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Published in final edited form as: Sens Actuators B Chem. 2013 May 4;185:587–593. doi: 10.1016/j.snb.2013.04.107

A Passive Radio-Frequency Identification (RFID) Gas Sensor With Self-Correction Against Fluctuations of Ambient Temperature

Radislav A Potyrailo 1,*, Cheryl Surman 1
PMCID: PMC3743261  NIHMSID: NIHMS491505  PMID: 23956496

Abstract

Uncontrolled fluctuations of ambient temperature in the field typically greatly reduce accuracy of gas sensors. In this study, we developed an approach for the self-correction against fluctuations of ambient temperature of individual gas and vapor sensors. The main innovation of our work is in the temperature correction which is accomplished without the need for a separate uncoated reference sensor or a separate temperature sensor. Our sensors are resonant inductor-capacitor-resistor (LCR) transducers coated with sensing materials and operated as multivariable passive (battery-free) radio-frequency identification (RFID) sensors. Using our developed approach, we performed quantitation of an exemplary vapor over the temperature range from 25 to 40 °C. This technical solution will be attractive in numerous applications where temperature stabilization of a gas sensor or addition of auxiliary temperature or uncoated reference sensors is prohibitive.

Keywords: passive RFID sensor, gas sensing, temperature correction, multivariate data analysis

Introduction

Uncontrolled fluctuations of ambient temperature greatly reduce accuracy of gas sensors [1-3] in their intended field applications. Thus, present research efforts are focused on the development of techniques for temperature compensation of gas sensor responses [4, 5]. One approach is to add separate a temperature sensor to a gas sensor system and to use the response of the temperature sensor to correct for the temperature-induced drift of the gas sensor response. Representative examples of this approach include temperature corrections of individual chemoresistor [6] and chemicapacitor [7] gas sensors as well as chemoresistor [8] and chemicapacitor [9] gas sensor arrays. Another approach is to add a separate uncoated sensor as a temperature reference and to use its temperature-affected response to correct for the temperature-affected response of a sensor coated with a gas-sensing film. Representative examples of this approach include microcantilevers [10] and acoustic wave resonators [11].

While these techniques can be applied for both tethered and wireless sensors [12], for wireless sensors, they are becoming progressively difficult due to the limitations of available on-board power and difficulties of adding separate temperature sensors for compensation and heaters for temperature control. As a result, temperature-induced drift is one of the most serious problems in wireless sensors [13]. Understanding of temperature effects followed by their significant reduction is critical for building robust temperature-corrected transfer functions for sensor operation in the field to preserve sensor response sensitivity, selectivity, and stability.

We recently developed a strategy of using resonant inductor-capacitor-resistor (LCR) transducers coated with different sensing materials as multivariable sensors to improve selectivity of gas-phase detection [14, 15]. These sensors can be utilized as passive (battery-free) radio-frequency identification (RFID) sensors fabricated using different methods, including a conventional roll-to-roll process [16]. Although battery-powered wireless sensors communicate over greater distances than passive sensors, their relatively large (battery-limited) size and the need for periodic battery replacement [17, 18] are serious limitations in numerous applications including food and medical packaging [19, 20], single-use biopharmaceutical manufacturing [21], and wearable electronics [17, 22]. For these and other applications, passive sensors are attractive as long-lasting, cost efficient, easy-to-manufacture, and inconspicuous devices [15].

In our approach of passive RFID-based sensing, we convert ubiquitous 13.56 MHz RFID tags into inductively coupled sensors, acquire the resonance impedance spectrum of the RFID antenna, obtain digital sensor calibration parameters stored in an integrated circuit (IC) memory chip, perform multivariate analysis of measured resonance impedance spectrum, and extract the relation between the sensor response and the gases of interest [14, 23, 24]. We demonstrated that such individual sensors have self-correction ability against uncontrolled fluctuations of humidity [24-26], quantify an analyte in the presence of several interferences [15], and achieve part-per-billion and part-per-trillion [20, 26] limits of detection. These abilities is an attractive departure from the concept of sensor arrays where otherwise individual sensors suffer from humidity and other interferences [27-29].

