“Nanotechnology Nanotechnology” · 2020. 7. 30. · events like National Nanotechnology Day,...

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Dr. RadislavA. Potyrailo

• MS Optoelectronics (Kiev Polytechnic Institute), PhD Analytical Chemistry (Indiana Univ.)

• Principal Scientist at GE Research, leads sensors programs for industrial/consumer markets

• Principal Investigator on programs funded by GE, industrial partners, US Government

• 125+ granted US Patents on sensing concepts/implementations to solve monitoring needs

• Examples of commercialized sensors for GE businesses: GE Plastics, GE Water,GE Oil & Gas

• 150+ publications, 10+ keynote/plenary lectures

• Examples of contributions to scientific community:- Editor of the Springer-Nature book series IntegratedAnalyticalSystems

- Chair of the MEMS and Sensors Industry Group (MSIG) Device Working Group

- North America Regional Chair of International Society for Olfaction and Chemical Sensing

- Initiator of the First Gordon Research Conference on Combinatorial Materials Science

• Recent recognitions include SPIE Fellow, Prism Award by Photonics Media/SPIE

GE ResearchElectronics& Sensing


>> Stacey Standridge: Good afternoon, and thanks for tuning in for today's webinar,“Nanotechnology For A New Generation of Gas Sensors: And Industrial Perspective onFundamental, Applied, and Commercialization Aspects.” My name is Stacey Standridge.I am Deputy Director of the National Nanotechnology Coordination Office, and I will bethe moderator today. This webinar is the first in our quarterly webinar series in supportof the “Nanotechnology for Sensors and Sensors for Nanotechnology” SignatureInitiative, and I'm happy to introduce our guest today, Dr. Radislav Potyrailo. Dr.Potyrailo is a principal scientist at GE Global Research, leading the sensors programs forindustrial and consumer markets with commercialized sensors for businesses, includingGE Plastics, GE Water, and GE Oil and Gas. Dr. Potyrailo has been a principal investigatoron programs funded by National Nanotechnology Initiative participating agencies,including DoD, DOE, DHS, NIH, and NIOSH. Efforts from these and other programs haveresulted in excess of 125 granted U.S. patents and 150 publications on transducertechnologies, sensing materials, and data analytics describing sensing concepts andtheir implementations. You can see a bit more about his background on the slide infront of you. I will also say that Dr. Potyrailo's recent recognitions included being namedSPIE Fellow and receiving the Prism Award by Photonics Media at SPIE. (Continued...)

1

Dr. RadislavA. Potyrailo

• MS Optoelectronics (Kiev Polytechnic Institute), PhD Analytical Chemistry (Indiana Univ.)

• Principal Scientist at GE Research, leads sensors programs for industrial/consumer markets

• Principal Investigator on programs funded by GE, industrial partners, US Government

• 125+ granted US Patents on sensing concepts/implementations to solve monitoring needs

• Examples of commercialized sensors for GE businesses: GE Plastics, GE Water,GE Oil & Gas

• 150+ publications, 10+ keynote/plenary lectures

• Examples of contributions to scientific community:- Editor of the Springer-Nature book series IntegratedAnalyticalSystems

- Chair of the MEMS and Sensors Industry Group (MSIG) Device Working Group

- North America Regional Chair of International Society for Olfaction and Chemical Sensing

- Initiator of the First Gordon Research Conference on Combinatorial Materials Science

• Recent recognitions include SPIE Fellow, Prism Award by Photonics Media/SPIE

GE ResearchElectronics& Sensing


>> Stacey Standridge: In the webinar today, Dr. Potyrailo will first discuss possibilitiesfor new principles of gas sensing based on nanomaterials and nanostructures. Then, hewill discuss the development of sensors capable of quantifying individual chemicalcomponents in mixtures, rejecting interferences, and enhancing response stability inwearable, stationary, and other formats. If you have questions as the presentation isgoing along, please feel free to send those to [email protected], or you can usethe “Submit Your Questions Here” window in the webinar interface. If you would likemore information about the Signature Initiatives and the Federal resources supportingthe development of nanosensors, or if you would like information about upcomingevents like National Nanotechnology Day, which is taking place next week on October9, please feel free to visit our website at nano.gov. And with that, I am happy to handthe floor over to Dr. Potyrailo.

>> Radislav Potyrailo: Thank you, Stacey, and hello everybody! Good morning, and goodday! I'm glad to discuss today with you this topic. You can see from the first slide thatI'm at the GE Global Research Center, an organization that is developing new types ofelectronic and sensing technologies. So, I will be going through the slides in such a wayas to give you the perspective of the state-of-the-art of gas sensors now, and hownanotechnology solutions are advancing this state-of-the-art quite significantly.

2

Nanotechnologyfor a new generation of gas sensors:

An industrial perspectiveon fundamental, applied, and commercialization aspects

National Scienceand Technology Council, National Nanotechnology Initiative Sensors NSI WebinarSeries

OCTOBER 02,2019

Dr. Radislav A. Potyrailo (GE Research, USA)

Image: GE (R.A.Potyrailo)


>> Radislav Potyrailo: This is the title of the presentation.

3

Technolo

gy

eff

ort

GEResearch

GEBusinesses,

External partners

Tech transfer NPI Product maturity


Our industrial R&D goal:

Develop technologies with new capabilities, transition for commercialization

Idea/discovery Feasibility

TRL = technology readinesslevel

NPI = new productintroduction

1995

SILICON CARBIDE PHOTODIODE 1997

WORLD’S FIRSTFULL FIELD X-RAY

2005

CT DETECTOR SYSTEM INTEGRATION

SILICON CARBIDE MOSFET

2014

RF MEMS SWITCH

RF MULTI-GAS SENSORS START-UP

2015 2018

IN-SITU TISSUEMULTIPLEXINGPLATFORM

2011

MULTI-ION WATER SENSORS

2014

2017

RF MULTIPARAMETER OIL SENSOR

Transitioned to commercialization

Early TRLresearch

>> Radislav Potyrailo: Because we are an industrial research organization, we are notonly focusing on innovative aspects of sensor technologies (you can see on the left ofthe slide the early technology readiness level (TRL) research that we do), but alsoworking with our GE partners and organizations in the United States and around theworld to transition our findings into successful commercial products.

4

Webinar outline

Segment1: Understanding and appreciating the StatusQuo

Segment 2: Breaking through the philosophy of conventional

sensorsSegment 3: Improving sensors for diverse newapplications

Segment 4: Commercialization unleashed


>> Radislav Potyrailo: The webinar is organized in four segments, outlined here.Segment 1 is giving us the ability to learn the basics, to understand the state-of-the-artnow (I call it the “Status Quo”). Segment 2 will demonstrate how to break through thephilosophy of conventional sensors. Segment 3 will show us how we can improve theneeded capabilities of gas sensors, what types of improvements are still required, andhow we can take advantage of all these learnings. Segment 4 will focus oncommercialization.

5

Learnings

Webinar Segment 1Understanding

and appreciating the Status Quo

Webinar Segment2 Breaking through the philosophyof conventional sensors

Webinar Segment3Improving sensors

for diverse new applications

Commercialization unleashed

(Webinar Segment 4)


>> Radislav Potyrailo: I view the four segments as a spiral of learning, because thebasics that we are learning in Segment 1 are expanded into the other segments. At theend, we have a lot of knowledge, which helps us move the technology forward.

6

Definitions of some key terms in gassensing

Pure Appl. Chem. 1991, 63,1247J. Janata, Principles of Chemical Sensors, Springer, 2009R. A. Potyrailo et al., Chem. Rev., 2008, 2011,2016

• Sensor calibration curve - relationship between measured variable X (gas conc.) and sensor response Y

• Sensitivity – slope of the response curve per unit concentration

• Dynamic range - concentration range of a linear portion of the sensor response curve

• Selectivity –ability of the sensor to measure one gas in the presence of others

• Accuracy – is how close a resultcomes to the true value measured with a reference instrument

Gas sensor – miniaturedevicethat recognizes in real time a change in a concentration

of one or more gases and converts this information into analytically useful signal(s)

Calibration curve Selectivity Correlation plot


>> Radislav Potyrailo: It's very important to define what gas sensors are early, becausethat will give us the ability to understand more clearly the examples that I will beshowing in this presentation. For this community, a gas sensor is a device, and thisdevice recognizes changes in the concentration of one or more gases in real time bycontinuously doing measurements and by converting the information that is containedin the device into analytically useful signals.

On this slide, I highlight important aspects of gas sensors. To the left, the calibrationcurve is the relation between the gas concentration and the sensor output, or sensorresponse. As an example, I am showing you one output per sensor. The dynamic rangeis the linear portion of this calibration curve, and the slope of this response is known asthe sensitivity. In the middle, I show the selectivity, which is another important aspectof a gas sensor. The selectivity is related to how nicely I can detect the gas of interest,which I am labelling “Gas 1,” in the presence of other types of gases. So, suppressingthe effects on my sensor response is a very important aspect of being highly selective,or selective enough. (Continued...)

