Advances in photo-thermal infrared imaging microspectroscopy

Robert Furstenberg*, Chris Kendziora, Michael Papantonakis, Viet Nguyen and Andrew McGill U.S. Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC 20375

ABSTRACT

There is a growing need for chemical imaging techniques in many fields of science and technology: forensics, materials science, pharmaceutical and chemical industries, just to name a few. While FTIR micro-spectroscopy is commonly used, its practical resolution limit of about 20 microns or more is often insufficient. Raman micro-spectroscopy provides better spatial resolution (~1 micron), but is not always practical because of samples exhibiting fluorescence or low Raman scattering efficiency. We are developing a non-contact and non-destructive technique we call photo-thermal infrared imaging spectroscopy (PT-IRIS). It involves photo-thermal heating of the sample with a tunable quantum cascade laser and measuring the resulting increase in thermal emission with an infrared detector. Photo-thermal emission spectra resemble FTIR absorbance spectra and can be acquired in both stand-off and microscopy configurations. Furthermore, PT-IRIS allows the acquisition of absorbance-like photo-thermal spectra in a reflected geometry, suitable for field applications and for in-situ study of samples on optically IR-opaque substrates (metals, fabrics, paint, glass etc.). Conventional FTIR microscopes in reflection mode measure the reflectance spectra which are different from absorbance spectra and are usually not catalogued in FTIR spectral libraries. In this paper, we continue developing this new technique. We perform a series of numerical simulations of the laser heating of samples during photo-thermal microscopy. We develop parameterized formulas to help the user pick the appropriate laser illumination power. We also examine the influence of sample geometry on spectral signatures. Finally, we measure and compare photo-thermal and reflectance spectra for two test samples.

Keywords: Chemical imaging, Micro-spectroscopy, Quantum cascade laser, Photo-thermal, Laser heating, Reflectance, Mie scattering, Forensics

1. INTRODUCTION FTIR micro-spectroscopy has emerged as one of the leading techniques for mapping the chemical structure of spatially inhomogeneous samples with feature sizes on the order of ~10-20 µm. In theory, its spatial resolution is diffraction limited, but due to weak IR sources the practical spatial resolution is often much poorer. One of the solutions is to employ brighter sources [1]. Synchrotron radiation facilities offer such bright sources, but they are of limited use to the average spectroscopist due to the small number of synchrotron facilities and associated usage costs. Recently, widely tunable quantum cascade lasers emerged as candidate sources for IR micro-spectroscopy. They offer bright sources in a small package suitable for tabletop and potentially fieldable systems. Commercially available QCL wavelengths span the whole mid-IR from around 4 µm to 12 µm). Currently the tuning range of a single QCL is about 10% around the center wavelength. Therefore, to cover the whole mid-IR range, several QCLs would need to be combined. Four QCLs could in principle cover at least 50% of the 6-12 µm fingerprint region.

An alternative to FTIR micro-spectroscopy is Raman micro-spectroscopy. It provides adequate spatial resolution (~1 μm), but is not always practical because samples exhibiting strong fluorescence or low Raman scattering efficiency.

We are developing a non-contact and non-destructive technique we call photo-thermal infrared imaging micro-spectroscopy (PT-IRIS). It involves photo-thermal heating of the sample with a focused tunable quantum cascade laser (or other suitable infrared laser) and measuring the resulting increase in thermal emission with an infrared detector. Photo-thermal spectra resemble FTIR absorbance even though they are acquired in a reflected geometry. This is especially advantageous for field applications and for in-situ study of samples on optically IR-opaque substrates (metals, fabrics, paper, paint, glass etc.). Conventional FTIR microscopes in reflection mode measure the reflectance spectra which are quite different from absorbance spectra (and which are usually measured in transmission mode).

* robert.furstenberg@nrl.navy.mil; phone +1 (202) 767-5947; fax +1 (202) 767-1697

Scanning Microscopies 2013: Advanced Microscopy Technologies for Defense, Homeland Security, Forensic, Life, Environmental, and Industrial Sciences, edited by Michael T. Postek, Dale E. Newbury, S. Frank Platek, Tim K. Maugel,

Proc. of SPIE Vol. 8729, 87290H · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2015990

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i

DichroicFilter

Reflecting ott.

