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Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants Michael S Wiederoder, Eric C Nallon, Matt Weiss, Shannon K McGraw, Vincent P Schnee, Collin J Bright, Michael P Polcha, Randy C Paffenroth, and Joshua R. Uzarski ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.7b00550 • Publication Date (Web): 11 Oct 2017 Downloaded from http://pubs.acs.org on October 12, 2017
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Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants Michael S. Wiederoder1, Eric C. Nallon2,4, Matt Weiss, Shannon K. McGraw1, Vincent P. Schnee 2, Collin J. Bright2, Michael P. Polcha2 , Randy Paffenroth3, Joshua R. Uzarski1* 1
Natick Soldier Research, Development and Engineering Center, United States Army, Natick, Massachusetts 01760, United States 2 Night Vision and Electronic Sensors Directorate, United States Army, Fort Belvoir, Virginia 22060, United States 3 Worcester Polytechnic University, Department of Mathematical Sciences, Worcester, Massachusetts, USA 4 Black Cow Analytics LLC, Charlottesville, VA 22936, United States * Joshua R. Uzarski, e-mail: [email protected], phone: +1-(508)-233-4018
Abstract A cross-reactive array of semi-selective chemiresistive sensors made of polymer-graphene nanoplatelet (GNP) composite coated electrodes was examined for detection and discrimination of chemical warfare agents (CWA). The arrays employ a set of chemically diverse polymers to generate a unique response signature for multiple CWA simulants and background interferents. The developed sensors’ signal remains consistent after repeated exposures to multiple analytes for up to five days with a similar signal magnitude across different replicate sensors with the same polymer-GNP coating. An array of 12 sensors each coated with a different polymer-GNP mixtures was exposed 100 times to a cycle of single analyte vapors consisting of 5 chemically similar CWA simulants and 8 common background interferents. The collected data was vector normalized to reduce concentration dependency, z-scored to account for baseline drift and signal to noise ratio, and Kalman filtered to reduce noise. The processed data was dimensionally reduced with principal component analysis and analyzed with four different machine learning algorithms to evaluate discrimination capabilities. For 5 similarly structured CWA simulants alone 100% classification accuracy was achieved. For all analytes tested 99% classification accuracy was achieved demonstrating the CWA discrimination capabilities of the developed system. The novel sensor fabrication methods and data processing techniques are attractive for development of sensor platforms for discrimination of CWA and other classes of chemical vapors. Keywords: Graphene, Polymer Sensors, Chemical Warfare Agents, Machine Learning, Chemical Discrimination Detecting chemical warfare agents (CWA’s), such as tabun (GA), sarin (GB), soman (GD), and VX, to prevent exposure events is of great interest given previous and possible future use by individuals, terrorist organizations, and state sponsored militaries.1 Current detection technologies include infrared and Raman spectroscopy for remote or standoff monitoring, ion mobility spectrometry, surface acoustic wave sensors, flame photometry, photoionization and electrochemical detection, and carbon nanotube gas ionization sensors for on-site testing.1 Many of these technologies require complex instrumentation, high costs, and expert personnel to operate, diminishing their capabilities in low-resource and remote
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environments. In addition, existing portable sensors are often only responsive to a select number of analytes and perform poorly in the presence of background interferents.1 Thus low-cost, low-power, miniaturized sensors capable of detection and discrimination of a broad range of CWA’s are needed for rapid deployment to mitigate risk of exposure. One method to address this problem is to use an array of semi-selective chemical sensors that respond to many analytes simultaneously, creating a unique analytical signature for detection and classification of multiple analytes using one platform.2 To optimize classification the developed sensor arrays should be designed in a cross-reactive manner with high chemical diversity for differential response to a large set of analytes. The collected data can then be analyzed using machine learning algorithms for subsequent detection and discrimination.3 Many array based sensors utilize commercial polymers because of their low cost and response diversity.4 Each polymer possesses unique physical and chemical properties which affect the adsorption and desorption of vapor molecules.5 The collective response of all the select polymer sensors in response to an analyte provides a unique signature that aids in detection and discrimination. Technologies that have used this principle include mass sensitive polymer coated acoustic wave oscillators6 , colorimetric detection with fluorescent organic polymers7 and quantum dot composites8, and electronic based detection with conductive polymers9 and carbon black composites10. For CWA’s specifically, cross-reactive arrays using surface acoustic wave sensors11, parallel-plate capacitance based sensors12, conductive polymers13, and chemiresistive sensors made of polymer/carbon black composites14 and polymer/carbon nanotube composites15 have been demonstrated. Recent research demonstrates that single element graphene chemical vapor sensors are capable of discriminating multiple vapor compounds with similar structures.16 Graphene is of particular interest for chemical sensing applications