7

Definitions of some key terms in gas sensing

Pure Appl. Chem. 1991, 63,1247J. Janata, Principles of Chemical Sensors, Springer, 2009R. A. Potyrailo et al., Chem. Rev., 2008, 2011,2016

Gas sensor – miniaturedevicethat recognizes in real time a change in a concentration

of one or more gases and converts this information into analytically useful signal(s)

Calibration curve Selectivity Correlation plot


• Sensor calibration curve - relationship between measured variable X (gas conc.) and sensor response Y

• Sensitivity – slope of the response curve per unit concentration

• Dynamic range - concentration range of a linear portion of the sensor response curve

• Selectivity –ability of the sensor to measure one gas in the presence of others

• Accuracy – is how close a resultcomes to the true value measured with a reference instrument

>> Radislav Potyrailo: To the right, I show another very important aspect of gas sensors:the correlation plot. On one axis, you're doing the gas measurement by a referencemethod – the method that you trust. It may be quite expensive but, very importantly,such a graph will give you an idea of how good the sensor is in relation to a trustedmethod. The closer the data points are to this line (with a slope of one), such as thegreen points in this example, the better the correlation is and the more successful thesensor will be in different applications. I am also showing points with other colors:yellow points, with marginal accuracy (or marginal correlation), and red points, withpoor correlation.

I am spending a lot of time on this because you will see examples from the field, andyou will judge for yourself how they look like. These are very important aspects of gassensors. The line with slope one will give you the quality of the fit. Typically, people callthat quality the r^2 value. So, if r^2 = 1, then all of my data points line up extremelywell into this line.

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DriversEnvironmental

Urban air pollution

Health / FitnessMedical condition

Home / RetailFood freshness

Industrial safetyPersonal monitors

Trends

Environmental Accuracy

Health / Fitness Accuracy, power

Home / Retail Accuracy, cost

Industrial safety Accuracy, power

Environmental background O2, CO2, H2O

Atmospheric pollutants CO2, CO, O3, H2S, NH3,

NOx, SO2, CH4

Breath biomarkers NO, H2S, NH4, acetone

Homeland safety toxic industrial

chemicals, explosives

Industrial safety H2S, CO, CH4, O2

Food safety

Driving forces for multiplemarkets

Industrial Safety: $3B, Food Safety: $14B,Health self-monitoring $15Bhttps://www.marketsandmarkets.com/PressReleases/safety-instrumented-system.asp

https://www.sensorsmag.com/components/food-safety-testing-market-worth-16-1-billion-usd-by-2020

https://www.prnewswire.com/news-releases/health-self-monitoring-technologies-and-global-markets-300102733.html

https://www.mordorintelligence.com/industry-reports/global-wearable-sensors-market

Wearable sensors: $7B (2017) → $24 B (2023)


>> Radislav Potyrailo: This slide shows that in the air, many gases are present. In thisexample, I am showing ambient air. You can see the environmental background anddifferent types of pollutants. If you are in an industrial setting, then there would beindustrial emissions, as well. The drivers behind the development of different types ofgas sensors are highlighted on the left, and the trends are highlighted on the right. Youcan see that I highlighted the word “Accuracy” in red, because the end users andapplications are calling for adequate accuracy. I'm not saying 100% accuracy, but“adequate” is a very important aspect of that.

Another important aspect is that the driving forces are different types of markets thatare interested in having that new gas-sensing solution. You can see that industrial safetymarkets are around $3 billion, food safety $14 billion, and health self-monitoring $15billion. So these are very important, sizable driving forces that allow us to focus onimpactful directions and provide technical solutions. Also, at the bottom of this slide, Iam showing that wearable sensors (the ones that will have the high-qualityperformance at low power and form factors) are also quite significant markets, so theseare very important driving forces.

9

Important health-related volatiles:

Ris

k

10-4

10-6

10-8TLV

(ppm)0.5

0.1

10

750

1000

Risks from: Loh et al., Environmental Health Perspectives, 2007, 115,1160-1168

TLV (Threshold Limit Values) from: Permissible Exposure Limits / OSHA

outdoor and indoor environments

Major outdoor volatile pollutants Indoor volatiles ofimportance

Ozone < 0.1ppm

Nitrogen dioxide < 0.2ppm

Sulfur dioxide < 0.2 ppm

Carbon monoxide <30 ppm

•Some volatiles are much more toxic than others

•Toxic volatiles often are at much lower levels than benign volatiles

•Need to detect toxic volatiles in complex background of benign volatiles


Brunekreef, Holgate, Lancet 2002, 360, 1233–1242

who.int/ceh/capacity/Outdoor_air_pollution.pdf

euro.who.int/document/e71922.pdf

who.int/airpollution/ambient/pollutants/en/

>> Radislav Potyrailo: This slide highlights a subset of the volatiles from the previousslide. I am highlighting the volatiles that I (and many people) care about, because ofhealth concerns about these volatiles. On the left, I am showing the top four volatilesfrom ambient outdoor air, and you can recognize that these are ozone, nitrogendioxide, sulfur dioxide, and carbon monoxide. You heard about them a lot, and you cansee that their allowed levels are still on the borderline of being dangerous to health.Ozone, nitrogen dioxide, and sulfur dioxide are less than one part per million, andcarbon monoxide is less than 30 parts per million. These values are important to keepin mind. So, out of all kinds of gases that you saw on the previous slide, these areimportant outdoor pollutants for health.

On the right, I am showing data from the literature about indoor volatiles. This graphshows on the y-axis the risks of different types of volatiles; the x-axis is highlightingmany examples of those volatiles. Those highlighted in blue show the levels of so-calledthreshold limit values (TLVs) – permissible exposure limits, in parts per million.(Continued…)

10

Important health-related volatiles:

Ris

k

10-4

10-6

10-8TLV

(ppm)0.5

0.1

10

750

1000

Risks from: Loh et al., Environmental Health Perspectives, 2007, 115,1160-1168

TLV (Threshold Limit Values) from: Permissible Exposure Limits / OSHA

outdoor and indoor environments

Major outdoor volatile pollutants Indoor volatiles ofimportance

Ozone < 0.1ppm

Nitrogen dioxide < 0.2ppm

Sulfur dioxide < 0.2 ppm

Carbon monoxide <30 ppm

•Some volatiles are much more toxic than others

•Toxic volatiles often are at much lower levels than benign volatiles

•Need to detect toxic volatiles in complex background of benign volatiles


Brunekreef, Holgate, Lancet 2002, 360, 1233–1242

who.int/ceh/capacity/Outdoor_air_pollution.pdf

euro.who.int/document/e71922.pdf

who.int/airpollution/ambient/pollutants/en/

>> Radislav Potyrailo: How much are different volatiles allowed to be present beforethey become dangerous to human health? You can see that some volatiles can bedangerous after they reach very small levels (less than one part per million), such asbenzene and formaldehyde. Other ones can be dangerous only after they reach higherconcentrations, such as ethanol and acetone (from nail polish remover, for example).What this means (the punch line for this graph) is that in the presence of all kinds ofvolatiles, we need to focus on the ones that are really harmful to our health and, in thepresence of other volatiles, we need to focus on the ones that are less harmful but arepresent in maybe four orders of magnitude higher concentrations. Resolving the lowconcentrations may be a challenge. The take-home message is that if we would like toreliably detect these types of toxic volatiles, we need to do it in a complex backgroundof volatiles that are much more benign.

11

Spinelle, L.et al., Review of low-cost sensors for the ambient air monitoring of benzene and other volatile organic compounds, EUR 27713.

European Union, Joint Research Centre: Luxembourg, 2015

Dynam

ic r

ange f

or

eth

yle

ne

oxid

e (

ppm

)

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

Dynam

icra

nge

for

div

ers

egase

s(p

pm

)

0.0001

0.001

0.01

0.1

1

10

100

1000

Dynam

ic r

ange f

or

benzene

(ppm

)

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

Well-established sensor technologies

are available to detect broad range of gas concentrations

Metal oxidesemiconductor10000

Photoionization Electrochemical

Typical dynamic ranges of different sensor types


>> Radislav Potyrailo: This slide shows that many prominent sensors (which we can getfrom the internet) are based on different detection principles, such as metal-oxidesemiconducting materials (which are used to measure resistance change, depending onthe types of gases), photoionization detectors, and electrochemical ones. Here, we areshowing that different types of sensors have different dynamic ranges for themeasurement of gas concentration (on the y-axis, in parts per million). You can see thatthe dynamic ranges span about three orders of magnitude of concentrations (andsometimes four), and depending on the type of sensor, you are at different levels, aswell. That's important to keep in mind.