40X0,4 NA

can stage

Furthermore, reflectance spectra are not catalogued in major FTIR spectral libraries. In theory, they can be derived from absorbance data (via the Kramers-Kronig transformation), but in practice this requires the knowledge of the absorbance spectrum across a wide spectral range.

In this paper, we continue the development of this new technique [2]. In particular, we perform a series of numerical simulations of the laser heating of samples that happens during photo-thermal microscopy. We develop parameterized formulas to help the user pick the appropriate laser illumination power and modulation frequency. Also, we measure and compare photo-thermal and reflectance spectra for two test samples.

2. DISCUSSION AND RESULTS

2.1 Photo-thermal IR Microscope

The details of our sub-diffraction-limited photo-thermal microscope were outlined in previous publications [2]. In this paper, we only consider the diffraction-limited modality. A simplified schematic of this setup is shown in Figure 1. A tunable QCL is focused onto the sample using a reflecting objective. The beam is modulated with a mechanical chopper at ~100Hz. As the sample is laser heated, it emits thermal radiation which is collected with the same objective and directed to a liquid nitrogen cooled single channel HgCdTe detector (Infrared Associates) through a dichroic beamsplitter. The signal from the IR detector is fed into a lock-in amplifier and the resulting demodulated signal is recorded with a computer. The sample is placed on a stage that can be controllably moved with respect to the laser position, allowing for scanning. To aid in focusing the infrared laser beam onto the sample we use a visible laser beam (co-aligned with the IR laser) and a removable viewer with a CCD camera (not shown in Figure 1).

Figure 1. Schematic drawing of the photo-thermal microscope.

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The theoretical laser spot size is given by [3,4]:

laser

2

D f4

πλMDspot = (1)

For the test-bed shown in Figure 1 and for λ=6.25 µm, M2=1.3 (manufacturer’s claim), Dlaser=7 mm (measured using a beam profiler) and f=6.4 mm the laser spot is Dspot=9.5 μm.

2.2 Imaging Software

The setup in Figure 1 is controlled by proprietary software developed at NRL for this particular application integrating data taking, display and analysis. A screen shot of the software is shown in Figure 2. The panel on the right is for setting the scan stage parameters and initiating image acquisition. Images from the hyperspectral datacube for a given wavelength are displayed on the left. In the middle, a plot of spectra at a given spatial spot or along a vertical or horizontal line in the image is displayed. It is also possible to group pixels together and display the average spectrum over a region of interest.

Figure 2. Graphical User Interface of the NRL photo-thermal microscopy software.

2.3 Laser Heating Simulations

Illuminating the sample with a focused laser beam during photo-thermal microscopy can produce substantial heating. This is also true for micro-Raman imaging (for dark, absorbing samples) where the laser spot is often <1 μm. The laser power should be set such that adequate signal is achieved without raising the temperature to levels that would damage the sample, or otherwise alter the spectral response.

We use the COMSOL Multiphysics software to model two distinct sample morphologies: flat solid and spherical particles on a substrate. The latter is useful for modeling particles in fingerprints (e.g. for forensics applications) or other trace (or bulk) amounts of solid particulates (e.g. in powder technology).

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For both sample morphologies, we give expressions for the highest temperature achieved for the given duration of the laser pulse and the thermal time constant which can be used to select an appropriate laser beam modulation frequency.