due to its 2D structure, where every carbon atom is a surface atom, unlike other conductive carbon materials such as carbon black and multiwall carbon nanotubes.17 This provides the greatest possible surface area per unit volume so electron transport is highly sensitive to adsorbed molecular species.17 Unfortunately, single graphene sensors are limited in their classification capabilities for some similar compounds as their response signatures are not distinguishable using machine learning algorithms.16 To increase response diversity across sensor elements, graphene can be modified using covalent functionalization methods such as the introduction of radicals, nitrenes, carbenes, arynes, and reactive plasma.18 Although these methods can provide the desired diversity, the process to apply multiple functionalities on a monolithic substrate is challenging and difficult to repeat consistently causing significant variations in measured signal across sensing elements. In contrast, it is possible to overcoat a set of unique polymers on planar graphene sensors to increase response diversity for discrimination of single analytes and complex vapors.19 Despite these advantages, adoption of single layer graphene sensors is constrained by limited production volumes and relatively high costs.20 In this study we focus on polymer-graphene nanoplatelet (GNP) composite coatings to create an array of semi-selective chemiresistive sensors to detect and discriminate CWA simulants and common background interferent compounds. GNP’s can be produced in large quantities, overcoming the costs of single layer graphene.20 By using polymer-GNP composites a facile approach to sensor fabrication can be utilized while taking advantage of the response diversity of polymers and the sensing potential of graphene. First multiple individual electrodes coated with the same polymer-GNP composite were evaluated to characterize repeatability of individual sensor response over a period of 5 days and across similarly coated sensors. Next a device containing 12 unique polymer-GNP coated sensors was fabricated to generate a large data set with high response diversity. The device was exposed a total of
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1300 times to 13 analytes to demonstrate discrimination of 5 CWA simulants with similar chemical structures from one another and from 8 dissimilar background interferents. The data is processed using vector normalization to reduce concentration dependence, z-score to normalize baseline drift and represent signal to noise ratio, and a Kalman filter to reduce noise. The processed data was dimensionally reduced using principal component analysis (PCA) before analysis with the machine learning algorithms k-nearest neighbors, support vector classifier, random forest, or linear discrimination analysis to determine classification accuracy. For the four machine learning algorithms used 95-99% classification success for all analytes was achieved demonstrating good discrimination capacities. These examined techniques for sensor fabrication and data analysis are adaptable for development of remote sensing platforms for CWA detection.
Experimental Section Materials - Graphene nanoplatelets (GNP) (Cheap Tubes Inc., Grafton, VT) were acquired with a platelet shape of 1-3 μm in diameter and an overall thickness of approximately 3-10nm. All polymers were acquired from Sigma-Aldrich, St. Louis, MO and their names and properties are listed in Table 1. Diisopropyl methylphosphonate (DIMP) was purchased from Fisher Scientific (Hanover Park, IL). All other solvents and analytes are listed in Table 2 and were purchased from Sigma-Aldrich, St. Louis, MO. RoundUp © (41% aqueous glyphosate, Scotts Company LLC, Marysville, OH), antifreeze, and diesel (Gulf Oil, Bowling Green, VA) were purchased from commercial sources. Sensor Fabrication -The fabrication of the sensor array consisted of simple drop-casting of GNP polymer-composite solutions on pre-fabricated interdigitated electrodes (IDE). First, Si/SiO2 substrates were cleaved into approximately 1.3 cm x 1.3 cm pieces followed by solvent cleaning in acetone, isopropanol, and DI water. Conventional lift-off photolithography was then used to pattern the Si/SiO2 substrate with the IDE design which consists of 24 identical devices, each comprised of 12 electrode fingers with length, width, and separation of 750 μm, 50 μm, and 50 μm, respectively, producing a total sensor area of 0.44 mm2 (Figure 1C). The patterned device was then transferred to a Temescal BJD-1800 electron beam evaporator (Ferrotec, Livermore, CA), where a layer of Ti/Au (25nm/300nm) was deposited once an appropriate base pressure was achieved. Following metal deposition, the device was left in a 100% acetone bath for approximately two hours to achieve metal lift-off. The metal patterned sensors were then rinsed with isopropanol and deionized water and dried with nitrogen. Once the metal deposition process was complete, a layer of SU-8-2005 photoresist (MicroChem, Westborough, MA) was spun onto the device at a thickness of approximately 5 µm. An additional photolithography mask design was used to provide an opening well to each sensor with length, width, and depth of 1300 µm, 1200 µm, and 5 µm, respectively. The device was then hard baked at 150 °C for 5 minutes to create a permanent structure. Lastly, the device was mounted in a 68-pin leadless ceramic chip carrier and each individual sensor wire bonded. The final device can be seen in Figure 1B. Commercial polymers used to create the sensor array are shown in Table 1, along with their abbreviation, respective solvent (if applicable), and molecular weight. The chemical structure of each polymer can be found in supplemental Figure S-1. The polymers were chosen based on previous research of polymer coated single layer graphene sensors.19 Each solution contained 12 mg/mL of polymer and 8 mg/mL of GNP in the solvent specified in Table 1 for a total solids content of 20 mg/mL. All sensor solutions were sonicated after mixing for 30 min to disperse GNP and again immediately before deposition. Sensor fabrication was completed by drop-casting of 0.2 µL of each GNP polymer-