12

Conventional gas sensors:Serving sensing needs in establishedmarkets

alphasense.com

amphenol-sensors.com

•mature technologies

•widely available

• Interchangeable

• inexpensive

Understanding the roots:single-output devices for detection of expected gases at high concentrations

Polymeric blends for humidity

US Patent 2047638, 1936

Metal oxides for gases

US Pat. 3644795 1972

Catalysts for combustible gases

US Patent 3092799, 1963

“Simple sensors performbest whenpollution levels are high and

when the compound of interestswamps others” Lewis, Edwards, Nature 2016

Un

iva

ria

tere

sp

on

se

Gas 1Gas 2

Gas 3


>> Radislav Potyrailo: This slide shows that the gas sensors from the previous slidecome from innovations that have been happening continuously since the 20th century.You can see that gas sensors have been invented in the 1930s, 1960s, and 1970s. Theyhave been very important analytical instruments to measure gases whose presenceneeds to be avoided in the atmosphere, or when they create explosive or toxicenvironments. That's why single-output sensors, like the ones shown here, are perfectfor those scenarios. Currently, gas sensors have very small form factors and consumeless and less power. They are getting into different types of wearable devices and arequite inexpensive. In the middle of the slide, I'm showing that gas sensors often arecomprised of a sensing material and a physical transducer, to recognize changes in thesensing material. By design, these sensors are reversible. By definition, a sensor iscontinuously doing measurements, so the interaction forces in these types of sensingmaterials are quite weak, in order to release the gas that is being detected.(Continued…)

13

Conventional gas sensors:Serving sensing needs in establishedmarkets

alphasense.com

amphenol-sensors.com

•mature technologies

•widely available

• Interchangeable

• inexpensive

Understanding the roots:single-output devices for detection of expected gases at high concentrations

Polymeric blends for humidity

US Patent 2047638, 1936

Metal oxides for gases

US Pat. 3644795 1972

Catalysts for combustible gases

US Patent 3092799, 1963

“Simple sensors performbest whenpollution levels are high and


Un

iva

ria

tere

sp

on

se

Gas 1Gas 2

Gas 3


>> Radislav Potyrailo: On the top of this slide, the second plot from the left shows theresponse of a sensor in the presence of “blank” (no gas present). You can see that theincrease in gas concentration made the signal change more and more proportionately.So schematically, what I'm showing here is that because of the way the sensor wasdesigned, it is okay that it will respond to different gases with slightly or similarintensity, because for those intended applications, there is no other gas that you careabout. If you are interested in flammable gases, you know that a flammable gas is theonly one that would be present. That's why at the bottom of the slide, I put a quotefrom a Nature article about the fact that these types of sensors are simple by designand perform best when the signal is strong and when nothing else will affect thatsensor response. That's a very important aspect of that type of sensors.

14

Nanotechnology impact on gas sensors

Potyrailo et al. Anal. Chem.

1998 Potyrailo et al. Analyst

2013Potyrailo et al. Angew. Chem. Int. Ed. 2013

Potyrailo et al. Analyst 2012Potyrailoet al. Nature Communications 2015

20nmSensor electrode

Engineered contact layer

Interfaces

Sensing film

CNTsGraphene

NanotechnologyQuantum dots Ligand–Au NPsConventional

Polymers Metal oxides

NanostructuresElectrical transducers

500 nm

Nanomaterial - material having particles or constituents of nanoscale dimensions, or one that is produced by nanotechnology

Nanostructure - structure of intermediate size between microscopic and molecular structures

Advantages of nanotechnology over conventional designs

Nanomaterials: high sensitivity due to surface area fast responsedue to 2D, 3D diffusion

Nanostructured transducers: refined sensitivity andselectivity control

Anticipated risks: drift, falsealarms

CNTs – carbon nanotubes NPs – nanoparticles


>> Radislav Potyrailo: Over the past several years, nanotechnology solutions have beenimpacting the development of these types of sensors in a very positive way. On thisslide, you can see on the left conventional sensing materials (polymers and metal oxidepowder) and conventional transducers (you can see a couple of transducer resistivereadouts). On the right of the slide are examples of new types of materials andinterfaces from nanotechnology. In my personal opinion, the advantages of thisnano‐approach are quite broad, and they range from improvement in materials tomuch higher sensitivity, compared to conventional materials (because of their surfacearea and properties), and much faster response time and recovery time (due to theability of the gases to have radial or 3D diffusion). If we make different types oftransducers that have that improved sensitivity and selectivity, we also needunderstand design principles for this transducer to behave that way. Anticipated risksare still related to drift and false alarms. From recent literature, we see here impactfulexamples of these types of technical solutions using nanomaterials.

15

Ref. 3: Borini et al., ACS Nano 2013, 7,11166-11173

Ref. 4: Favier et al., Science 2001, 293, 2227-2231

Prominent examples of gas nanosensors:

Fast response time - CNTs, graphene,nanowires

CNTs: response time

Ref.1

Graphene: response time

Ref.3

CNTs: response time

Ref.2

Pd microwires: response time

Ref.4

Ref. 1: Kayastha Sensors EXPO & Conference 2017

Ref. 2: Snow et al., Science 2005, 307,1942-1945 Public Copyright © 2019 GE All rights reserved

>> Radislav Potyrailo: This slide shows the response times that have been achieved forsensors based on carbon nanotubes, graphene, and palladium microwires. Theseresponse times are very impressive. Brewer Science (plot on the top left) is a companythat commercializes gas sensors for community measurements. The Naval ResearchLaboratory (plot on the bottom left) shows very impressive work, which was publisheda few years ago in Science (on rapid response and selectivity for functionalized carbonnanotubes). You can see other examples from the literature, as well.

16

Ref. 1: Shi et al., Appl. Phys. Lett. 2019, 115,121602Ref. 2: Mulchandani & co-workers, ACS Nano 2019, 13, 3196−3205


Room- and high-temperature operation, light activation, flexible

Ref.3

Red-light activated MoS2

Ref.2

Graphene on flexible polyimide

Ref.4

Ref. 3: Houlihan et al., ACS Sens. 2018, 3,2684−2692

Ref. 4: Stanford et al., ACS Nano 2019, 13, 3474−3482 Slide 13

Absorb

ance

UV-light activated TiO2/GaN nanowires High temperature plasmonics

Ref. 1 600 Wavelength (nm) 800


>> Radislav Potyrailo: Our next examples from the literature are about other types ofsensors with nanomaterials at room temperature and high temperature and also lightactivation (plot on the bottom left), to improve the response recovery times of thenanomaterials and also for wearable devices and other applications. Technical solutionson flexible substrates (plot on the bottom right) is an important aspect of thatdevelopment, as well. The example of new UV light-activated nanowires (plot on thetop left) is something that I'm very happy to share with you. It comes from GerogeMason University, George Washington University, and N5 Sensors, Inc. Also, as you cansee, most of these examples are from this year or from last year.

17


Functionalization, elimination of humidity effects for selectivity

Semiconductor-Enriched SWCNTs

Ref.1

Au NPs-CNTs with Ion Exchange Matrix

Ref.2

Pt NPs on functionalized silane

Ref.4

Ref. 3: Meyyappan & co-workers, ACS Appl. Nano Mater. 2019, DOI: 10.1021/acsanm.9b01401Ref. 1: Star & co-workers, ACS Sens. 2019, 4, 2084−2093

Ref. 2: Swager & co-workers, Chem. Mater. 2019, 31, 5413−5420 Ref. 4: Javey & co-workers, ACS Sens. 2019, 4, 1857−1863

Carboxylated SWCNTS with Varying pH

Ref.3


>> Radislav Potyrailo: My last example on nanomaterials is related to thefunctionalization of different types of nanomaterials – mostly carbon nanotubes, asyou can see, and also different types of nanoparticles. Why? Because we need to get amore selective response and also to reject the humidity effect. Because humidity ispresent in ambient air at very high levels, at 50% humidity, there is 15,000 parts permillion of water vapor. At much higher humidity, that number increases to up to20,000 parts per million. So, the ability to reject such levels of humidity is a veryimportant aspect of practical sensors.

18

Potyrailo, Mirsky, Chem. Rev. 2008 Potyrailo et al., Chem. Rev. 2011 Potyrailo, Chem. Rev. 2016

Top 3Focused sensor

requirements

High reliability, accuracy

Low cost

Low power

Markets for Sensors in Industrial Internet, 2014 Potyrailo, IDTech Internet of Things 2014 Potyrailo, TSensors Summit 2015

Top 10General sensor

requirements

Dynamic range

Initial cost

Long-term stability

Operation cost

Power consumption

Response speed

Robustness

Selectivity

Sensitivity

Size

Requirements and opportunities for

modern sensors


>> Radislav Potyrailo: What we hear from end users is that while there are manytechnical requirements for sensors (you can see a list of those requirements inalphabetical order on the left of this slide), basically, the technical requirements boildown to the top three, which are driven by those different markets that I showed onslide 2. Those top three requirements are related to how accurate a sensor is (reliabilityis related to accuracy), low cost to use it in ubiquitous applications, and low power fordifferent types of wearables and mobile solutions. These are the requirements that arecoming from end user and system integrators.

A gas sensor is a type of analytical instrument that was designed in the 20th century tohave a single output. Why? Because you don't need more outputs, you need to detectonly one gas (and you know what it is). That's why these days, if we're trying to usethese types of sensors in applications that there were not intended for, then we needto pay attention to the fact that they will respond to other gases, as well.

19

Gas cross-sensitivity of sensors: what does it mean?

Gas cross-sensitivity in conventional andnanomaterialsdue to conflicting requirements for sensor selectivity vs reversibility

Conventional Nanomaterials

MOFs CNTs

Graphene

Y-axes represent

corresponding

sensor responses

Ligand-metal NPs

Potyrailo, Naik, Annu. Rev. Mater. Res. 2013

MOFs – metal organic frameworks CNTs – carbon nanotubesNPs – nanoparticles

Un

iva

riate

resp

on

se

Gas 1Gas 2

Gas 3

Common theme forconventional andnanomaterials


>> Radislav Potyrailo: When we buy conventional sensors (left side of the slide) fromdifferent manufacturers, they have tables or bar graphs (or other type of summaries)that say “I am calibrated for this gas or that gas, but I will respond to many other gases,so please pay attention to that fact.” Why? Because, as we were mentioning, theresponse from these sensors needs to be reversible, and therefore the interactionforces between the sensing material and the gas should be weak enough to release thegas, once the concentration gets smaller and smaller. So that's why these nonselectiveresponses are happening, because of the way these sensors are designed.

