horowitz, J.W. Kang,
R.R. Dasari, and I. Barman, PLoS One
7(2), e32406 (2012).
(7) I. Barman, N.C. Dingari, J.W. Kang, G.L.
Horowitz, R.R. Dasari, and M.S. Feld,
Anal. Chem. 84(5), 2474–2482 (2012).
(8) J.W. Kang, N. Lue, C.R. Kong, I. Bar-
man, N.C . Dingari , S. J . Goldfless,
J .C . Ni les , R .R . Dasar i , and M.S .
Feld, Biomed. Opt . Express 2(9),
2484–2492 (2011).
(9) N. Lue, J.W. Kang, C.C Yu, I. Barman,
N.C. Dingari, M.S. Feld, R.R Dasari,
and M. Fitzmaurice, PloS one, 7(1),
e30887 (2012).
(10) M. Li, J.W. Kang, S. Sukumar, R.R.
Dasari, and I. Barman, Chem. Sci.
6(7), 3906–3914 (2015).
(11) S.K. Paidi, P.M. Diaz, S. Dadgar, S.V.
Jenkins, C.M. Quick, R.J. Griffin, R.P
Dings, N. Rajaram, and I. Barman,
Cancer Res . 79(8), 2054–2064
(2019). doi: 10.1158/0008-5472.
(12) Z. Huang, S. Siddhanta, C. Zhang, T.
Kickler, G. Zheng, and I. Barman, J.
Raman Spectrosc. 48, 1365–1374
(2017).
(13) S.K. Paidi, S. Siddhanta, R. Strouse,
J .B . McGivney, C . Lark in , and I .
Barman, Anal. Chem. 88(8), 4361–
4368 (2016).
(14) C. Zhang, J.S. Springall, X. Wang, and
I. Barman, Anal. Chim. Acta, 1081,
138–145 (2019).
(15) A .K. Myakalwar, N. Spegazzini, C .
Zhang, S.K. Anubham, R.R. Dasari,
I. Barman, and M.K. Gundawar, Sci.
Rep. 5, 13169 (2015).
Jerome Workman, Jr. is the Senior
Technical Editor for Spectroscopy and
LCGC North America. Direct correspon-
dence to [email protected] ◾
For more information on this topic, please visit our homepage at: www.spectroscopyonline.com
Summary
Light sheet microscopy is evolving quickly and in many forms. From table-top “opentop” devices to miniaturized and modular systems, LSM is moving into pathology, being combined with more traditional confocal-type systems, and becoming simpler and more modu-lar. This mode of imaging will continue to gain in popularity as the hardware becomes increasingly developed. For spectroscopists, these are interesting developments to understand, as there are clear applications to consider, both in biology and elsewhere in planar il-lumination and imaging applications. A future edition of this column we will review some of the most common fluo-rescence methods and new techniques, including upconversion imaging.
References
(1) J. Reichmann, B. Nijmeijer, M.J. Hossain,
M. Eguren, I. Schneider, A.Z. Politi, M.J.
Roberti, L. Hufnagel, T. Hiiragi, and J. Ellen-
berg, Science 361(6398), 189–193 (2018).
(2) K. Chung, J. Wallace, S.Y. Kim, S. Kaly-
anasundaram, A.S. Andalman, T.J. Da-
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(3) D. Jing, S. Zhang, W. Luo, X. Gao, Y. Men, C.
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Z. Zhao, Cell Res. 28(8), 803–818 (2018).
(4) Retrieved from https://luxendo.eu/
news-events/news/2018/09/04/ground-
breaking-work-published-by-embl-utiliz-
ing-a-prototype-that-led-to-the-develop-
ment-of-the-invi-spim-by-luxendo/ on
September 22, 2019.
(5) US Patent 6,744,572.
(6) A.K. Glaser, N.P. Reder, Y. Chen, E.F. Mc-
Carty, C. Yin, L. Wei, Y. Wang, L.D. True,
and J.T. Liu, Nat. Biomed. Eng. 1(7), 0084
(2017).
(7) N.P. Reder, A.K. Glaser, E.F. McCarty, Y.