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composite solution into individual IDE wells using a microliter syringe (Hamilton, Reno, NV). Following coating deposition the entire device was heated to 70 °C for 10 min to remove residual solvents. An example sensor is shown in Figure 1D. Table 1: Polymers used to create GNP-Polymer composites for each sensor material. Polymer Abbreviation Solvent Molecular Weight (Da) Polycaprolactone Polyepichlorohydrin Nafion 117 Polyvinyl alcohol Polyisobutylene
PCL PECH Nafion PVA PIB
Chloroform Chloroform Water/alcohols Water Chloroform
14,000 700,00 Unspecified 31,000 500,000
Poly(4-vinylphenol-co-methyl methacrylate)
PVPH-MMA
Tetrahydrofuran
3,000-5,000
Poly(1-vinylpyrrolidone)-graft-(1triacontene)
PVPDY_gt
Chloroform
643
Poly(vinylphosphonic acid) Polyacenaphthylene Polytetrafluoroethylene Poly(ethylene-co-vinyl acetate) Poly(4-vinylphenol)
PVPA PACN PTFE PEVA PVPH
Tetrahydrofuran Chloroform Chloroform
Unspecified 5,000 - 10,0000 Unspecified 55,000 25,000
Chloroform Tetrahydrofuran
Figure 1: A custom vapor generation system was used to infuse test samples through a Teflon gas flow cell (A). Each device contains 24 interdigitated electrode arrays wire-bonded to a leadless chip carrier (B) inserted into the flow cell. The electrode dimensions (C) and an example GNP-Polymer composite coated sensor (D) are shown for reference.
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Instrumentation and Analysis - The vapor generation system and gas flow cell used in these experiments is shown in Figure 1A was described previously.21 Briefly, a 68-pin breakout board was inserted into a custom machined Teflon block to provide a 0.25 mL flow channel to the sensor surface. For each trial, time vs. resistance measurements were collected and divided into three sections, baseline, sample, and recovery periods set at 60s, 30s, and 180s, respectively. The flow rate through the sensor was a constant 40 mL/min controlled by a digital mass flow controller (MKS Instruments, Andover, MA). Flow through the analyte vials was controlled by computer actuated solenoid valves. Vial bubblers containing analyte consisted of an inlet tube with a sparger at the end for bubble creation and Table 2: CWA simulant compounds and interferent compounds used to challenge the GNP-Polymer sensor array with their respective abbreviation, vapor pressure, and concentration used during testing.
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an exit tube leading to the flow cell. During baseline and recovery periods, 100% nitrogen is passed across the sensor while during the response period a ratio of nitrogen and saturated vapor of the analyte (Table 2) is combined before infusion into the flow cell. The device was exposed for 80-100 cycles with each cycle consisting of one trial for each analyte. The final data set contained 80-100 trials for each tested analyte for classification and cross validation. It is assumed a concentration equal to the saturated vapor of each analyte is infused for each trial because the fluid volume within the sealed vial remains constant and the temperature is steady during measurements. The sampling system and sensor were contained in a custom plastic enclosure outfitted with a fume extraction system and experiments were conducted at temperatures ranging between 21.8 and 22.4°C and 63.1 and 67.3% relative humidity. The resistance of each coated electrode was measured using a data acquisition and control cube configured with two 12-channel, fully isolated resistance input boards (United Electronics Industry, Walpole, MA), creating 24 total channels with simultaneous data collection. Each channel was configured to operate in 3-wire mode, simultaneously collecting resistance measurements at a rate of approximately 15 Hz with a current of 50 µA. A custom Python script was used to read a user generated input CSV file specifying the measurement parameters for a single measurement which are: baseline time, sample time, recovery time, active channels, minimum/maximum resistance, and sample flow rate. All post-processing, data analysis, and plotting was performed in Python 3.6 using Numpy, Scikit Learn and Matplotlib libraries. Table 2 lists the CWA simulant and interferent compounds and chemical structures used to challenge the discrimination capability of the sensor array. The CWA simulants (DIMP, DEMP, DMMP, TEP, and TMP) are commonly used as chemical surrogates for G-series CWA.22 The simulants chosen have similar chemical structures (phosphate groups), differing in functional group substitution around the central phosphoryl group. The interferents include common solvents (acetone, ethanol, hexane, and toluene) known to generate a good response for polymer based sensors5b, water, and complex mixtures (antifreeze, diesel, RoundUp) that are examples of background interferents expected in a real-world scenario. The antifreeze is a mixture of ethylene glycol (~90-95%), water and stabilizers, the RoundUp is a mixture of 41% glyphosphate, water, and stabilizers, and the diesel contains diesel and trace amounts of naphthalene. These background interferents provide a realistic stressor to evaluate discrimination capabilities of the sensor arrays.