For gas sensors made with nanomaterials, here are examples from the literature (rightside of the slide) showing that different types of nanomaterials have these types ofresponses to different gases, or vapors. (I call them “volatiles,” in general.)

My schematic (“Common theme for conventional and nanomaterials”) shows that,indeed, both types of sensors will respond to different gases.

20

Exploring new applications with traditional gas sensors

https://www.treehugger.com/clean-technology/air-

quality-sensor-makes-a-fashion-statement.html

https://techcrunch.com/2017/01/03/plume-labs-

flow-is-an-air-quality-tracker-to-avoid-pollution/

https://plumelabs.com/en/

2009

2018

2016

2019

https://www.itri.org.tw/eng/

“Simple sensors perform best whenpollution levels are high and


Un

iva

riate

resp

on

se

Gas 1Gas 2

Gas 3

Common theme forconventional andnanomaterials


>> Radislav Potyrailo: So, if single-output sensors respond with different types ofdesigns, then their nonselective responses will be observed, regardless of whether abig battery is needed to carry it or whether it is miniaturized. As you can see on thisslide, single-output sensors can have variable formats. This challenge of not recognizingdifferent types of gases still remains, due to the design of these types of sensors.

21

CO

conc. by s

ensor(p

pb)

CO conc. by reference (ppb)

Hagler et al., Atmospheric Meas. Tech. 2016, 9, 5281-5292 Peterson et al., Sensors 2017, 17, 1653Bart et al., Environ. Sci. Technol. 2014, 48, 3970−3977

Ozone O3 Carbon monoxide CO Nitrogen dioxideNO2

Gas cross-sensitivity of sensors: what does it mean?

Un

iva

ria

tere

sp

on

se

Gas1Gas2

Gas3

Common theme forconventional and nanomaterials

For gas sensors revolution to take off, accuracy must improve


>> Radislav Potyrailo: For end-use applications, people are really interested to see howaccurately, or how reliably, they can detect pollutants in ambient air and whether theycan use different types of sensors for those reliable measurements. Ozone, carbonmonoxide, and nitrogen dioxide are examples of pollutants in ambient outdoor air. Youcan see here the correlation between the reference instrument and the sensors, whichwere recently reported in the literature. You can see that if these data points line upperfectly on the line with the slope of one, then that will be a perfect correlation. Ifthey don't line up as well, then it will be a less perfect correlation. So depending on theend-use scenario, some of the correlations, even if they're not perfect, may or may notbe allowed. It really depends on the end‐use scenario.

You can see from these two quotes from Nature that the end users are saying thatinterfering chemicals are really adding to uncertainties in our measurements,something that one of them calls the “biggest headaches.” What it means for us is thatwe need to pay attention to how we deal with this performance in the field.

22

Work required to meet monitoring quality criteria per specified directives

(e.g. EU Air Quality Directive 2008/50/EC,others)

Path forward through innovations in sensor systems designs:

Sensing materials, filter materials, sensor excitation, dataprocessing

End-user perspective

of low-cost sensors for outdoor airpollution monitoring

Ozone O3 Carbon monoxide CO Nitrogen dioxide NO2

Fie

ldL

abo

rato

ry

A.C.Rai et al.,End-userperspective of low-cost sensors for outdoorair pollution monitoring, Science of theTotalEnvironment2017, 607–608, 691–705


>> Radislav Potyrailo: This slide shows a summary from European work, whereresearchers were doing measurements of ozone, carbon monoxide, and nitrogendioxide. But the researchers were doing calibrations in the lab (yellow regions) and inthe field (blue regions). In the lab, when they were calibrating, then the linear fit quality(r^2) was always almost one: r^2 = 1 is the best quality. You see that no matter whatthe gas was, the quality of that correlation in the lab was perfect. When thoseresearchers were in the field, then the challenges started to show up very clearly. Thecorrelation dropped quite significantly, and depending on the type of sensor, some ofthem were more affected than others. What this means is that that we, as a communityof sensor developers, need to think of how to improve this type of sensor performanceso that it is as close to the required correlation as appropriate.

23

Inspiration from physiological wearable sensors

Fitness WearablesLack Accuracy

C.Van Hoof, EE Times, 2014

More accurate readings without the goop

Y. Zhu, NC State Engineering

FALL/WINTER, 2015

http://www.eetimes.com/author.asp

?section_id=36&doc_id=1321976

https://news.engr.ncsu.edu/2015/10/more-

accurate-readings-without-the-goop/

2018

https://www.forbes.com/sites/jeanbaptiste/2018/09/14/ apple-watch-4-is-now-an-fda-class-2-medical-device-

detects-falls-irregular-heart-rhythm/#3513aae82071

FreeStyle Libre Flash

Fokkert et. al.,

BMJOpen

Diabetes

Researchand

Care 2017,5,

art.e000320

Earbud HR VSECG

LeBoeuf etal.,

Med Sci

Sports Exerc.

2014,46,

1046–1052

ECG heart rate (bpm)

Earb

ud

heart

rate

(bp

m)

0 2000

200

For The Wearable Revolution To Take Off,Accuracy Must Improve

CVC, Inside Activity Tracking,2013

http://www.insideactivitytracking.com/tracking-

accurately-how-is-it-done-why-is-it-difficult/

The wearable revolutiondid take off once accuracy has been improved


>> Radislav Potyrailo: Many inspirations come from adjacent fields. For researchersdeveloping different types of sensors, part of that inspiration has come fromphysiological wearable sensors. Several years ago, when the first wearable sensorswere introduced, there was also a fashion statement. But then, people started toconfess (in their reports) that the expected “revolution” from these wearable sensorscouldn’t take off because their accuracy wasn’t good enough. This means that theaccuracy, or the correlation plot between the reference measurements and the sensormeasurements, was “sprayed.” So, researchers showed that if the accuracy, or theperformance, of these sensors needed to become as good as hospital equipment orFDA-approved equipment. Then, they developed wearable sensors with nicecorrelation plots, and more and more of those wearables were produced. That’s whenthe “revolution” took off because the accuracy improved significantly, and the endusers were satisfied.

24

Systems with multiple analytical detectors

http://eis-me.com https://uk-air.defra.gov.uk

Mass spectrometry Laser spectroscopy

https://danvillesanramon.com

Gas chromatography

Qin, Gianchandani, Microsyst.Nanoeng. 2016 May 28, 2018 https://alliedscientificpro.com

Traditional analytical instruments:

Eliminated gas cross-sensitivityproblem by design

Complex Chemical

mix

Response

#1

Gas 1

Gas 2

Gas 3

http://www.yalescientific.org

Port

able

IR

Refe

rence

GC

-MS

Anesthetics in breath

Acetone in breath

Reference GC

Ref. 1 Ref.2

Fie

ld P

ort

able

CG

-MS

Environmental

Excellent progress in miniaturization, accuracy

Ref. 1: Hendrickx et al., BMC Anesthesiol. 2008, 8,2

Ref. 2: Sun et al., Rev. Sci. Instrum. 2015, 86, 095003

Ref. 3: EPA Environm. Techn. Verification Report 1997

Ref. 4: Lerner et al., Atmos. Meas. Tech., 2017, 10,291–313 CRDS - cavity ringdown spectroscopy

GC-MS - chromatography-mass

spectrometry

CRDS

Ref.3

LaboratoryGC-MS Tunable

Dio

de L

ase

r(p

pb)

Ref.4

LaboratoryGC-MS

Environmental


>> Radislav Potyrailo: This slide shows that by design, there are also other types ofanalytical instruments that have more than one output (on purpose). The reason forthat is that when these instruments are used for analysis of complex mixtures in gasphase, you would like to detect and quantify multiple components in that mixture.Then, by design, you have more than one output. On the left side of the slide areexamples of different instruments. They can be big, but many of them are gettingsmaller and smaller. Some examples are gas chromatography (where the independentvariable is retention time), mass spectrometry (where the independent variable is themass-to-charge ratio), or laser spectroscopy (where the independent variable is thewavelength of light). On the right side of the slide are correlation plots, which are quiteimpressive to me. Why? Because they are much tighter, which helps to line up thosedata points along this slope of one. So that's a very impressive progress inminiaturization and keeping the accuracy quite high, even if you use a small instrument.

25

2019 advances in micro-gas chromatography (GC):

Eliminated gas cross-sensitivity problem bydesign

2019 achievements in miniaturization and power reduction

2 nm

Zellers et.al., Anal. Chem. 2019, 91, 4747−4754 omniscent.com/wp-content/uploads/2019/05/Solution-Brief-2100-v2.pdf

40 nm

NEW GC detector based onnanoparticles

Resolutionof multiplevapors

Complex Chemical

mix

Response

#1

Gas 1

Gas 2

Gas 3

http://www.yalescientific.org


>> Radislav Potyrailo: This slide is one of my favorites. This is an example of a wearable,portable, belt-attached gas chromatography system. This very impressive work is fromthe University of Michigan. You can see that the detector is placed on goldnanoparticles to detect different types of volatiles (Gas 1, Gas 2, and Gas 3, top right).The sensor can be in a form factor where it can be a part of a wireless network, and youresolve the different types of volatiles in a very nice way from a complex mixture. That'swhere we currently are in gas chromatography, and that's inspiring for the wholecommunity of analytical science and sensors community.