Flat (semi-infinite) solid substrate

The substrate is modeled as a 2 mm x 2 mm slab 1 mm thick with a laser beam heating the center of the slab. A 1 mm thick layer of air is placed above the sample to model sample-to-air heat transfer. The sample thickness of 1 mm is sufficient to model an infinite substrate, given the simulation time is less than the time it takes for the thermal weave to reach the bottom of the sample. The irradiance of the laser beam [W/m2] is assumed to be Gaussian and given by:

20

22

20

02)( rr

erPrI

−

=π

(2)

Where 0P is the total laser power reaching the sample and 0r is the radius at which the beam intensity falls off by 1/e2. The simulations were performed for P0=1 mW and r0=5 μm. The laser beam is assumed to be absorbed at the surface of the sample. This is the case of resonant absorption and will give the maximum temperature. It turns out the effect of air on the temperature profile is less than 5%. For the case without an air layer, an analytical expression for the thermal time constant and temperature of the hottest spot after laser illumination for a time t exists [5] and is given by:

Krc p

4

20ρ

τ = (3)

τπt

KrP

tT arctan2

)(0

2/30

max =Δ (4)

where ρ , pc and K are the density, heat capacity and thermal conductivity of the slab.

Figure 3a shows the temperature map of a soda-lime glass slide irradiated with a Gaussian laser beam (P0=1 mW, r0=5 μm) for t=2.5 ms. The time dependence of the temperature of the hottest spot is shown in Figure 3b for the cases with both air and vacuum above the glass. The vacuum case is in excellent agreement with Equation 4. The inclusion of an air layer above the substrate has a small effect and it lowers the temperature by ~3.5% compared to the sample in vacuum.

0 1 2 3 4 5-10

0

10

20

30

40

50

60

70

80

90

COMSOL Model (in air) COMSOL Model (in vacuum) Theory (in vacuum)

ΔTm

ax[K

]

Time [ms]

5 μm

(a) (b)

AIR

GLASS

Figure 3. (a) Simulated temperature maps of a soda-lime glass slide illuminated with a Gaussian beam (P0=1 mW, r0=5 μm) at the end of a 2.5 ms laser pulse. The laser heated region is localized to the size of the spot, confirming that the lateral spatial resolution is not degraded. (b) Temperature profile at the hottest spot. Simulated data is in excellent agreement with theory.

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We have performed simulations for a series of thermal conductivities (0.1 to 400 W/m/K) that span the range of relevant values and found out that the shape and size of the heated spot remains essentially the same.

Spatial Resolution

Figure 3a also illustrates that any degradation of the spatial resolution (both in the lateral and axial directions) is negligible compared to the laser spot size. This is in contrast with traditional photo-thermal spectroscopy and lock-in thermography where the excitation light is defocused and where the spatial (axial) resolution is determined by the one-dimensional thermal diffusion length given by:

cfKL

ρ4

= (5)

where f is the modulation frequency of the laser beam. For the experimental conditions in our simulation (f=200 Hz) this would be L=133μm. However, in a 3 dimensional geometry (as is the case in microscopy), the heat can diffuse in many directions and consequently the temperature can fall off rapidly as seen in Fig 3a.

Particles on a flat substrate

We model particles as spheres in contact with the substrate. The thermal resistance of the contact can vary depending on the material, its shape and how it sits on the substrate. However, for particles loosely attached to the substrate, varying the thermal contact resistance from 0 to infinity, will have at most a ~25% effect on the temperature a laser heated particle will reach. This is because the physical contact of the particle with the substrate is small [6,7]. As a result, the dominant heat loss mechanism is conduction from the particle to air. Previously, we have measure the contact conductance for polyethylene spheres [13]. We apply the same contact conductance here as well: 23500 pRH = [W/K], where Rp is the particle radius. The size of the substrate and air region above the sphere is the same as for the flat solid described above. The laser beam is assumed to be uniformly absorbed in the top hemisphere to simulate resonant heating which yields the highest temperature. We refer to this absorbed power as Pabs. We study the average temperature of the top hemisphere.

We performed a series of over 3000 simulations where we varied the particle size (Rp from 0.5 μm to 50 μm), and thermo-physical properties of both the particle (p) and substrate (s): thermal conductivity from 0.05 to 400 W/m/K, density from 100 to 10000 kg/m3 and heat capacity from 100 to 5000 J/kg/K. The duration of the laser pulse (Pabs=0.1 mW) was 5 times the estimated thermal time constant for the given sample. For each simulation run we obtained ΔTmax and τ by fitting the particle temperature rise to the following function:

( )( )τ/exp1)( max tTtT −−Δ=Δ (6)

The results of a typical simulation are shown in Figure 4. Figure 4a is the temperature map of a 10 μm diameter TNT particle on glass at the end of the laser pulse (5 ms). The temperature rise of the particle is shown in Fig. 4b along with the fit to Equation 6.