Results and Discussion Coating formulation –The formulation of the coatings is important to optimize the discrimination capabilities of sensor array. The GNP loading of 40% w/w was chosen based on previous work with carbon black – polymer based vapor sensors that show a linear response vs. analyte exposure which is important for subsequent data processing methods.10b This loading is greater than the 1-5% required for conductivity, according to the manufacturer, resulting in a highly conductive film with a consistent linear sensor response to analyte. The IV curve of the coatings (Figure S5) shows a linear slope over the entire voltage range measured, including at the 50 µA current used for this study, which indicates device resistance. Many studies with sorption based chemical sensors utilize linear solvation energy relationship (LSER) parameters as a theoretical model to optimize polymer selection for sensor arrays.23 In contrast for this study the 12 polymers employed were down selected from a library of 23 polymers reported in a previous study with polymer overcoated graphene sensors.19a The diversity of the molecular weight and chemical structure (Figure S-1) of the selected polymers generates a wide range of analyte sorption rates for each sensor to create a resistance response signature (magnitude and shape) for subsequent classification. The down selected polymers that were chosen display high
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signal response magnitude, represent response diversity as demonstrated by a hierarchical cluster analysis, facilitate even dispersion of GNP’s based on visual analysis (no clumping after sonication), and show a stable signal response over time.19a These parameters are important to enhance discrimination capabilities and sensor repeatability. Sensor Array Response - The sensor response was investigated by measuring the resistance of the coated electrode arrays upon exposure to select analytes from Table 2. Figure 2 shows a subset of 10 trials to demonstrate the signal of sensors composed of PCL and PECH in response to toluene vapor exposure. The measured time series includes three distinct regions of baseline, response, and recovery for each trial with 80 trials total for each analyte. During the baseline period only nitrogen is flowing and the resistance remains flat. In the response section the resistance increases upon initial exposure and the rate of increase diminishes as the analyte absorption rate decreases as a function of time. Finally, during the recovery period the resistance decreases as analyte desorbs at an exponential rate while nitrogen flows over the sensor. The resistance increase results from analyte adsorption into the polymer causing swelling that increases the distance between immobilized GNP’s. In contrast, resistance decreases as analyte desorbs and the coating shrinks bringing the immobilized GNP’s closer together. This phenomena is also observed in other polymer-conductive carbon vapor sensors.14-15 As shown in Figure 2, raw resistance measurements for PCL and PECH upon repeated exposures to toluene exhibit baseline resistance drift over time. In Figure 2 the baseline drift is normalized by subtracting the average baseline resistance from all resistance measurements for each trial, demonstrating the magnitude of signal remains similar across trials. The magnitude and shape of the response for each polymer coating to each analyte is unique and varies as a function of the properties of both. The shape of the curve during both the absorption and desorption has unique properties that yield additional Figure 2: Measured resistance of a single polymer-GNP sensor in response diversity for response to toluene vapor is shown as raw signal and after discrimination.24 baseline subtraction. Each graph contains 10 representative trials Sensor Repeatability- Figure 3 for a single sensor with a PCL or PECH coating during baseline (060 sec), response (60-90 sec), and recovery (90-270 sec) phases. shows the relative maximum The maximum resistance change ∆R is also shown. resistance response (∆R/R), where the maximum resistance
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change (∆R) is divided by the average baseline resistance (R), of single PECH (3A) and PCL (3B) sensors to select analytes. Even when only evaluating one component of the total sensor response, there are clear differences in measured signal between tested analytes. In addition there are differences in signal magnitude Figure 3: The maximum relative resistance response to select analytes over between the two time is shown for individual sensors coated in PCL PECH (A) and PECH PCL (B). polymer coatings, Average relative resistance response of all trials to select analytes for sensors demonstrating a unique response with same polymer is shown with error bars ± 1 SD (C). The maximum and minimum relative maximum resistance response (∆R/R) for PCL and PECH to signature for each each analyte (except water) is shown (D). analyte. For both polymers tested the relative maximum response of the sensor remains consistent after 1040 unique exposures or 80 cycles of 13 different analytes over a period of 5 days. Some differences in response magnitude across trials can be explained by minor variations in analyte concentration due to temperature changes, flow rate fluctuations, analyte exposure history, and baseline drift (Figure 2). To validate sensor reproducibility a device containing multiple electrodes with the same polymer-GNP coating was tested. Replicate coatings of the polymers PCL and PECH were each deposited on four different electrode arrays for testing (Figure 3C). For the evaluated PCL sensors the initial resistance range was 35.0 - 152 Ω and for PECH sensors it was 196 - 660 Ω due to variations in hand spotted drop cast coatings. The range of baseline drift over a 5 day test period for the PCL sensors was 4.7-11.9% and for the PECH sensors it was 1.2-7.2%. For all sensors the ∆R/R for each analyte was statistically significant from a blank sample (nitrogen) using a student’s t-test (p