26

Segment 1:

Understanding and appreciating the Status Quo

1.Gas sensors were invented as single-output devices to detect expected gases at

relatively high concentrations

2.Sensing nanomaterials boost sensor sensitivity and enhance response speed

3. Mature analytical instruments reject interferences by their design principles;

some contemporary fieldable deployments utilize nanomaterial-based detectors

Top three learnings from Segment 1


>> Radislav Potyrailo: This is what we learned from Segment 1: First, we understandthat gas sensors were, by design, invented as single-output devices (they detectexpected gases). Second, we also realized, from examples that I showed you, thatsensing nanomaterials improve sensor sensitivity and improve response speed. Third,we recognized that there are other types of analytical instruments that are based ondesign principle, where you have more than one output. Those were examples that Ishowed you on the last two slides; I also showed you an example of a sensor from thisyear, indicating that sensors are getting smaller and smaller, and that's a veryencouraging direction.

27

Learnings







(Webinar Segment 4)


[There is no text for this slide].

28

Conventional single-output

sensor

Adding independent variables into sensor readout

Sin

gle

-ou

tpu

tres

po

nse

Independent variable


>> Radislav Potyrailo: Now, let’s look at how can we improve the state of the art ofexisting sensors. We need to recap that we have individual sensors that have singleoutput, and if we add independent variables, then we may be able to get moreinformation from the sensor designs.

29


sensor


Sin

gle

-ou

tpu

tre

spo

nse

Independent variablePublic Copyright © 2019 GE All rights reserved

>> Radislav Potyrailo: If I stretch this line slightly this way, then it opens up a newdimension – literally a new x-axis. Now, you can see what you have been missing!

30

Gas sensor arrays for selectivityboost

Electrical

MechanicalOptica

l

Gravimetric

1

Dickson et al., IEEE Symp.Circ. Syst.2000

324-sensor array

Beccherelli et al., Sens. Actuat. B 2010

65,536-sensor array

Persaud & Dodd, Nature 1982

First sensor array

Time

Adding independent variable = individual elements in sensor array


>> Radislav Potyrailo: Adding this independent variable, or dimension, is an approachthat was introduced by Krishna Persaud in 1982. This approach consists of addingdifferent types of sensing elements, and they are indeed giving you this independentvariable. These sensing elements are individual elements in the sensor array. Then, youcan do your data analytics and get the response that, schematically, is the same as frommore sophisticated instruments that we discussed in Segment 1.

From examples from the literature, you can see sensor arrays with 65,000 elements.We are part of the sensor array community. We have been doing a lot of work in sensorarrays. A synonym for that is “electronic nose,” because when you are doing dataprocessing and are mimicking an electronic nose’s performance.

31

https://airsense.com/en/products/

portable-electronic-nose

Metal oxides Nanofiber

s

http://www.vaporsens.com/products/

Examples of modern gas sensor arrays: 2019

https://dooclip.me/UN6sncfybic.html

OMNI SENTIR [email protected]

Metal oxides

http://n5sensors.com/

Semiconductor nanostructures


>> Radislav Potyrailo: Here are a few examples of modern gas sensor arrays (which Itook from the internet) from different companies.

32

Tools for data analysis of multivariable sensors:

Machine learning (ML), data analytics, multivariatestatistics

Examples of GE ML tools

• Support Vector Machines (SVM)

• Principal component analysis (PCA)

• Hierarchical cluster analysis (HCA)

• Discriminant Analysis (DA)

• Artificial Neural Network (ANN)

• Independent Component Analysis (ICA)

• Partial least squares (PLS) regression

• Principal Component Regression (PCR)

• New tools for boosting sensor stability

R. A. Potyrailo, Multivariable sensors for ubiquitous monitoring of gases

in

the era of Internet of Things and Industrial Internet,

Chem. Rev. 2016, 116, 11877–11923

Andreu-Perez et al.,

IEEE Trans. Biomed. Eng. 2015

Importance of response dispersion

in a sensorarrayfor reliable performance


>> Radislav Potyrailo: Data analysis is a very important aspect of getting thatinformation from individual elements. On the left of the slide, you can see a very nicegraph from the literature that summarizes different types of analytical tools that arebeing used. There are synonyms for these analytical tools: chemometrics, dataanalytics, multivariate statistics, and machine learning (the most recent synonym). Onthe right of the slide, I show an almost complete list of the types of tools that we areusing. We are developing new tools, as well. The bottom line here (bottom of the slide)is that to have this dimensionality as big as appropriate, or as big as you can, in order toget that selectivity line, you need response dispersion in a sensor array.

33

All rights reserved

Prominent examples of sensor arrays:

Breath analysis, humans detection

Detection of entrapped humans with nanostructured metal-oxide sensors

Ref.1

Ligand-capped Au NPs for lung cancer biomarkers

Ref.2

Nanostructured Pt-, Si-, Pd-, Ti-doped SnO2 films for lung cancer screening

Re f. 3

Ref. 3: Guntner et al., ACS Sens. 2016, 1, 528−535

Ref. 4: Guntner et al., Anal. Chem. 2018, 90,4940−4945

Ref. 1: Haick & co-workers, Nano Lett. 2008, 8, 3631-3635Ref. 2: Haick & co-workers, Nature Nanotechnol. 2009, 4, 669-673

Public Copyright © 2019 GE

PC

2

PC

2

PC1 PC1

SWCNTs with organicmaterials for lung cancer biomarkers

0 %RH 80 %RH

Ref.4


>> Radislav Potyrailo: From the literature, you can see very impressive examples ofanalyses of volatiles in complex mixtures and in the presence of high levels of humiditywith carbon nanotubes, ligand-functionalized gold nanoparticles, and nano‐structuredmaterials of different kinds. These are very impressive recent results.

34

Prominent examples of sensor arrays:

Room temperature applications for lowpower

20 CNTs for food classification

Ref.1

Conjugated polymer NPs for explosives and TICS

Ref.2

Ref. 3: Gustafson and Wilmer, ACS Sens. 2019, 4, 1586−1593

Ref. 4: Javey & co-workers, Sci. Adv. 2017, 3,e1602557

Ref. 1: Swager & co-workers, ACS Sens. 2019, 4,2101−2108

Ref. 2: Zhao et al., Anal. Chem. 2018, 90, 4815−4822

PC1

PC

2

Chemical-sensitive FET based on 3.5-nm-thin Si channel transistors

H2S, H2, NO2

Ref.4

Metal-organic frameworks for methane sensing

Ref.3


>> Radislav Potyrailo: Here, I am showing examples from recent results with carbonnanotubes, conjugated polymer nanoparticles, metal-organic frameworks, and othertypes of structures that are showing progress in this area, which is quite significant.

35

Long term instabilitiescan increase prediction error of sensor

array

Potyrailo Chem. Rev.2016

Uncorrelated drift of individual elements in the array

reduces accuracy of performance of sensor array system


>> Radislav Potyrailo: As sensors were developed in the 1930s as individual sensors,and as sensor arrays were introduced in 1982, then, little by little, we, as a community,started to learn that these individual sensing elements gracefully age, independentlyfrom each other, because, by design, they are slightly different. That's why, over time,we lose the prediction accuracy using a sensor array. And we, as the community, areworking hard on this challenging problem.

36

Long term instabilities

can increase prediction error of sensor array

“long-term drift”

Uncorrelated drift of individual elements in the array

reduces accuracy of performance of sensor array system

“need to maintain robust

responses over extended

time periods”

“the efforts ….

are disappointing”

Trends in Biotechnology 1998,

16, 250-258

Chemical Reviews 2000,

100, 2595-2626

Chemical Reviews2008,

108, 705-725

“the environment effectless

e-nose is urgently desired

but still challenging”

Advanced Materials

Technologies 2019,4, 1800488

Potyrailo Chem. Rev.2016FIRST SENSOR ARRAYNature 1982


>> Radislav Potyrailo: This slide recaps that since the development of the first sensorarray in 1982, we started to work on removing the effects of long‐term drift. You cansee quotes from different review articles from 1998 to 2019. All of this is work inprogress, and we need to refocus, perhaps, and use less explored methods in reducingthe drift. The reason I'm saying “less explored” is because, we, as a community, haveexplored these analytical tools quite well. So, my personal opinion (and that of others)is that we need to think about this situation even further.

37


sensor


Sin

gle

-ou

tpu

tres

po

nse

Independent variable

Only sensor arrays?

What other methodscan be applied to achieve

independent responses?


>> Radislav Potyrailo: If I look at slide 34 again (Mona Lisa) (and I'm paying attention tothe x-axis because it says “independent variable”), if I move a little bit outside of mycomfort zone – such as individual elements in a sensor array – then I will think, “Whatother types of changes can I induce?” That's that punch line. What we see is that sincethe development of the first sensors and sensor arrays, perhaps we need to switch tonew concepts in getting that data out (or data acquisition) through excitation of thesensors, in order to get more stability.