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0 1 2 3 4 50

5

10

15

20

25

Simulation Data FitΔT

max

[K]

Time [ms]

(a) (b)

5 μm

AIR

GLASS

TNT

t=5ms

Figure 4. (a) Simulated temperature maps of a 10 μm diameter TNT particle on a soda-lime glass slide illuminated with a Gaussian beam (P0=0.1 mW, r0=5 μm) at the end of a 5 ms laser pulse. (b) The temperature rise of the particle along with the fit to Equation 6.

Normally, for simple heat transport problems (such as the flat surface described above by Equations 3 and 4), the maximum temperature rise ( maxTΔ ) depends only on the thermal conductivity, while the speed at which this temperature is achieved (i.e. thermal time constant) depends only on the thermal diffusivity (α=K/ ρc). In the case of particles on substrates, ΔTmax is indeed only dependent on Kp and Ks (vide infra).

It was found that ΔTmax has a near linear dependence on K/1 .Also, it was found that ΔTmax ~1/R. Combining these two along with ΔTmax~Pabs we fit the simulation data points to the following formula:

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛+−+−−++++−=Δ

22/3max026425.02406.002704.01025.004070.006960.06296.04135.02616.004573.0

ppsppsspspspP

abs

KKKKKKKKKKKKRP

T (7)

For the thermal time constant we get:

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛+−+−−++++−= 22/3

2 0745.06653.01178.04338.02079.04321.06356.19135.10948.11413.0ppsppsspspsp

Ppp KKKKKKKKKKKKRcρτ (8)

These formulas should be applicable to a wide number of samples. The R dependence of these formulas is extremely close to 1/R and R2 (respectively) and should be valid well outside the 0.5-50 μm range tested.

So by combining Equations 7 and 8 with Equation 6, one can estimate the maximum allowed laser power (for the maximum allowed ΔT) and optimal modulation frequency for any sample. Please note that all equations in this paper assume SI units.

2.4 Spectral signatures

A very important point to consider in any spectroscopy is how spectral signatures compare to published library spectra. In chemical imaging the “gold standard” is absorbance spectra most often acquired by FTIR spectroscopy (most often in a transmission mode) and are readily available from various spectral libraries. With proper sample preparation, absorbance is proportional to the absorption coefficient, which is the only intrinsic property of the material. If the sample

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thickness or concentration is known, the infrared absorbance (calculated from FTIR transmittance spectra) is the absorption coefficient.

Sample morphology and measurement geometry will cause changes in measured spectral signatures due to interference, reflectance loss and other effects [8,9,10]. This is especially important in infrared microscopy applications where the feature sizes studied are often on the order of the wavelength of light used. It is important to point out that the following analysis is equally pertinent to conventional FTIR micro-spectroscopy as it is to photo-thermal microscopy.

To illustrate the effect of sample geometry, we will consider the case of spherical particles on a flat substrate. This is a complex geometry, and in lieu of a full electromagnetic analysis, we will apply the Mie scattering theory for an isolated sphere and neglect the influence of the substrate. This may be a reasonable approximation for dielectric substrates away from any strong optical resonances. We apply the convention and formulas for Mie scattering from Bohren et al. [11] for spherical particles with radii ranging from 1 µm to 50 µm. We examined two quantities: the absorption cross section scattering efficiency of the particle (relevant to the photo-thermal process) and backscattering efficiency relevant to reflectance microscopy. Scattering efficiencies represent scattering cross sections divided by the geometric cross section of the particle.