Chen, L.D. True, and J.T. Liu, Arch. Pathol.
Lab. Med. 143(9), 1069–1075 (2019).
(8) A.B. Kashekodi, T. Meinert, R. Michiels,
and A. Rohrbach, Biomed. Opt. Express
9(9), 4263-4274 (2018).
(9) Retrieved from https://involv3d.org/gal-
lery/ on September 27, 2019.
For more information on this topic, please visit our homepage at: www.spectroscopyonline.com
Steven G. Buckley,
PhD, is the Vice President
of Product Development
and Engineering at Ocean
Insight, an affiliate associate
professor at the University of
Washington, and has started
and advised numerous companies in spectros-
copy and in applications of machine learning.
He has approximately 40 peer-reviewed pub-
lications and 6 patents. His work in practical
optical spectroscopy, such as LIBS, Raman, and
TDL spectroscopy, dovetails with the coverage
in this column, which reviews methods (new
and old) in laser-based spectroscopy and op-
tical sensing. Direct correspondence to: Spec-
S
P
o
a
I
p
W
Continued from page 32
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www.spec t roscopyonl ine .com52 Spectroscopy 34(10) October 2019
PRODUCTS & RESOURCES TF fiber probe application noteAn application note from art photonics demonstrates an improved transflection (TF) fiber probe design and shows the probe’s increased functionality in experimental and industrial applications. According to the company, possible applications of the probe range from biopharmaceutical analysis, real-time reaction monitoring, analytical characterization, and the production and development of biofuels. art photonics,Berlin, Germany.www.artphotonics.com
EBSD patterns simulations softwareOIM Matrix software from Ametek EDAX is designed to provide dynamic diffraction-based electron backscatter diffraction (EBSD) pattern simulations and dictionary indexing capabilities. According to the company, the software allows users to simulate EBSD patterns based on the physics of dynamical diffraction of electrons. EDAX, Inc., Mahwah, NJ. www.edax.com
All-purpose diamond ATR accessoryThe IRIS diamond attenuated total reflectance accessory from PIKE Technologies is designed for infrared sampling of powders, gels, liquids, solids, and more. According to the company, the accessory is suitable for research, QA/QC, and sample identification. PIKE Technologies, Madison, WI. www.piketech.com
Raman analyzerThe Virsa Raman analyzer from Renishaw is designed for detailed remote analysis. According to the company, the analyzer is a fiber optic coupled spectroscopy system that includes a spectrometer with one or two internal lasers, with the dual excitation option enabling users to avoid fluorescence by switching between wavelengths at the touch of a button. Renishaw,West Dundee, IL. www.renishaw.com
Microwave digestion systemThe CEM MARS 6 microwave digestion system is designed to digest challenging samples for trace metals analysis. According to the company, the system has hundreds of preprogrammed methods and vessel options for high throughput and difficult samples. CEM Corporation, Matthews, NC.www.cem.com/mars6
Laboratory servicesOcean Insight’s laboratory testing and application consultation services are designed to provide a range of services for various customer application needs. According to the company, these services range from basic feasibility studies to more complex offerings such as experimental design, machine learning, and consultancy. Ocean Insight,Largo, FL. www.oceaninsight.com/solutions/lab-services
Optics, prisms, and polarizersREFLEX Analytical’s selection of optical materials are designed for use in vacuum ultraviolet through far-infrared detection. According to the company, the materials are manufactured into transmission windows, lenses, viewports, beamsplitters, attenuated total reflectance prisms, rods, hemispheres, and linear holographic infrared and free-standing wire grid polarizers.REFLEX Analytical Corporation, Ridgewood, NJ.reflexusa.com/noname.html
I n-situ reaction monitoring analyzerABB’s MB-Rx in-situ reaction monitoring analyzer is designed to provide plug-and-play analyses for research laboratories and pilot plants. According to the company, the analyzer offers chemists direct access to real-time experiment data via an insertion probe and an intui-tive software interface. ABB Measurements & Analytics,Quebec, Canada.www.abb.com/analytical
www.spec t roscopyonl ine .com October 2019 Spectroscopy 34(10) 53
X-ray system for XRFAmptek’s Mini-X2 miniature X-ray tube system is designed to simplify the X-ray fluores-cence (XRF) process by provid-ing a grounded anode, USB control of current and voltage, a collimator mount, and ease of operation. According to the company, the system is optimized for compact XRF applications. Amptek, Inc., Bedford, MA.www.Amptek.com/products/mini-x2-ray-tube