38

Time

Perf

orm

ance

Archives of US ArmyECBC

1914 1950-70s 1982

Individual sensing materials

Sensing arrays of semiselective

sensing materials

Human“sensors”

2007

Multivariable sensors

Bielanski et al., Electric conductivity and

catalyticactivity of semiconductingoxide

catalysts, Nature 1957, 179,668-669

Persaud et al, Analysis of discrimination

mechanisms in the mammalian olfactory system

using a model nose, Nature 1982, 299, 352-355

Potyrailo et al., Morpho Butterfly Wing Scales

DemonstrateHighlySelectiveVapourResponse,

Nature Photonics, 2007, 1,123-128

New sensors

Imagination drives technology:

impact of new concepts onperformance


>> Radislav Potyrailo: This slide summarizes the timeline. You will note that beforesensors were invented, people were doing measurements with humans, mammals, andbirds. So we are advancing this knowledge.

39

Segment 2:

Breaking through the philosophy of conventional sensors

1. Sensor arrays have one independentvariable such as elements in the array

2. Sensing nanomaterials have tunable selectivity for determinations of different

gases

3. Users request to improve stability of sensor arrays for their broadacceptance



>> Radislav Potyrailo: Segment 2 is showing the following: First, sensor arrays have oneindependent variable, such as elements in the array, which really helps indiscriminating between different types of volatiles and rejecting interferences; second,sensing nanomaterials have a lot of useful additions to the capabilities of the sensorarrays; and third, what we saw from those quotes from different review articles is thatusers request stability improvements. So, we, as a sensor community, are focusing onthat.

40

Learnings







(Webinar Segment 4)


>> Radislav Potyrailo: Segment 3 is: How can we improve this situation? We recognizethe capabilities of mature analytical technologies, which we discussed in Segment 1.Also, we recognize how arrays and individual sensors operate.

41

Information Not decoded by components

Conventional sensors

Mature technologies

Multivariable sensors

Complex chemical

mix

1

Resp

onse

Analyte 2

Analyte Analyte 3

Information decoded by components R

esp

on

se

#1

Analyte 1

Analyte 2

Analyte 3

Resp

on

se

#1

Analyte 1

Analyte 2

Analyte 3Information decoded by components

Requirements and opportunities forsensors

Synonyms for multivariable sensors: Intelligent sensors [1], multiparameter sensors [2], High-order

sensors [3], multidimensional signature sensors [4], virtual multisensor system [5], virtual sensor array [6]1 Amrani, M. E. H.; Dowdeswell, R. M.; Payne, P. A.; Persaud, K. An Intelligent Gas Sensing System. Sens. Actuators, B 1997, 44, 512−516.2 Torsi, L.; Dodabalapur, A.; Sabbatini, L.; Zambonin, P. G. Multi-Parameter Gas Sensors Based on Organic Thin-Film-Transistors. Sens. Actuators, B 2000, 67, 312−316.3 Hierlemann, A.; Gutierrez-Osuna, R. Higher-Order Chemical Sensing. Chem. Rev. 2008, 108, 563−613.4 Nomani, M. W. K.; Kersey, D.; James, J.; Diwan, D.; Vogt, T.; Webb, R. A.; Koley, G. Highly Sensitive and Multidimensional Detection of NO2 Using In2O3 Thin Films. Sens. Actuators, B 2011, 160, 251−259.5 Schutze, A.; Gramm, A.; Ruhl, T. Identification of Organic Solvents by a Virtual Multisensor System withHierarchical Classification. IEEE Sens. J. 2004, 4, 857−863.6 Ankara, Z.; Kammerer, T.; Gramm, A.; Schutze, A. Low Power Virtual Sensor ArrayBased on a MicromachinedGasSensor for Fast DiscriminationbetweenH2, CO and RelativeHumidity. Sens. Actuators, B 2004, 100, 240−245.


Multivariable sensor:

sensor with several outputs to detect diverse properties of ambient

environment

>> Radislav Potyrailo: Now, we are able to develop single sensors but with multipleoutputs, also called multivariable sensors. These are sensors with several outputs todetect diverse properties of ambient environment. Different synonyms have beendeveloped over the years for these types of sensors, and I am highlighting them on thisSlide, as well (bottom of the slide).

42

Design principles of multivariable gas sensors

and opportunities for nanomaterials andnanostructures

Sensing nanomaterial with independentresponse mechanisms to different gases

Physical multivariable transducer with several independent outputs

Excitation/modulation conditions ofsensing material and / or transducer

Examples of fabricated multivariable nanosensors

Opportunities of nanotechnology over conventional designs

Nanomaterials: design for independent response mechanisms to different volatiles

Nanostructured transducers: design for enhanced stability

Anticipated risks: fabrication cost


>> Radislav Potyrailo: I would like to emphasize that there is nothing magical about thissensor design. We need to learn, or need to apply our learning, on how to developthese sensors to correct remaining things that need to be corrected before they wouldbe accepted by users. First, nanomaterials should have independent responsemechanisms to different gases. That's relatively easy to do, and that will help us bigtime. Then, our physical transducer should be excited or measured in such a way as todetect these changes in the materials. Also, we should pay more attention, perhaps, tohow we will excite and modulate our physical transducer. Overall, that will give us theopportunity to improve the performance of our sensors. Here, I'm highlightingopportunities of nanomaterials and nanostructures over conventional designs to solveremaining challenges, as well.

43

Monolayer-protected metal nanoparticles:electron tunneling between metal cores and chargehopping

along the atoms of ligandshell

Zeolites:molecular discrimination by size and shape

Surpamolecular materials:molecular recognition based on complexation interactions of

gases in due to the presence of organic hosts with enforced

cavities

Metalloporphyrins and related

macrocycles:hydrogen bonding, polarization, polarity interactions, metal

center coordination interactions and molecular

arrangements

Metal-organic frameworks:van der Waals interactions of the framework surface,

coordination to the central metal ion, and hydrogenbonding

of the framework surface

Dielectric polymers:dispersion, polarizability, dipolarity, basicity, acidity,

and hydrogen bonding

Conjugated polymers:density and charge carrier mobility, swelling, conformation

transitions of chains

Carbon nanoparticle-filled polymers:Swelling

Fullerenes:dispersion, dipolarity/polarizability, and hydrogen-bond

acidity

Carbon nanotubes & graphene:charge transfer and polarization of molecular adsorbates,

gas-induced Schottky barrier modulation at contacts

Semiconducting metal oxides:adsorption/desorption, reduction/reoxidation, bulk effects

Vapor-response mechanisms:

conventional and nanomaterials

Potyrailo et al., Chem. Rev. 2011

Potyrailo, Naik, Annu. Rev. Mater. Res.2013


>> Radislav Potyrailo: The reason I was saying it is relatively easy to think about thesedifferent response mechanisms is because we already have a lot of sensing materialsthat we have been using for some time – conventional ones and nanomaterials. Now,with our new learning, we are seeing what types of response mechanisms sensingmaterials may have and how to take advantage of these diverse response mechanisms.Then, once we know what to expect, we can have a transducer that will pick up thosedifferent responses.

44

Electromagnetic multivariable nanosensors:

New philosophy for selective gas sensing

Resp

onse

Gas 3Gas 1

Gas 2M

ult

ivari

ate

resp

onse

#1

Gas 1

Gas 3

Gas 2

Err

or

of pre

dic

ted g

as

level

Radiofrequencies

Microwave frequencies

Optical frequencies


Sensor arrays

Contemporary needs

•Gas selectivity•Low power•Unobtrusive

Multivariable sensor

Sensor drift (%)

>> Radislav Potyrailo: As an example, on this slide, I'm showing that we can visualizethe different types of multivariable sensors on the electromagnetic spectrum. We canhave sensors that are operating at radio frequencies (resonant impedance sensors ornormal impedance sensors), microwave frequencies, and optical frequencies (differenttypes of resonant and non-resonant optical structures). The goal is the same as forsensor arrays: to put the responses of different gases into different directions after dataanalysis. Also, what we do more and more is to have our individual sensors as stable (asfresh) as possible when they are slightly aged and when there is a significant sensordrift (which we are inducing on purpose in our labs).

45


0

-20

-40

-60

-80

-100De

lta

Fp

(k

Hz

)

Potyrailo Chem. Rev. 2016

Conc. of vapors: 0.089, 0.178, 0.267, 0.356P/Po

Raw dynamic

responses

De

lta

F

1(k

Hz

)D

elt

a F

2(k

Hz

)

-500 100 200 300 400 500

De

lta

Z

p(O

hm

)

Time(min)

1 2 3 4 5 6 7 8 9 10 vapors

3-D sensor dispersion in response to 10vaporsP

C3

1

Blank

2

3 4

5

6

8

7

9

10

Vapors 1 – 10: 200watermethanol 100

ethanol1-propanol 0

1-butanol1-pentanol -100

1-hexanol -2001-heptanol1-octanol, 0

1-nonanol -50

-100

-150

-200

-250

-300

1251007550250

-25

PCA scores plot

Thiols – Au NPs

Frequency

Fp

Zp

F1

F2

Z1Z2

Capacitive reactance

Inductive reactance

Zre

(Ω)

Zim(Ω)

>> Radislav Potyrailo: As an example, here, I am showing that we can have three‐dimensional sensor responses after multivariate analysis when we are looking at theresonant properties of a sensor that is coated with gold nanoparticles and when we areexposing this type of sensor to different types of volatiles (here, alcohols from thesame family of linear alcohols, and water vapor as interference). You can see that whenwe are exposing the sensor to full concentrations of each of these volatiles, theresponse of the peak shift is having the profile shown on the top graph, the F1 an F2responses (second and third plot from the top, right side of the slide). These are theresonant and anti‐resonant frequencies of the imaginary part. They have a differentresponse profile. And then, the big height change of the real part have even furtherdifferent profiles. So this pattern gives us the ability to discriminate between these 10vapors quite nicely. These are the individual vapors in the experiment.