Wireless or non-contact implementations of these sensors are applicable for numerous applications, for example for the rapid screening of multiple sensors embedded in food packaging [20], for the elimination of the need for de-contamination in homeland security applications [26], and for monitoring of pollutants of industrial and environmental importance [23]. However, in these and many other applications, there is a strong need to have a methodology for the self-correction against fluctuations of ambient temperature in a single sensor.

In this study, we developed an approach for the self-correction against fluctuations of ambient temperature of individual passive RFID gas sensors inductively coupled with a sensor reader without the need for a separate uncoated reference sensor or a separate temperature sensor. Using this approach, we performed quantitation of water vapor (selected as a relevant model vapor) from 0 to ~ 8500 ppm with the standard error of 212 ppm of water vapor over the temperature range from 25 to 40 °C.

Experimental

RFID gas sensors were constructed from available 13.56 MHz RFID tags as detailed earlier [14, 23, 24, 30]. Polyetherurethane (PEUT) was selected as a sensing material. The PEUT material was drop cast onto the sensing antenna from a PEUT solution in dichloromethane forming a ~ 50-μm thick sensing film. Water vapor was selected as a model vapor for the demonstration of the principle of self-correction of individual multivariable vapor sensors against temperature fluctuations. Sensor was exposed to different water vapor concentrations at different temperatures such as 25, 30, 35, and 40 °C by placing the RFID sensor into an environmental chamber with ~0.1 °C temperature control. Different concentrations of water vapor were generated and monitored outside the environmental chamber using a computer-controlled vapor-generation system using dry nitrogen gas as a blank gas and keeping the total flow rate constant at 0.45 L/min. Tested concentrations of water vapor were 0, 2807, 4210, 5614, 7017, and 8421 ppm (corresponding to 0, 9, 13, 18, 22, and 27 % of relative humidity (RH) at 25 °C) as determined at the smallest tested temperature using a reference humidity sensor (Model HS1101LF, Humirel, Inc.). Measurements of low (part-per-million) and high (% RH) concentrations of water vapor are important for different applications as described earlier [31-35]. Conversion of water vapor concentration from ppm to %RH at different temperatures can be easily performed as described elsewhere [36]. Exposures to five vapor concentrations were done in three replicates at each temperature. Vapor exposure steps were 4.5 min long with 9 min long exposures to the blank nitrogen gas. The duration between replicates was 16.5 min long.

Measurements of resonance impedance of the RFID sensors were performed with a network analyzer (Model E5062A, Agilent Technologies, Inc., Santa Clara, CA). Collected impedance data was analyzed using KaleidaGraph (Synergy Software, Reading, PA) and PLS_Toolbox (Eigenvector Research, Inc., Manson, WA) operated with Matlab (The Mathworks Inc., Natick, MA). Digital ID readings from RFID sensors were performed using handheld and computer-controlled SkyeTek readers (Westminster, CO) and a computer-controlled RFID Reader/Writer evaluation module (Model TRF7960 Evaluation Module, Texas Instruments).

Results and Discussion

A. Principle of self-correction against temperature fluctuations

The principle of operation of our multivariable RFID sensors and the self-correction against fluctuations of ambient temperature is illustrated in Fig. 1. We employ 13.56 MHz RFID tag-based sensors available from different manufacturers or with antennas fabricated in-house [24], deposit a sensing material onto a resonant antenna of the sensor [15], acquire the resonance impedance spectrum Ž(f) of the RFID antenna, and obtain digital sensor calibration parameters stored in an IC memory chip (Fig. 1A). Measurement of Ž(f) of the RFID sensor antenna and reading/writing of digital data from the IC chip are performed via mutual inductance coupling between the RFID sensor antenna and the pickup coil of a reader.

Figure 1.

Figure 1

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Principle of operation of passive multivariable RFID sensors and their self-correction against fluctuations of ambient temperature. (A) Sensor layout with a resonant antenna coated with a gas-sensing film and an IC memory chip. (B) Equivalent circuit of an RFID sensor. (C) Measured impedance spectrum, Zre(f) and Zim(f), and examples of parameters for multivariate analysis: the frequency Fp and magnitude Zp of the maximum of the real part of impedance, the resonant F1 and anti-resonant F2 frequencies of the imaginary part of impedance, and magnitudes Z1 and Z2 at the resonant and anti-resonant frequencies.

