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Extraordinary performance of semiconducting metal oxide gas sensors using dielectric excitation

Nature Electronics volume 3, pages 280–289 (2020)Cite this article

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Abstract

Semiconducting metal oxides are widely used for gas sensors. The resulting chemiresistor devices, however, suffer from non-linear responses, signal fluctuations and gas cross-sensitivities, which limits their use in demanding applications of air-quality monitoring. Here, we show that conventional semiconducting metal oxide materials can provide high-performance sensors using an impedance measurement technique. Our approach is based on dielectric excitation measurements and yields sensors with a linear gas response (R2 > 0.99), broad dynamic range of gas detection (six decades of concentrations) and high baseline stability, as well as reduced humidity and ambient-temperature effects. We validated the technique using a range of commercial sensing elements and a range of gases in both laboratory and field conditions. Our approach can be applied to both n- and p-type semiconducting metal oxide materials, and we show that it can be used in wireless sensor networks, and drone-based and wearable environmental and industrial gas monitoring.

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Fig. 1: Metal oxide semiconducting materials for gas sensing using conventional resistance and dielectric excitation schemes.
Fig. 2: Theoretical verification of the experimentally developed dielectric excitation scheme for the controlled linearity of SMOX sensors.
Fig. 3: Effects of ambient humidity and temperature on the resistance and dielectric responses of SMOX sensing elements.
Fig. 4: Linearity of SMOX gas sensors under dielectric excitation.
Fig. 5: General improvement of SMOX sensor baseline using dielectric excitation scheme.
Fig. 6: Dielectric excitation measurements of responses of SMOX sensing elements to various volatiles with LODs at ppb levels.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Different phases of this project were funded by GE Research Innovation Fund, GE Services, National Institute for Occupational Safety and Health Contracts 211-2015-63806 and 75D30118C02617, GE Renewable Energy and BHGE. The findings and conclusions in this study should not be construed to represent any determination or policy of the US Government. The content of this report does not necessarily reflect the position or the policy of the US Government. Certain commercial equipment, instruments or materials are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute for Occupational Safety and Health and the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

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Authors and Affiliations

  1. GE Global Research, Niskayuna, NY, USA

    Radislav A. Potyrailo, Steven Go, Daniel Sexton, Bruce Amm, Richard St-Pierre, Brian Scherer, Majid Nayeri, Guang Wu, Christopher Collazo-Davila & Doug Forman

  2. GE Global Growth Organization, Shanghai, China

    Xiaxi Li

  3. BHGE Oil & Gas Technology Center, Oklahoma, OK, USA

    Nasr Alkadi

  4. National Institute of Standards and Technology, Gaithersburg, MD, USA

    Andrei Kolmakov

  5. GE Renewable Energy, Lisburn, Ireland

    Chris Calvert, Craig Mack & Philip McConnell

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Contributions

R.A.P. conceived and led the research, R.A.P., C.C. and N.A. designed the laboratory and field experiments, R.A.P., S.G., B.A. and R.S.-P. developed the experimental set-ups for the laboratory tests, S.G., D.S., B.A. and R.S.-P. designed the wireless sensor nodes, A.K. designed and performed the nanocharacterization experiments, R.A.P., S.G., N.A., D.F., C.M. and P.M. performed laboratory and field experiments, X.L. and C.C.-D. performed the theoretical modelling, M.N., G.W. and R.A.P. analysed field data from the sensor nodes, R.A.P. and B.S. analysed data from the multigas experiments and R.A.P. wrote the manuscript with input and comments from all the authors.

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Correspondence to Radislav A. Potyrailo.