46

Detection of vapors of similar dielectric constantr

Exploring the role of biological molecules for gas-phase detection

Potyrailo et al., Analyst2013

150

100

50

0

-50

-100

-150

-200

-250

Fp

F1

F2Fre

qu

en

cy

S

hif

t

(kH

z)

0 10 20 30 40 50 60 70

Time (min)

-1.5

-1

-0.5

0

0.5

1

Zp

Z1

Z2

Imp

ed

an

ce

S

hif

t(O

hm

)

0 10 20 30 40 50 60 70

Time (min)

ACN DCM MeS

PC1

PC

2

Blank

DCM = dichloromethane

MeS = methyl salicylate

ACN = acetonitrile

Concentrations of vapors: 0.044 and 0.089 P/P0

Water vapor background: 0.18P/P0

Peptides –Au NPs

MeS

r=9.0

ACN

r=37.5

DCM

r=9.1


>> Radislav Potyrailo: The next example shows that if we use peptide-coated goldnanoparticles (shown on the left of the slide), then we can recognize volatiles of verysimilar dielectric constants. Very often, the plots that are showing this recognition arethe scores plots of the principal component analysis (PCA), which is the unsupervisedtool of machine learning that is very often used for analysis of sensor arrays andindividual sensors (which you will see more and more).

47

Analyte

0

50

75 %RH

14

12

10

8

6

4

2

0

-2

1

0

-1

-20

500 InterferenceTime (min)

PC

1(9

0.1

8%

)1-nonanol:

3.5, 5.3, 7.0 ppm

Interference rejection at 2,000,000 fold

Potyrailo, Chem. Soc. Rev. 2017

Thiols – Au NPs


Mu

ltiva

riate

resp

on

se #

1

G a s 1

G a s 3

G a s 2

>> Radislav Potyrailo: We are showing here an example of rejection of interference. Wehave learned how to develop a transducer that will push the response to interference inone plane and the response to our analyte in another plane. So, the detection limit forthis model analyte, in relation to a relatively high humidity of 75%, gave us thatrejection ratio of two billionfold.

48

1 2 3 4 5 6 7

0.1575

60

4

1 Blank3

-0.15

220

-2-4-20

PC22

PC

3

7

Blank

0.062

1

05

4

3-2-0.06

60

0.150 2

-0.15

PC

4

Sensing of complexaromas

Potyrailo, Chem. Soc. Rev. 2017

Teas:(b) Blank ambient air

(1) Robert Roberts Blackcurrant

(2) Dilmah Moroccan Mint Green

(3) Bigelow Green With Lemon

(4) Dilmah Pure Camomile Flowers

(5) Bewley's Pure Rooibos

(6) Numi Organic Jasmine Green

(7) Dilmah Earl Grey

Importance of odor discrimination in medical diagnostics, homeland security

High

dimensionality

(4-D)

of response of

multivariable

sensor

Photo byR. A. Potyrailo

Thiols – Au NPs


>> Radislav Potyrailo: When we have such a prominent sensor, then we can use thistype of system to recognize different complex aromas, as I am showing you in thisgraph. You can see that there are four dimensions for the response of this individualsensor. From an optical perspective, there are nice solutions for developing suchmultivariable sensors with optical-detection principles.

49

Chemistry and physics of colorformation:

Applications for multivariable photonicsensors

2 nm

All photosby GE ResearchPublic Copyright © 2019 GE All rights reserved

>> Radislav Potyrailo: On this slide, I am showing on the left an example of a chemicalresponse: This is an organic dye that one of our students was breathing on duringScience Day. On the right, I am showing examples of physical responses, whereiridescence of nanostructures – either natural or nanofabricated – give color changes orplasmonic changes in nanoparticles.

50

2-D dispersion of multivariable sensors:

Ligand-functionalized plasmonic Aunanoparticles

-25 -20 -15 -10 -5 0

PC1

5 10 15 20-6

-4

-2

0

2

4

6

8

Methyl salicylate

Dmethylformamide

Ethyl acetate

Blank

1.372

Tetrahydrofuran 1.407

1.430

1.501

1.528

Water

Benzene

PC

2

Multivariate analysis

Ab

so

rbance

Wavelength

Principle Sensing film: Au-organothiol NPs

A()vapor N

= f(DN, nN, RN)

A()vapor K

= f(DK, nK, RK)

R()

D

r , n

PC

2

Sensing film: Au-dithianate NPs

Tetrachloro-

ethylene

2-propanol

Butyl acetate

Toluenen-octane

2-butanone

PC1Zhang, Wright, Scholten, Fan, Zellers, Proc. Transducers ’15, 2015, 1448–1451

2 nm

Vapor

40 nm

Sensor selectivity is based on interparticle spacing, dielectric constant, refractive index, and film reflectivityPublic Copyright © 2019 GE All rights reserved

>> Radislav Potyrailo: Talking about plasmonic changes, this slide shows examples ofnot only color changes (which have been noticed a long time ago), but alsodiscrimination between different types of volatiles (when we look at the full spectrum),which we have been showing in our labs. The University of Michigan showed very nicework of these nanoparticles, as well.

51

-10

0

10-10 -5 0 5

-3

-20

-2

-1

0

1

Fa

cto

r3

Acetonitrile

Toluene

Wate

r

Dichloromethane

Bla

nk

Sensor selectivity is based on optical lattice constant of colloidal crystalwith cores and shells of nanospheres responding to diverse vapors

500 nm2 m

R. A. Potyrailo, Z. Ding, M. D. Butts, S. E. Genovese, T. Deng, IEEE Sens. J., 2008, 8,815–822.

Photonic colloidal crystal gas sensors

for 3-D sensordispersion


>> Radislav Potyrailo: You can have more significant dispersion, such as three‐dimensional dispersion, if you have colloidal crystals arranged for gas sensing. On thisslide, I show an example of three‐dimensional dispersion using a colloidal crystal cell.

52

Natural photonic nanostructures

for 3-D and 4-Ddispersion

1cm 100 um

Structural colors in nature: unique path for selective sensing

500 nm

Polar

Non-polar

Chem

ical pola

rity

gra

die

nt

Vapors

in gas phase

Adsorbedvapors

NP-

P-

P-

P-NP-P-

NP-

NP-

NP-

Potyrailo et al.

NaturePhotonics

2007

Unusually high vapor-response selectivity

Potyrailo etal.

Proc.Natl.Acad.Sci.USA

2013

Origin: surface polarity gradient

All Photos by

GEResearch


>> Radislav Potyrailo: The biggest success that we have obtained, in terms ofdispersion, was when we were using photonic structures from iridescent butterflies.What we saw initially was that under an optical or electronic microscope, thesestructures were nanostructures of periodic lamellae. These nanostructures arearranged on ridges, which helps in getting the iridescence. When we expose thesestructures to different types of vapors, they selectively respond to different volatiles indifferent optical domains.

53

Design rules for selectivity control:•Spatial orientation of surfacefunctionalization•Chemistry of surfacefunctionalization•Extinction and scattering ofnanostructure

Design of bio-inspired multi-gas sensors

Broadband excitation

Broadband excitationMixture

of vaporsMixture

of vapors

Potyrailo et al. Nature Communications 2015

E-beam lithography

Conventional lithography

400 Wavelength (nm) 550

Spectr

alre

spon

se

900600 Wavelength (nm)

Spectr

alre

spon

se


>> Radislav Potyrailo: We have learned that the selectivity comes from the chemicalgradient, or polarity, along the height of this nanostructure, which helped us big time todevelop design rules for our engineered sensors. On the left side of the slide, I amshowing the spectrum of a natural butterfly (left) and the spectrum of ournanofabricated structures (right), which we are making with different tools (right sideof the slide), such as conventional lithography (top) and electron‐beam lithography(bottom). At the bottom of the slide, I outline the design rules that we are applying forthe detection of different types of gases.

54

Example of results of exposures tovapors

1 0 3

1 0 4

9 6

1 0 0

1 0 4

9 6

9 8

1 0 0

9 2

1 0 2

4 0 0 5 0 0 8 0 0 9 0 0

1 0 4

1 0 0

1 0 8

1 0 2

1 0 1

1 0 0

1 1 2

9 6

1 1 6

1 1 2

1 0 8

1 0 4

1 0 0

9 61 0 8

6 0 0 7 0 0

Wavelength (nm)

R

R

R

R

R

Ben

ACN

MEK

MeOH

H2O

Differential reflectance spectra

-100

1020

30010

-5

0

5

10

PC

3(1

2.1

%)

ACNBlank

Ben

MeOH

MEK

H2O

-10-20

-30

Ben = benzene ACN = acetonitrile MEK = methyl ethyl ketoneMeOH = methanol H2O = water

Concentration

s of vapors:

0.05, 0.07,

0.09, 0.11

P/P0

Potyrailo et al. Nature Communications 2015


>> Radislav Potyrailo: This slide shows the detection of different types of volatiles witha single sensor. This is done at room temperature.