A simplified equivalent circuit of the multivariable RFID sensor is illustrated in Fig. 1B with inductance LA, capacitance CA, and resistance RA of the sensing antenna coil and capacitance CC and resistance RC of the IC chip. A sensing film that affects CA and RA of the equivalent circuit is further applied onto the sensing antenna. The complex permittivity of the sensing film is described as ε′r – j ε″r where the real part ε′r is also known as dielectric constant and the imaginary part ε″r is directly proportional to conductivity σ. Variations in ambient temperature and gas concentrations produce uncorrelated effects on LA, CA, RA, CC, and RC of the equivalent circuit. These uncorrelated effects originate from the different materials (dielectric sensor substrate, metal sensor coil, dielectric sensing film, and semiconductor IC memory chip) and their different temperature- and gas-induced effects on the equivalent circuit of the sensor. These uncorrelated effects are quantitatively separated by the multivariable response of the sensor making possible temperature correction with the same multivariable gas sensor without the need for a separate uncoated reference sensor or a separate temperature sensor [37].

To accurately measure gases in the presence of the uncontrolled temperature fluctuations, the real Zre(f) and imaginary Zim(f) parts of the impedance spectra Ž(f) are measured from the resonant sensor antenna coated with a sensing film and several spectral parameters are calculated from the measured Zre(f) and Zim(f) as shown in Fig. 1C. These parameters include the frequency position Fp and magnitude Zp of Zre(f), the resonant F1 and antiresonant F2 frequencies of Zim(f), and the impedance magnitudes Z1 and Z2 at F1 and F2 frequencies, respectively. Additional parameters can also be calculated (zero-reactance frequency, quality factor, etc). From the measured parameters, resistance, capacitance, and other parameters of the polymer-coated resonant antenna can be also determined.

Multivariate analysis reduces the dimensionality of the complex impedance response (from measured real Zre(f) and imaginary Zim(f) parts of the complex impedance spectra or calculated parameters Fp, Zp, F1, F2, Z1, and Z2 to a single data point in multidimensional space for selective quantitation of different gases and for the self-correction against fluctuations of ambient temperature.

B. Univariate analysis of RFID sensor response

For demonstration of our method for the self-correction against fluctuations of ambient temperature, we employed PEUT as a “classic” sensing material [38-43], previously used for detection of different organic vapors and water vapor. Chemical structure of PEUT is presented in Fig. 2. This material is a dielectric polymer with a glass transition temperature below room temperature [41, 44].

Figure 2.

Figure 2

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Chemical structure of PEUT employed in fabricated RFID sensors.

Examples of univariate responses of the RFID sensor such as Fp and Zp responses are illustrated in Fig. 3 A, B. Measurements were performed at four temperatures (25, 30, 35, and 40 °C). At each temperature, the sensor was exposed to three replicate runs of five water vapor concentrations (2807, 4210, 5614, 7017, and 8421 ppm) spaced by the exposures to the dry carrier gas (nitrogen). These replicate measurements provided information about the reproducibility of sensor performance when different levels of the humidified gas were brought to the sensor at a flow rate of 0.45 L/min. At the experimental conditions of these studies, temperature equilibration was several tens of minutes (see Fig. 3 A, B). Thus, for modeling of sensor performance at different temperatures, we used sensor responses only from the second and third replicate runs.

Figure 3.

Figure 3

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Examples of individual univariate responses (A) ΔFp and (B) ΔZp of the sensor upon exposure to five water vapor concentrations (2807, 4210, 5614, 7017, and 8421 ppm) at four temperatures (25, 30, 35, and 40 °C) and control steps of (C) water vapor concentrations and (D) temperature. At each temperature, three replicate measurements of exposures to five water vapor concentrations were performed. Inset in (B) illustrates a reversible response and a low noise level of the sensor. Actual temperature and vapor concentrations were measured using reference sensors as detailed in the Experimental Section.

The temperature changes from 25 to 30, 40, and 40 °C produced temperature-induced baseline frequency and impedance offsets. For example, the Fp frequency offset was ~100 kHz when the sensor was at 40 °C as compared to Fp response at 25 °C (see Fig. 3 A). Similarly, the Zp impedance offset was ~ 5 Ohm upon temperature change from 25 to 40 °C (see Fig. 3 B). These baseline offsets were the result of the temperature effects on the sensor electronic circuitry and sensing film. Temperature effects on the sensor electronic circuitry were pronounced via the changes in the dielectric constant of the sensor substrate, resistance of the sensing coil, and capacitance and resistance of the IC memory chip. Temperature effects on the sensing material were pronounced via the changes in the dielectric constant of the sensing film.