Figure 5a shows the absorption cross section efficiency along with the (bulk) absorption coefficient for a DNT particle in the range of our tunable QCL. It can be seen that the photo-thermal spectral signatures resemble the absorption coefficient for all but the two largest particles (for which the spectral peaks appear widely broadened). In contrast, Figure 5b shows the backscattering efficiency widely fluctuating as a function of particle size. Furthermore, reflectance spectra for most particle sizes deviate grossly from the bulk reflectance spectrum.

6.10 6.15 6.20 6.25 6.30 6.35 6.40 6.45 6.500.0

0.2

R=1 μm R=2.5 μm R=5 μm R=10 μm R=25 μm R=50 μm Bulk Reflectance

Ref

lect

ance

[a.u

.]

Wavelength [μm]6.10 6.15 6.20 6.25 6.30 6.35 6.40 6.45 6.500

R=1 μm R=2.5 μm R=5 μm R=10 μm R=25 μm R=50 μm Bulk Absorbance

Abs

orba

nce

[a.u

.]

Wavelength [μm] Figure 5. Mie scattering efficiencies for absorption (a) and backreflectance (b) as a function of particle size, along with bulk values. Absorption efficiencies appear less dependent on particle size.

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50µm

(b)

3. RESULTS

We demonstrate our infrared photo-thermal imaging micro-spectroscopy technique by applying it to two distinct types samples:

- A grain of DNT crystal on a glass substrate

- A MEMS chemical preconcentrator in the shape of a perforated thin polyimide membrane with platinum heater traces [12] and coated with a sorbent polymer HCSPA2 [13]. Both a bare device and one coated with a sorbent polymer are imaged. Figure 6a shows the optical micrograph of a bare (uncoated) preconcentrator.

The photo-thermal microscopy spectra for these test samples were described before [2]. In this paper, we report the reflectance spectra that were collected alongside photo-thermal spectra.

DNT crystal on glass

Spectra were collected at 35 wavelengths across the tuning range (6.1 µm to 6.5 µm) of a tunable quantum cascade laser. Laser power was on the order of 1mW and was modulated at 100 Hz. A 180 µm by 180 µm section of the sample was imaged in 15 µm steps (12x12 points).

Figure 6a shows an optical micrograph of a DNT crystal, while Fig 6b and 6c depict a slice of the hyperspectral datacube at 6.25 µm for the reflectance and photo-thermal image, respectively.

Figure 6. (a) Optical micrograph of a DNT crystal. Reflectance map (b) and photo-thermal map at 6.25 μm. The optical, reflectance and photo-thermal maps are slightly offset as the sample may have been bumped between scans.

Figure 7a depicts the average reflectance spectrum from the group of pixels comprising the particle, along with the average reflectance of the substrate (glass) as a function of wavelength. Due to high scattering losses, the reflectance spectrum of the particle was multiplied by 16 to bring its intensity on par with the reflectance off glass. Unfortunately, any subtle differences in reflectance that may exist are masked by a “fixed pattern” noise of the laser power fluctuations as the laser is tuned. This is due to absorption lines of water vapor in air and interference fringes from the pick-off beamsplitter used to measure the laser power. Since then, we have fixed this problem by measuring laser power in a reflection geometry (to minimize interference effects) and by carefully avoiding water vapor lines and choosing laser wavelengths that lie between those lines. Figure 7b shows the average photo-thermal spectrum from the DNT crystal and surrounding glass. Despite the fixed pattern noise, the difference between the two spectra is clear and a peak in the middle of the tuning range is clearly observed. This peak is wider than the absorption peak of bulk DNT, but this is in line with the expectation for larger particles (see Figure 5a).

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6.10 6.15 6.20 6.25 6.30 6.35 6.40 6.45 6.500

2

4

6

8

10

12

14

16

18 DNT Glass

Phot

o-th

erm

al S

igna

l [a.

u.]

Wavelength [μm]6.10 6.15 6.20 6.25 6.30 6.35 6.40 6.45 6.500

10

20

DNT (X16) Glass

Ref

lect

ance

[a.u

.]

Wavelength [μm] Figure 7. Reflectance (a) and photo-thermal (b) average signals from the DNT crystal (red line) and glass background (black line).