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Extended data

Extended Data Fig. 1 Spectral details of dielectric excitation measurements of response of a SMOX sensing element to different concentration ranges of methane.

a, 0–10 ppm, b, 0–100 ppm, c, 0–1,000 ppm, and d, 0–10,000 ppm. Each panel (a–d) has the top graph of Z′(f) spectra, middle graph of Z″(f) spectra and the bottom graph is the zoomed-in region of Z″(f) spectra with the spectral region of the linear sensor response to methane (dotted lines). Different colors in spectra in (a–d) are labeled as 0 to 16 as the respective methane gas concentration steps depicted in Fig. 1g–n and plotted as a blank (0) and every other spectrum (2–16).

Extended Data Fig. 2 Broad range of gas-response linearity achieved with dielectric excitation measurements.

a, Detection of methane at sub-ppm and low-ppm concentrations with the achieved LOD of 0.02 ppm. b, Detection of methane from 0 to 11 % vol.

Extended Data Fig. 3 Examples of responses of different types of SMOX sensing elements to diverse gaseous species obtained using conventional resistance (top graphs) and dielectric excitation measurements (bottom graphs).

a, Ethanol, b, CH4, c, H2. Insets in bottom graphs are different generations of SMOX sensing elements. For details of the SMOX sensing elements, see Supplementary Table 1. For corresponding Nyquist plots, see Supplementary Fig. 8.

Extended Data Fig. 4 Dielectric excitation measurements with a p-type SMOX material using a VOCM31 sensing element (see Supplementary Table 1).

Ethanol was used as a model analyte. Monitoring of ethanol concentrations using a, conventional resistance and b, dielectric excitation measurements. c, Z′(f) and d, Z″(f) spectra and e, Nyquist plots of sensor response. f, Frequency dependence of the R2 values of the linear fit. Inset, low-frequency range. Ethanol vapour concentrations: 0, 300, 600, 900, 1200, 1500, and 1800 ppm. For details about the VOCM31 sensing element (see Supplementary Table 1).

Extended Data Fig. 5 Rules for dielectric excitation measurements to achieve linear gas-sensing response in n- and p-type SMOX materials.

a, Response of n-type materials to increasing concentrations of reducing volatiles where Z″(f) spectra follow the increasing gas concentrations with the high-frequency shifts. b, Response of p-type materials to increasing concentrations of reducing volatiles where Z″(f) spectra follow the increasing gas concentrations with the low-frequency shifts. Thus, for both, n- and p-type SMOX materials the linear Z″(f) gas responses were observed on the front-edge shoulder of the relaxation peak that followed the gas concentrations. For n- and p-type materials, the front-edge shoulder was the high- or low-frequency regions of the relaxation peak, respectively.

Extended Data Fig. 6 Dynamic response of the SMOX-based sensor in different modes of operation.

a, Conventional chemiresistor mode, b, Dielectric excitation measurement mode.

Extended Data Fig. 7 Wireless sensor node components and field data collection unit.

a, Boards for sensor data acquisition, b, Boards for sensor data acquisition and wireless data communication. c, Assembled sensor node. d, Sensor nodes in a chamber for gas calibration. e, Field data collection unit, paper coffee cup shown for scale.

Extended Data Fig. 8 Benchmarking of the performance of the developed wireless sensor node against a tunable diode laser absorption spectroscopy (TDLAS) system in dynamic detection of methane under ambient wind conditions.

a, Test layout. Dynamic responses of b, TDLAS and c, developed sensor system.

Extended Data Fig. 9 Summary of calibration stability of several sensor nodes after 407 days as percent of sensitivity change of the sensors.

a, b, Z′ measurements and histogram for all nodes. c, d, Z″ measurements and histogram for all nodes. e, Summary for all nodes demonstrating Z′ calibration stability from–3% to 3% and Z″ calibration stability from – 15% to - 3%. Node 7 was not tested.

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Supplemental Information

Supplementary Notes 1–9, Tables 1–3, Figs. 1–29 and references 1–151.

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Potyrailo, R.A., Go, S., Sexton, D. et al. Extraordinary performance of semiconducting metal oxide gas sensors using dielectric excitation. Nat Electron 3, 280–289 (2020). https://doi.org/10.1038/s41928-020-0402-3

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