The sensitivity of this RFID sensor to water vapor was expected to be temperature-dependent as was demonstrated earlier with numerous sensing materials [1, 6, 45-48]. The effects of temperature on the water-vapor sensitivity of the sensor are illustrated in Fig. 3 A, B. Upon the temperature increase, the changes in Fp and Zp signals between the sensor response to dry and humidified carrier gas were proportionally reduced. The main factor in the temperature-affected vapor sensitivity of the PEUT dielectric sensing material used in these studies was the temperature dependence of the vapor-partition coefficient of PEUT.

The inset of Fig. 3 B illustrates two additional sensor aspects such as (1) a reversible sensor response with the response/recovery times controlled by gas flow rate, cell dead volume, and sensing film thickness and (2) the low noise level of the sensor. In our earlier work, the measured short term sensor noise (as one standard deviation, 1σ) was 60 Hz and 0.025 Ohm for Fp and Zp, respectively [30]. In the present work, the noise level was reduced by at least two-fold.

Fig. 4 presents a summary of the temperature-induced baseline frequency and impedance offsets for all univariate parameters (Fp, Zp, F1, F2, Z1, and Z2) employed in this study. Similarly, Fig. 5 summarizes the vapor-induced signal changes in Fp, Zp, F1, F2, Z1, and Z2 responses at different temperatures where the sensor was exposed to the highest water vapor concentration. This data is from the second and third replicate runs of the sensor; each data point is the mean of baseline values and the error bars represent one standard deviation 1σ. The sensor precision was < 5 % RSD (relative standard deviation): most of the error bars of the Fp, Zp, F1, F2, Z1, and Z2 responses were smaller than the size of the data points. This diversity of individual responses Fp, Zp, F1, F2, Z1, and Z2 to temperature and water-vapor concentrations provided the foundation for the multivariate analysis as described in the next section.

Figure 4.

Figure 4

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Temperature-induced baseline frequency and impedance offsets: (A) frequency offsets Fp, F1, and F2, (B) impedance offsets Zp, Z1, and Z2. The data is from the second and third replicate runs of the sensor; each data point is the mean of baseline values and the error bars represent one standard deviation 1σ. Most of the error bars of the Fp, Zp, F1, F2, Z1, and Z2 responses are smaller than the size of the data points.

Figure 5.

Figure 5

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Vapor-induced sensor signal changes at different temperatures where the sensor was exposed to the highest water vapor concentration: (A) frequency shifts Fp, F1, and F2, (B) impedance shifts Zp, Z1, and Z2. The data is from the second and third replicate runs of the sensor; each data point is the mean of baseline values and the error bars represent one standard deviation 1σ. Most of the error bars of the Fp, Zp, F1, F2, Z1, and Z2 responses are smaller than the size of the data points.

C. Multivariate analysis of RFID sensor response

By applying multivariate analysis of sensor response, discrimination between temperature and vapor concentration effects was obtained. We applied principal components analysis (PCA) to produce a multivariate signature from the single sensor. PCA is a multivariate data analysis tool that projects the data onto a subspace of lower dimensionality with removed collinearity and displays variance in the data as weighted sums of the original variables (called principal components, PCs) [49]. The responses Fp, Zp, F1, F2, Z1, Z2 for different water vapor concentrations and temperatures were processed using PCA. Fig. 6 illustrates a PCA scores plot of the sensor response to variable concentrations of water vapor at four tested temperatures. Similar with the analysis of individual responses Fp, Zp, F1, F2, Z1, and Z2, the multivariate sensor response was strongest at 25 °C as indicated by the distance between the data points associated with the sensor responses to different water vapor concentrations at 25 °C. The first two PCs of the built PCA model included more than 99% of the total variance from the data set.

Figure 6.

Figure 6

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PCA scores plot of multivariate sensor response to variable concentrations of water vapor at four tested temperatures.