Raman spectrometerThe Mira P Raman spectrometer from Metrohm is designed for material varication in regulated industries. According to the company, the spectrometer is barely larger than a smartphone and provides results in seconds.Metrohm USA,Riverview, FL.www.metrohmusa.com
T ungsten and deuterium lampsREFLEX’s deuterium lamps, pre-aligned deuterium lamps, tungsten halogen lamps, and mounted tungsten halogen lamp assemblies are designed to complement the company’s xenon lamps, hollow-cath-ode lamps, photoionization detector PID lamps, mercury, and mercury-xenon ultra-violet (UV) detector lamps. According to the company, more than 1000 lamp types for UV-vis-NIR spectrometers, HPLC detec-tors, atomic absorption instrumentation, photochemical processing, fluorimetry, mercury analyzers, medical and environ-mental equipment, and fixed-wavelength detectors are available. REFLEX Analytical Corp., Ridgewood, NJ. www.reflexusa.com
Compact FT-IR spectrophotometerShimadzu Scientific’s IRSpirit FT-IR spectrophotometer is designed for space efficiency in the laboratory, allowing access from two sides, and providing a wide sample compartment. According to the company, the instrument includes a sealed interferometer that protects the beam splitter from both air and moisture. Shimadzu ScientificInstruments, Columbia, MD.www.ssi.shimadzu.com
Diffraction databaseThe PDF-4+ 2019 database from ICDD is designed for phase identifica-tion and quantitative analysis. Accord-ing to the company, the database includes 412,083 entries with digital patterns for use in total pattern analy-sis; 312,395 entries with I/Ic values for quantitative analysis by reference intensity ratio; and 311,225 entries with access to atomic coordinates sets for quantitative analysis by the Rietveld method. International Centre for Diffraction Data, Newtown Square, PA.www.icdd.com
High-throughput screening systemThe TR-WPS automated well plate measurement system from Ondax, now a Coherent company, is designed to automatically identify and screen polymorphic compounds and co-crystals, or quantify degree of crystallinity. According to the company, the system features automated calibration and collection, intrawell mapping, polarized light detection, and SPC and ASCII file format outputs. Ondax, now a Coherent company, Monrovia, CA.www.ondax.com
X-ray diffraction systemsRigaku’s sixth-generation Mini- Flex X-ray diffraction system is designed as a multipurpose ana-lytical instrument that can deter-mine phase identification and quantification, percent crystallinity, crystallite size and strain, lattice parameter refinement, Rietveld refinement, and molecular struc-ture. According to the company, the system includes a HyPix-400 MF 2D hybrid pixel array detector, an available 600-W X-ray source, and an eight-position automatic sample changer. Rigaku Corporation, Tokyo, Japan. www.rigaku.com/en/products/xrd/miniflex
CRMs for spectrophotometer qualificationStarna’s range of certified ref-erence materials (CRMs) for UV-visible spectrophotometer qualifications are designed to provide tailored solutions, advice, and support for a given situation. According to the company, the CRMs can help users of UV-vis spectrophotometers comply with the new editions of U.S. Pharmacopeia Chapter <857> and European Pharmacopoeia Chapter 2.2.25. Starna Cells Inc., Atascadero, CA.www.starnacells.com
54 Spectroscopy 34(10) October 2019 www.spec t roscopyonl ine .com
Amptek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Analytik Jena US . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
CEM Corporation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
EDAX, Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
International Centre for Diffraction Data . . . . . . . . . . . . . . . . . . . . . . . . . 9
Metrohm USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
MRS Spring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17
Ocean Insight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CV2
Ondax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
PIKE Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Renishaw, Inc.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Rigaku GMG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19
Shimazdu Scientific Instrument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CV4
Starna Cells, Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41, 43
Winter Conference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CV3
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