Article
Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants Michael S Wiederoder, Eric C Nallon, Matt Weiss, Shannon K McGraw, Vincent P Schnee, Collin J Bright, Michael P Polcha, Randy C Paffenroth, and Joshua R. Uzarski ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.7b00550 • Publication Date (Web): 11 Oct 2017 Downloaded from http://pubs.acs.org on October 12, 2017
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Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants Michael S. Wiederoder1, Eric C. Nallon2,4, Matt Weiss, Shannon K. McGraw1, Vincent P. Schnee 2, Collin J. Bright2, Michael P. Polcha2 , Randy Paffenroth3, Joshua R. Uzarski1* 1
Natick Soldier Research, Development and Engineering Center, United States Army, Natick, Massachusetts 01760, United States 2 Night Vision and Electronic Sensors Directorate, United States Army, Fort Belvoir, Virginia 22060, United States 3 Worcester Polytechnic University, Department of Mathematical Sciences, Worcester, Massachusetts, USA 4 Black Cow Analytics LLC, Charlottesville, VA 22936, United States * Joshua R. Uzarski, e-mail: [email protected], phone: +1-(508)-233-4018
Abstract A cross-reactive array of semi-selective chemiresistive sensors made of polymer-graphene nanoplatelet (GNP) composite coated electrodes was examined for detection and discrimination of chemical warfare agents (CWA). The arrays employ a set of chemically diverse polymers to generate a unique response signature for multiple CWA simulants and background interferents. The developed sensors’ signal remains consistent after repeated exposures to multiple analytes for up to five days with a similar signal magnitude across different replicate sensors with the same polymer-GNP coating. An array of 12 sensors each coated with a different polymer-GNP mixtures was exposed 100 times to a cycle of single analyte vapors consisting of 5 chemically similar CWA simulants and 8 common background interferents. The collected data was vector normalized to reduce concentration dependency, z-scored to account for baseline drift and signal to noise ratio, and Kalman filtered to reduce noise. The processed data was dimensionally reduced with principal component analysis and analyzed with four different machine learning algorithms to evaluate discrimination capabilities. For 5 similarly structured CWA simulants alone 100% classification accuracy was achieved. For all analytes tested 99% classification accuracy was achieved demonstrating the CWA discrimination capabilities of the developed system. The novel sensor fabrication methods and data processing techniques are attractive for development of sensor platforms for discrimination of CWA and other classes of chemical vapors. Keywords: Graphene, Polymer Sensors, Chemical Warfare Agents, Machine Learning, Chemical Discrimination Detecting chemical warfare agents (CWA’s), such as tabun (GA), sarin (GB), soman (GD), and VX, to prevent exposure events is of great interest given previous and possible future use by individuals, terrorist organizations, and state sponsored militaries.1 Current detection technologies include infrared and Raman spectroscopy for remote or standoff monitoring, ion mobility spectrometry, surface acoustic wave sensors, flame photometry, photoionization and electrochemical detection, and carbon nanotube gas ionization sensors for on-site testing.1 Many of these technologies require complex instrumentation, high costs, and expert personnel to operate, diminishing their capabilities in low-resource and remote
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environments. In addition, existing portable sensors are often only responsive to a select number of analytes and perform poorly in the presence of background interferents.1 Thus low-cost, low-power, miniaturized sensors capable of detection and discrimination of a broad range of CWA’s are needed for rapid deployment to mitigate risk of exposure. One method to address this problem is to use an array of semi-selective chemical sensors that respond to many analytes simultaneously, creating a unique analytical signature for detection and classification of multiple analytes using one platform.2 To optimize classification the developed sensor arrays should be designed in a cross-reactive manner with high chemical diversity for differential response to a large set of analytes. The collected data can then be analyzed using machine learning algorithms for subsequent detection and discrimination.3 Many array based sensors utilize commercial polymers because of their low cost and response diversity.4 Each polymer possesses unique physical and chemical properties which affect the adsorption and desorption of vapor molecules.5 The collective response of all the select polymer sensors in response to an analyte provides a unique signature that aids in detection and discrimination. Technologies that have used this principle include mass sensitive polymer coated acoustic wave oscillators6 , colorimetric detection with fluorescent organic polymers7 and quantum dot composites8, and electronic based detection with conductive polymers9 and carbon black composites10. For CWA’s specifically, cross-reactive arrays using surface acoustic wave sensors11, parallel-plate capacitance based sensors12, conductive polymers13, and chemiresistive sensors made of polymer/carbon black composites14 and polymer/carbon nanotube composites15 have been demonstrated. Recent research demonstrates that single element graphene chemical