Critical to the sensor ability for the self-correction against temperature fluctuations is the separation between the data points in the PCA scores plot when the data originated from the sensor response at different temperatures. This separation between the respective data points is due to the independent effects of temperature on the different components of the sensor equivalent circuit and the sensing film (see Fig. 1 B).

We further developed a model for prediction of water vapor concentrations at various temperatures using only a single sensor (see Fig. 7). Results of developed quadratic model are illustrated in Fig. 7 A. The predicted values of water vapor concentrations at various temperatures were calculated and compared with the actual measured values. As illustrated in a correlation plot in Fig. 7 B, the data points were close to a line corresponding to a slope of unity. The plot of residual errors of prediction (see Fig. 7 C) demonstrated a desired almost random data spread. The standard error of the quadratic model was 212 ppm of water vapor (i.e., 0.7 % relative humidity).

Figure 7.

Figure 7

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Single RFID sensor quantifies water vapor independently of temperature. (A) Developed quadratic model of multivariate sensor response. (B) Actual vs. predicted values of water vapor concentrations at four different temperatures. (C) Plot of residual errors of prediction of water vapor concentrations at four different temperatures.

Conclusions

While implementation of ubiquitous passive 13.56 MHz RFID tags as inductively coupled sensors opens numerous opportunities, it is important to provide technical solutions for these sensors to operate in the field conditions of uncontrolled ambient temperature. This study demonstrated such an approach where multivariate analysis was applied for the self-correction for temperature effects in the response to individual LCR transducers coated with sensing materials and operated as multivariable passive RFID sensors without the need for a separate uncoated reference sensor or a separate temperature sensor. Our current work is focused on design of a portable and cost-effective network analyzer system as a sensor reader [50, 51]. Results of those studies are forthcoming.

Acknowledgments

This work has been supported in part by GE Corporate long-term research funds and by the National Institute of Environmental Health Sciences under Grant No. 1R01ES016569-01A1.

Biographies

Radislav A. Potyrailo is a Principal Scientist at GE Global Research Center in Niskayuna, NY. He holds an Optoelectronics degree from Kiev Polytechnic Institute, Ukraine, and a Ph.D. in Analytical Chemistry from Indiana University, Bloomington, IN. Radislav joined GE Global Research in 1998 and contributed to sensing technologies and functional materials for GE Plastics, Water, Security, Sensing, Healthcare, Consumer & Industrial, and Energy. His research interests include analytical instrumentation, functional nanomaterials, bioinspired photonics, wireless sensors and sensor networks, energy harvesting, and data analysis algorithms. Radislav is an Adjunct Industrial Professor at Indiana University, Department of Chemistry, Bloomington, IN performing research on sensors, plasmonics, and fluidics with several research groups. Radislav has 150+ publications and 75+ granted US Patents. He gave 60+ invited and several keynote lectures on national and international technical meetings. He serves as an editor of the Springer book series Integrated Analytical Systems, Consulting Editor of ACS Combinatorial Science, and Editorial Board Member of Sensors. Most recent awards include 2010 Prism Award for photonics innovation by SPIE and Photonics Media and 2012 Blodgett Award by GE Global Research for outstanding technical achievements. In 2011 Radislav was elected SPIE Fellow for achievements in fundamental breakthroughs in optical sensing and innovative analytical systems.

Cheryl Surman (Bratu) is a Senior Scientist at General Electric Global Research Center in Niskayuna, New York. She received her B.S. in Chemistry and Biology from Valparaiso University in Valparaiso, Indiana, in 1997 and her Ph.D. in Bioanalytical Chemistry from Rensselaer Polytechnic Institute in 2006. She has ~15 years of experience in the development of various analytical techniques including 5 years of experience in online process control. In addition, Dr. Surman has extensive background and practical experience in chemometric techniques and combinatorial methods. Prior to joining GE, she worked at UOP, LLC, developing data-analysis techniques for process control and combinatorial screening of heterogeneous catalysts. At GE, Cheryl played a major role in the development of new sensing materials and an optical sensor array system for GE Water Technologies that has been awarded with the 2010 Prism Award for photonics innovation by SPIE and Photonics Media. Her current research is in the area of radio frequency identification (RFID) chemical, biological, and physical sensors. She has 20+ technical publications and 20+ filed patents.

Footnotes

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