55

Vapor 1 + H2O

Vapor 2 + H2O

Vapors 1 + 2 + H2O

0.2

0.4

0.6

0V

ap

or 2

flo

w

Binary mixtures

Blank

0

Conc. of methanol (CH3OH), ethanol (CH3CH2OH):0.05, 0.07, 0.09, 0.11 P/P0

Conc. of water (H2O): 0.4 P/P0

PC1

PC

2

Blank

FS nanostructure

PC1 PC1

PC

2

PC

2

Blank

Blank

QCM sensor

array

0.2 0.4 0.6Vapor 1 flow

MOS sensor array

FS = nonafluorohexyl-trimethoxysilane QCM = quartz crystal microbalance MOS = metal oxidesemiconductor

Single photonic nanosensor vs sensorarrays:

Ethanol, methanol vapors, their mixtures + water background

Nanostructured sensor outperformed QCM and MOS sensor arraysPotyrailo et al. Nature Communications 2015


>> Radislav Potyrailo: Because we are part of the electronic nose and sensor arraycommunity, on this slide, we were comparing the behavior of a nanostructured singlesensor with our sensor arrays, such as quartz-crystal microbalance (QCM) andmetal‐oxide semiconductor (MOS) sensor arrays. We saw that our individual sensor wasoutperforming those two popular sensor arrays.

56

Potyrailo et al. Nature Photonics 2007Potyrailo Chem. Soc. Rev. 2017

Advancing design rules of nanostructures for high

temperature gas-sensing applications

Selectivity control for vapors at room temp.

Selectivity control for gases at high temp.

Interference rejection control

•Polymeric nanostructure

•Absorption and adsorption of vapors

•Multi-material inorganic•nanostructure

•Catalytic reactions of gases

•Inorganic nanostructure

•Catalytic reactions of gases

250 nm


>> Radislav Potyrailo: We also moved to high‐temperature applications, because forfuel cells and other types of applications, it is important to have sensors operating athigh temperature. We are doing this work with our collaborators from SUNYPolytechnic Institute.

57

Sensor stability boost:

using new machine learning (ML) tools

Time

Pre

dic

ted

conc.

Pre

dic

ted

conc.

New ML

Conventional ML

Classic vs GE ML to predict sensor response in the presence of drift

Potyrailo et al, NETL-DOE SOFC Project Review Meeting 2018


>> Radislav Potyrailo: The important aspect that I would like to show is that when weare developing our sensors and calibrating them using conventional machine-learning(ML) tools, in the presence of drift, we lose our quality of prediction. But when we usethe sensor design in combination with new ML tools, then we keep the quality.

58

Segment 3:

Breaking through the philosophy of conventional sensors

1. Multivariable sensors is the natural next phase driven by progress in

sensors science andtechnology

2. Demonstrated multivariable sensors have 2-D, 3-D, 4-D dispersion

3. Reduction/elimination of drift in multivariable sensors by focused sensor

design and simple data analytics



>> Radislav Potyrailo: So, this is what we learned from Segment 3: First, there isanother way of getting the signals out from individual sensors when you have morethan one output, and we're calling this type of sensors “multivariable sensors.” Second,we are developing multivariable sensors that have two-dimensional, three-dimensional,and four- dimensional dispersion. Third, by focused sensor design and simple dataanalytics, we can reduce or eliminate the drift.

59

Learnings







(Webinar Segment 4)


[There is no text for this slide.]

60

http://n5sensors.com/

Rockville, MD

Semiconductor nanostructures

http://www.vaporsens.com/products/

Salt Lake City, UT

Nanofibers

Gas nanosensors:

Examples of today’s commercialofferings

https://www.brewerscience.com

100 nm

Rolla, MO

Carbon nanotubes


>> Radislav Potyrailo: To conclude, I would like to show you that there are successfulcompanies out there, in the U.S., for example. These companies are commercializingdifferent types of gas sensors with nanomaterials quite successfully, which is veryinspiring for us, as a research community. If we are looking forward into the future,what type of roadmap is in front of us, and what type of things are we focusing on?

61

Gas sensors:

Driving forces, evolution and 2060 innovationsroadmap

Materials

Transducers

Microsystems

Analytics

Polymers

Sensorarrays

Multivariablesensors

Conjugated Ligand-functionaAlized Bio-inspiredpolymers nanoparticles nanostructures

Resistors Capacitors

Individual single-output

sensors

Integratedelectronics

Metal Colorimetric Carbonoxides formulations nanotubes Graphene

Ionic liquids

Artificial Artificialnucleic acids catalysts

“Odor” binding Supramolecularartificial proteins materials

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Neuromorphic systems

Fluorescent

Acoustic Work Field-effect resonators function transistors

Multivariable photonic

Multivariable electrical

Colorimetric

PellistorsNano-

pellistors

Multi-parameter electromagnetic

Artificial moths antenna

Direct-indirect electromagnetic

Ubiquitous sensor networks

Multivariate statistics

Univariate statistics

Machine learning:- multi-gas quantitation- known interferences

Auxiliary sensors for correction

Event-based sensors

- all interferences- drift correction- self-calibration

Sensor fusion

Artificial intelligence:- sensing materials design- transduction principledesign- sample manipulation design- power budget design- user-tailored outcome

Fusion of multi-source contextual data

Machine learning:

R. Potyrailo, 256th ACS Meeting 2018


>> Radislav Potyrailo: You can see this roadmap, which includes advances in materials,transducers, microsystems, and data analytics, and, overall, bringing this type ofanalytical devices to new levels. This is an innovation roadmap for 2060.

62

Instruments based on mature analytical concepts

Response #1

Analyte 1

Analyte 3

Analyte 2

Sensors and systems based on multivariable sensing concepts

Photos by R. A. Potyrailo

Summary:

Multivariable sensor solutions for demandingapplications

http://eis-me.com/environmental-monitoring-mobile-station-dm/

https://uk-air.defra.gov.uk/networks/network-info?view=aurn

Qin, Gianchandani, Microsyst. Nanoeng.2016

C&EN, May 28, 2018

• Value of multivariable nanosensors over existing traditional analytical instruments: multi-gas detection and rejection of interferences using individual sensors

• Detection sensitivity: part-per-million → part-per-billion → part-per-trillion levels

• High sensor stability due to reduced number of noise sources

• Simplified device packaging


>> Radislav Potyrailo: In summary, we are not competing, but, with our designs, we arecomplementing the performance of mature analytical instruments. You see that thesecomplementary efforts pay off with the capabilities that we discussed today.

63

Acknowledgments

A.Obi

M. Palacios

M. Pietrzykowski

B.Scherer

A.Shapiro

V.Srivastava

N. Stoffel

R.St-pierre

A.Tang

S. Zhong

Air Force Research Lab

R.Naik

J.Slocik

M. Vasudev

N.Kelley-Loughnane

Avery Dennison

DJ Lee

E.McGinniss

University at Albany

H.Ghiradella

J.Hartley

R.Bonam

M. Carpenter

N.Karker

N.Houlihan

V. VulcanoRossi

University of Exeter

P.Vukusic

T.Starkey

GE Global Research

B.Amm

J.Ashe

J.Brewer

C. Collazo-Davila

T.Deng

S. Go

C. Henderson

J.Iannotti

A.Minnick

M. Nayeri

Y.Lee


>> Radislav Potyrailo: These are my collaborators and funding agencies. Thank you foryour attention.

64

Thank you

Dr. Radislav A. Potyrailo (GE Research,USA) [email protected]


>> Stacey Standridge: Thank you. That was a really interesting and informativepresentation. We had a couple of questions come in, but given the time, I think I willjust pose one question to you before we wrap up for the day. Near the end of yourpresentation, you have a 2060 innovation roadmap, and I was wondering: From yourperspective, what are the top priorities for that roadmap?

>> Radislav Potyrailo: I think the top priorities are basically the ability to think aheadwhat the application scenarios should be for a particular sensor that we're developingand then understand what type of interferences or other types of challenges thissensor will experience. The priority will be to make sure that you are rejecting theinterferences that are important to reject in that application. Another priority is thatyou are focusing on the lifetime of that particular sensor and understanding how tokeep that lifetime as long as appropriate because, depending on the application, it canbe two weeks, but if it is bedside monitoring, it can be five or ten years. So, knowingthose values or numbers up front will give us the priorities. (Continued)

65

Thank you

Dr. Radislav A. Potyrailo (GE Research,USA) [email protected]


>> Stacey Standridge: That makes perfect sense, and I think that's something we hearvery much: It all depends on the case. With that, I want to thank you, Dr. Potyrailo foryour time today, and I want to thank everybody who participated in the webinar. Again,a friendly reminder that we have National Nanotechnology Day on October 9th (for10^-9) next week, and we hope everyone can participate. And please check nano.govfor more updates.

>> Radislav Potyrailo: Thank you, everybody.

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