vapor sensors are capable of discriminating multiple vapor compounds with similar structures.16 Graphene is of particular interest for chemical sensing applications due to its 2D structure, where every carbon atom is a surface atom, unlike other conductive carbon materials such as carbon black and multiwall carbon nanotubes.17 This provides the greatest possible surface area per unit volume so electron transport is highly sensitive to adsorbed molecular species.17 Unfortunately, single graphene sensors are limited in their classification capabilities for some similar compounds as their response signatures are not distinguishable using machine learning algorithms.16 To increase response diversity across sensor elements, graphene can be modified using covalent functionalization methods such as the introduction of radicals, nitrenes, carbenes, arynes, and reactive plasma.18 Although these methods can provide the desired diversity, the process to apply multiple functionalities on a monolithic substrate is challenging and difficult to repeat consistently causing significant variations in measured signal across sensing elements. In contrast, it is possible to overcoat a set of unique polymers on planar graphene sensors to increase response diversity for discrimination of single analytes and complex vapors.19 Despite these advantages, adoption of single layer graphene sensors is constrained by limited production volumes and relatively high costs.20 In this study we focus on polymer-graphene nanoplatelet (GNP) composite coatings to create an array of semi-selective chemiresistive sensors to detect and discriminate CWA simulants and common background interferent compounds. GNP’s can be produced in large quantities, overcoming the costs of single layer graphene.20 By using polymer-GNP composites a facile approach to sensor fabrication can be utilized while taking advantage of the response diversity of polymers and the sensing potential of graphene. First multiple individual electrodes coated with the same polymer-GNP composite were evaluated to characterize repeatability of individual sensor response over a period of 5 days and across similarly coated sensors. Next a device containing 12 unique polymer-GNP coated sensors was fabricated to generate a large data set with high response diversity. The device was exposed a total of
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1300 times to 13 analytes to demonstrate discrimination of 5 CWA simulants with similar chemical structures from one another and from 8 dissimilar background interferents. The data is processed using vector normalization to reduce concentration dependence, z-score to normalize baseline drift and represent signal to noise ratio, and a Kalman filter to reduce noise. The processed data was dimensionally reduced using principal component analysis (PCA) before analysis with the machine learning algorithms k-nearest neighbors, support vector classifier, random forest, or linear discrimination analysis to determine classification accuracy. For the four machine learning algorithms used 95-99% classification success for all analytes was achieved demonstrating good discrimination capacities. These examined techniques for sensor fabrication and data analysis are adaptable for development of remote sensing platforms for CWA detection.
Experimental Section Materials - Graphene nanoplatelets (GNP) (Cheap Tubes Inc., Grafton, VT) were acquired with a platelet shape of 1-3 μm in diameter and an overall thickness of approximately 3-10nm. All polymers were acquired from Sigma-Aldrich, St. Louis, MO and their names and properties are listed in Table 1. Diisopropyl methylphosphonate (DIMP) was purchased from Fisher Scientific (Hanover Park, IL). All other solvents and analytes are listed in Table 2 and were purchased from Sigma-Aldrich, St. Louis, MO. RoundUp © (41% aqueous glyphosate, Scotts Company LLC, Marysville, OH), antifreeze, and diesel (Gulf Oil, Bowling Green, VA) were purchased from commercial sources. Sensor Fabrication -The fabrication of the sensor array consisted of simple drop-casting of GNP polymer-composite solutions on pre-fabricated interdigitated electrodes (IDE). First, Si/SiO2 substrates were cleaved into approximately 1.3 cm x 1.3 cm pieces followed by solvent cleaning in acetone, isopropanol, and DI water. Conventional lift-off photolithography was then used to pattern the Si/SiO2 substrate with the IDE design which consists of 24 identical devices, each comprised of 12 electrode fingers with length, width, and separation of 750 μm, 50 μm, and 50 μm, respectively, producing a total sensor area of 0.44 mm2 (Figure 1C). The patterned device was then transferred to a Temescal BJD-1800 electron beam evaporator (Ferrotec, Livermore, CA), where a layer of Ti/Au (25nm/300nm) was deposited once an appropriate base pressure was achieved. Following metal deposition, the device was left in a 100% acetone bath for approximately two hours to achieve metal lift-off. The metal patterned sensors were then rinsed with isopropanol and deionized water and dried with nitrogen. Once the metal deposition process was complete, a layer of SU-8-2005 photoresist (MicroChem, Westborough, MA) was spun onto the device at a thickness of approximately 5 µm. An additional photolithography mask design was used to provide an opening well to each sensor with length, width, and depth of 1300 µm, 1200 µm, and 5 µm, respectively. The device was then hard baked at 150 °C for 5 minutes to create a permanent structure. Lastly, the device was mounted in a 68-pin leadless ceramic chip carrier and each individual sensor wire bonded. The final device can be seen in Figure 1B. Commercial polymers used to create the sensor array are shown in Table 1, along with their abbreviation, respective solvent (if applicable), and molecular weight. The chemical structure of each polymer can be found in supplemental Figure S-1. The polymers were chosen based on previous research of polymer coated single layer graphene sensors.19 Each solution contained 12 mg/mL of polymer and 8 mg/mL of GNP in the solvent specified in Table 1 for a total solids content of 20 mg/mL. All sensor solutions were sonicated after mixing for 30 min to disperse GNP and again immediately before deposition. Sensor fabrication was completed by drop-casting of 0.2 µL of each GNP polymer-