MEMS preconcentrator

Spectra were collected at 35 wavelengths across the tuning range (6.1 μm to 6.5 μm) of a tunable quantum cascade laser. Laser power was on the order of 1mW and was modulated at 210 Hz. A 300 μm by 300 μm section of the MEMS preconcentrator was imaged in 16.66 μm steps (18 x 18 points) for reflectance and 12 μm steps (25 x 25 points) for the photo-thermal data.

Fig 8a and 8b show the photographs of the device and the absorbance spectra of polyimide and the sorbent polymer. Figure 8c and 8d shows photo-thermal images on-resonance and off-resonance with the sorbent polymer. Figure 8e and 8f show the photo-thermal spectra of a group of pixels over the platinum trace (sorbent polymer only) and away from the platinum trace (spectral mixture of the polyimide and the sorbent polymer). The polyimide spectrum of a bare (uncoated) device is shown in Figure 8g. We show that the coating process was successful and that the device surface was fully coated, with limited thickness variation. [2]

Fig 9a shows the average reflectance spectra of a group of pixels over the platinum trace and another group from the polyimide membrane. As the platinum is deposited on top of the polyimide, this part of the sample should only have signatures from the sorbent coating, HCSPA2 [13]; the spectra from the polyimide range should contain signatures from both the polyimide and the sorbent coating. Figure 9b shows a reflectance map at 6.35 μm. The reflectance spectra have a subtle feature around 6.35 µm which we suspect to be related to the absorption peak of the sorbent polymer.

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50μm

6.10 6.15 6.20 6.25 6.30 6.35 6.400.0

0.4

0.8

1.2

1.6

2.0

Phot

o-th

erm

al S

igna

l [V]

Wavelength [μm]

6.10 6.15 6.20 6.25 6.30 6.35 6.400.0

0.4

0.8

1.2

1.6

2.0

Phot

o-th

erm

al S

igna

l [V]

Wavelength [μm]

6.10 6.15 6.20 6.25 6.30 6.35 6.400

1

2

3

4

5

6

7

8

Phot

o-th

erm

al S

igna

l [V]

Wavelength [μm]λ=6.17 μm λ=6.30 μm

6.10 6.15 6.20 6.25 6.30 6.35 6.400.06

0.08

0.10

0.12

0.14

0.16

0.18

Polyimide HCSFA2

Abs

orba

nce

[a.u

.]

Wavelength [μm]

FTIR (ATR)

POLYIMIDE

HCSFA2

POLYIMIDE+HCSFA2

(a)

(b)

(c) (d)

(e)

(f)

(g)

PERFORATION(AIR)

POLYIMIDEMEMBRANE

WITHHCSFA2

COATING

PLATINUM TRACE

Figure 8. Chemical imaging of a MEMS preconcentrator coated with a sorbent polymer [2].

6.10 6.15 6.20 6.25 6.30 6.35 6.400

5

10

15

20

25

HCSFA2 only HCSFA2+Polyimide

Ref

lect

ance

[a.u

.]

Wavelength [μm](a) (b)

Figure 9. Reflectance spectra (a) from the region above the platinum heater trace (sorbent only region, red line) and over the polyimide membrane (sorbent+polyimide signal). (b) Reflectance map at 6.35 μm is showing spectral contrast between sorbent polymer and polyimide membrane.

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4. CONCLUSIONS We presented a novel chemical imaging technique based on infrared photo-thermal spectroscopy that provides improved spatial resolution compared to FTIR micro-spectroscopy. Samples can be studied in a reflected geometry which is very useful for in-situ studies and when samples are on opaque substrates. Measured photo-thermal spectra were similar to absorbance spectra as measured by FTIR spectroscopy. We also measured reflectance spectra with the same test bed and showed that reflectance spectra are harder to interpret and vary with sample geometry. Photo-thermal spectra are much more immune to variations in sample geometry. We also performed modeling of the photo- thermal process and derived formulas for the maximum allowed laser power and thermal time constant. We successfully demonstrated our microscopy setup on two test samples, a grain of DNT on glass and a flat MEMS preconcentrator.

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