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composite solution into individual IDE wells using a microliter syringe (Hamilton, Reno, NV). Following coating deposition the entire device was heated to 70 °C for 10 min to remove residual solvents. An example sensor is shown in Figure 1D. Table 1: Polymers used to create GNP-Polymer composites for each sensor material. Polymer Abbreviation Solvent Molecular Weight (Da) Polycaprolactone Polyepichlorohydrin Nafion 117 Polyvinyl alcohol Polyisobutylene
PCL PECH Nafion PVA PIB
Chloroform Chloroform Water/alcohols Water Chloroform
14,000 700,00 Unspecified 31,000 500,000
Poly(4-vinylphenol-co-methyl methacrylate)
PVPH-MMA
Tetrahydrofuran
3,000-5,000
Poly(1-vinylpyrrolidone)-graft-(1triacontene)
PVPDY_gt
Chloroform
643
Poly(vinylphosphonic acid) Polyacenaphthylene Polytetrafluoroethylene Poly(ethylene-co-vinyl acetate) Poly(4-vinylphenol)
PVPA PACN PTFE PEVA PVPH
Tetrahydrofuran Chloroform Chloroform
Unspecified 5,000 - 10,0000 Unspecified 55,000 25,000
Chloroform Tetrahydrofuran
Figure 1: A custom vapor generation system was used to infuse test samples through a Teflon gas flow cell (A). Each device contains 24 interdigitated electrode arrays wire-bonded to a leadless chip carrier (B) inserted into the flow cell. The electrode dimensions (C) and an example GNP-Polymer composite coated sensor (D) are shown for reference.
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Instrumentation and Analysis - The vapor generation system and gas flow cell used in these experiments is shown in Figure 1A was described previously.21 Briefly, a 68-pin breakout board was inserted into a custom machined Teflon block to provide a 0.25 mL flow channel to the sensor surface. For each trial, time vs. resistance measurements were collected and divided into three sections, baseline, sample, and recovery periods set at 60s, 30s, and 180s, respectively. The flow rate through the sensor was a constant 40 mL/min controlled by a digital mass flow controller (MKS Instruments, Andover, MA). Flow through the analyte vials was controlled by computer actuated solenoid valves. Vial bubblers containing analyte consisted of an inlet tube with a sparger at the end for bubble creation and Table 2: CWA simulant compounds and interferent compounds used to challenge the GNP-Polymer sensor array with their respective abbreviation, vapor pressure, and concentration used during testing.
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an exit tube leading to the flow cell. During baseline and recovery periods, 100% nitrogen is passed across the sensor while during the response period a ratio of nitrogen and saturated vapor of the analyte (Table 2) is combined before infusion into the flow cell. The device was exposed for 80-100 cycles with each cycle consisting of one trial for each analyte. The final data set contained 80-100 trials for each tested analyte for classification and cross validation. It is assumed a concentration equal to the saturated vapor of each analyte is infused for each trial because the fluid volume within the sealed vial remains constant and the temperature is steady during measurements. The sampling system and sensor were contained in a custom plastic enclosure outfitted with a fume extraction system and experiments were conducted at temperatures ranging between 21.8 and 22.4°C and 63.1 and 67.3% relative humidity. The resistance of each coated electrode was measured using a data acquisition and control cube configured with two 12-channel, fully isolated resistance input boards (United Electronics Industry, Walpole, MA), creating 24 total channels with simultaneous data collection. Each channel was configured to operate in 3-wire mode, simultaneously collecting resistance measurements at a rate of approximately 15 Hz with a current of 50 µA. A custom Python script was used to read a user generated input CSV file specifying the measurement parameters for a single measurement which are: baseline time, sample time, recovery time, active channels, minimum/maximum resistance, and sample flow rate. All post-processing, data analysis, and plotting was performed in Python 3.6 using Numpy, Scikit Learn and Matplotlib libraries. Table 2 lists the CWA simulant and interferent compounds and chemical structures used to challenge the discrimination capability of the sensor array. The CWA simulants (DIMP, DEMP, DMMP, TEP, and TMP) are commonly used as chemical surrogates for G-series CWA.22 The simulants chosen have similar chemical structures (phosphate groups), differing in functional group substitution around the central phosphoryl group. The interferents include common solvents (acetone, ethanol, hexane, and toluene) known to generate a good response for polymer based sensors5b, water, and complex mixtures (antifreeze, diesel, RoundUp) that are examples of background interferents expected in a real-world scenario. The antifreeze is a mixture of ethylene glycol (~90-95%), water and stabilizers, the RoundUp is a mixture of 41% glyphosphate, water, and stabilizers, and the diesel contains diesel and trace amounts of naphthalene. These background interferents provide a realistic stressor to evaluate discrimination capabilities of the sensor arrays.
Results and Discussion Coating formulation –The formulation of the coatings is important to optimize the discrimination capabilities of sensor array. The GNP loading of 40% w/w was chosen based on previous work with carbon black – polymer based vapor sensors that show a linear response vs. analyte exposure which is important for subsequent data processing methods.10b This loading is greater than the 1-5% required for conductivity, according to the manufacturer, resulting in a highly conductive film with a consistent linear sensor response to analyte. The IV curve of the coatings (Figure S5) shows a linear slope over the entire voltage range measured, including at the 50 µA current used for this study, which indicates device resistance. Many studies with sorption based chemical sensors utilize linear solvation energy relationship (LSER) parameters as a theoretical model to optimize polymer selection for sensor arrays.23 In contrast for this study the 12 polymers employed were down selected from a library of 23 polymers reported in a previous study with polymer overcoated graphene sensors.19a The diversity of the molecular weight and chemical structure (Figure S-1) of the selected polymers generates a wide range of analyte sorption rates for each sensor to create a resistance response signature (magnitude and shape) for subsequent classification. The down selected polymers that were chosen display high
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signal response magnitude, represent response diversity as demonstrated by a hierarchical cluster analysis, facilitate even dispersion of GNP’s based on visual analysis (no clumping after sonication), and show a stable signal response over time.19a These parameters are important to enhance discrimination capabilities and sensor repeatability. Sensor Array Response - The sensor response was investigated by measuring the resistance of the coated electrode arrays upon exposure to select analytes from Table 2. Figure 2 shows a subset of 10 trials to demonstrate the signal of sensors composed of PCL and PECH in response to toluene vapor exposure. The measured time series includes three distinct regions of baseline, response, and recovery for each trial with 80 trials total for each analyte. During the baseline period only nitrogen is flowing and the resistance remains flat. In the response section the resistance increases upon initial exposure and the rate of increase diminishes as the analyte absorption rate decreases as a function of time. Finally, during the recovery period the resistance decreases as analyte desorbs at an exponential rate while nitrogen flows over the sensor. The resistance increase results from analyte adsorption into the polymer causing swelling that increases the distance between immobilized GNP’s. In contrast, resistance decreases as analyte desorbs and the coating shrinks bringing the immobilized GNP’s closer together. This phenomena is also observed in other polymer-conductive carbon vapor sensors.14-15 As shown in Figure 2, raw resistance measurements for PCL and PECH upon repeated exposures to toluene exhibit baseline resistance drift over time. In Figure 2 the baseline drift is normalized by subtracting the average baseline resistance from all resistance measurements for each trial, demonstrating the magnitude of signal remains similar across trials. The magnitude and shape of the response for each polymer coating to each analyte is unique and varies as a function of the properties of both. The shape of the curve during both the absorption and desorption has unique properties that yield additional Figure 2: Measured resistance of a single polymer-GNP sensor in response diversity for response to toluene vapor is shown as raw signal and after discrimination.24 baseline subtraction. Each graph contains 10 representative trials Sensor Repeatability- Figure 3 for a single sensor with a PCL or PECH coating during baseline (060 sec), response (60-90 sec), and recovery (90-270 sec) phases. shows the relative maximum The maximum resistance change ∆R is also shown. resistance response (∆R/R), where the maximum resistance
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change (∆R) is divided by the average baseline resistance (R), of single PECH (3A) and PCL (3B) sensors to select analytes. Even when only evaluating one component of the total sensor response, there are clear differences in measured signal between tested analytes. In addition there are differences in signal magnitude Figure 3: The maximum relative resistance response to select analytes over between the two time is shown for individual sensors coated in PCL PECH (A) and PECH PCL (B). polymer coatings, Average relative resistance response of all trials to select analytes for sensors demonstrating a unique response with same polymer is shown with error bars ± 1 SD (C). The maximum and minimum relative maximum resistance response (∆R/R) for PCL and PECH to signature for each each analyte (except water) is shown (D). analyte. For both polymers tested the relative maximum response of the sensor remains consistent after 1040 unique exposures or 80 cycles of 13 different analytes over a period of 5 days. Some differences in response magnitude across trials can be explained by minor variations in analyte concentration due to temperature changes, flow rate fluctuations, analyte exposure history, and baseline drift (Figure 2). To validate sensor reproducibility a device containing multiple electrodes with the same polymer-GNP coating was tested. Replicate coatings of the polymers PCL and PECH were each deposited on four different electrode arrays for testing (Figure 3C). For the evaluated PCL sensors the initial resistance range was 35.0 - 152 Ω and for PECH sensors it was 196 - 660 Ω due to variations in hand spotted drop cast coatings. The range of baseline drift over a 5 day test period for the PCL sensors was 4.7-11.9% and for the PECH sensors it was 1.2-7.2%. For all sensors the ∆R/R for each analyte was statistically significant from a blank sample (nitrogen) using a student’s t-test (p
