Abstract
Human olfactory sensors have a large variety of receptor cells that generate signature responses to various gaseous molecules. Ideally, artificial olfactory sensors should have arrays of diverse sensors. However, it is challenging to monolithically integrate large-scale arrays of different high-performance gas sensors. Here we report biomimetic olfactory chips that integrate nanotube sensor arrays on nanoporous substrates with up to 10,000 individually addressable sensors per chip. The range of sensors is achieved using an engineered material composition gradient. Supported by artificial intelligence, the chips offer a high sensitivity to various gases with excellent distinguishability for mixed gases and 24 distinct odours. We also show that the olfactory chips can be combined with vision sensors on a robot dog to create a system that can identify an object in a blind box.
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Data availability
Data that support the findings of the study are available from the corresponding author upon reasonable request. Source data are provided with this paper.
Code availability
All the codes used to support the conclusions of the paper are available from the corresponding author upon reasonable request.
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Acknowledgements
This work is supported by the National Key Research and Development Program of China (grant no. 2022YFB203500), the Hong Kong Innovation Technology Fund (grant no. GHP/014/19SZ), the Zhongshan Municipal Science and Technology Bureau (grant no. ZSST21EG05), the Internal Fund of the Hong Kong University of Science and Technology (HKUST; grant no. IOPCF21EG01), the Center on Smart Sensors and Environmental Technologies, Foshan HKUST projects (Project Nos. FSUST21-HKUST08D and FSUST21-HKUST09D) and the Foshan Innovative and Entrepreneurial Research Team Program (grant no. 2018IT100031). Z.F. acknowledges support from the New Cornerstone Science Foundation through the XPLORER PRIZE and the Hong Kong Alliance of Technology and Innovation through the Bank of China (Hong Kong) Science and Technology Innovation Prize. We thank the Material and Characterization Preparation Facility and the Nanosystem Fabrication Facility, both at HKUST, and also MNT Micro and Nanotech Co., Ltd for technical assistance.
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Contributions
Z.F., C.W. and Z.C. conceptualized the experiments and methodology design. C.W. fabricated the sensor-array chip and collected the sensing test data. Z.C. designed the algorithm and processed the collected sensing data. W.Y. and C.L.J.C. helped with validating the quadruped robot. Zhu’an Wan and C.L.J.C. assisted with designing and fabricating the read-out circuit. C.L.J.C. and Z.C. designed the interface programme for data acquisition and visualization. W.T., W.Z., B.R., D.Z. and S.M. contributed to the characterization of the materials and devices. Z.M., Z.L., Zixi Wan, F.X., Z.S. and S.P. assisted with the fabrication of the sensor-array chip. Y.D. assisted in designing the figures. G.L. and K.L. contributed to discussions about the paper. Z.F. and Q.Z. provided financial support and oversight of the whole project. C.W., Z.C., Zhu’an Wan and Z.F. wrote the paper. All authors participated in manuscript revision and refinement.
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Extended data
Extended Data Fig. 1 Fabrication process of the biomimetic olfactory chip (BOC).
a, Bare PAM substrate. b, SnO2 deposition inside PAM by ALD. c, Pd deposition inside PAM by ALD. d, Multi-component interfacial layer deposition on PAM substrate by sputter. e, Top and bottom Au electrodes deposition by thermal evaporation. f, SiO2 insulating layer deposition by E-beam evaporation. g, Pt heater deposition by E-beam evaporation.
Extended Data Fig. 2 Device-to-device repeatability test by the statistics along the diagonal sensors (Sensor #1, 12, 23, 34, 45, 56, 67, 78, 89, 100, test gas: 1 ppm acetone).
a, Optical image of twelve 100-pixel BOC chips. b, Average resistance of sensors. The sample size used to derive statistics is 12. The error bars indicate the standard deviation (SD). Data are presented as mean values +/- SD. c, Standard deviation of resistances. d, Coefficient of variation of resistances. e, Average gas response of sensors. The sample size used to derive statistics is 12. The error bars indicate the standard deviation (SD). Data are presented as mean values +/− SD. f, Standard deviation of gas responses. g, Coefficient of variation of gas responses.
Extended Data Fig. 3 The statistical data of the diagonal sensors’ response (Sensor #1, 12, 23, 34, 45, 56, 67, 78, 89, 100) in sensor array chip to 1 ppm acetone at different working temperatures.
a, Response times. b, Recovery times. c. Gas responses. The sample size used to derive statistics is 6. The error bars indicate the standard deviation (SD). Data are presented as mean values +/- SD.
Extended Data Fig. 4 Single gas classification with the BOC (The training data is collected in the 1st month and the test data is collected in the 2nd and 3rd months.).
a, Confusion matrix of the actual class and predicted class for recognizing 8 gases (A: acetone, C: carbon monoxide, E: ethanol, F: formaldehyde, N: nitrogen dioxide, T: toluene, H: hydrogen, I: isobutylene) by using the testing data collected in the 2nd month. b, Confusion matrix of the actual class and predicted class for recognizing 8 gases by using the testing data collected in the 3rd month. c, Prediction accuracy by using the 1st month data as the training data and the 2nd and 3rd months data as the testing data.
Extended Data Fig. 5 The path design of robot for the experiment of the fusion of the quadruped robot’s vision and olfactory functions to implement blind box recognition.
We design two experiments: one is using only one odour (red wine), and another one is using two odours (orange and red wine).
Extended Data Fig. 6 Construction of ultra-large-scale sensor array BOCs.
a, Picture of 20 × 20 sensor array chip bonded on LCC chip carrier. b, c, Optical images of 20 × 20 sensor array chip with different magnification (scale bar: b is 1 mm and c is 100 µm). d, Gas response pattern of 20 × 20 sensor array chip to 1 ppm acetone. e, Picture of 100 × 100 sensor array chip bonded on C-PGA chip carrier. f and g, Optical images of 100 × 100 sensor array chip with different magnification (scale bar: f is 1 mm and g is 100 µm). h, SEM image of 100 × 100 sensor array chip (scale bar: 10 µm). i, Gas response pattern of 100 × 100 sensor array chip to 1 ppm acetone.
Supplementary information
Supplementary Information
Supplementary Texts 1–3, Figs. 1–54, Tables 1–5, Videos 1 and 2, and references.
Supplementary Video 1
Real-time classification of odours using our developed BOC system.
Supplementary Video 2
Fusion of the quadruped robot’s vision and olfactory sensors to recognize odours in a blind box.
Source data
Source Data Fig. 1
Number of genes expressed in intact olfactory receptors from different mammals.
Source Data Fig. 2
XRD profiles of the MCI layer and sensing layer and XPS spectra of the sensing layer.
Source Data Fig. 3
Data from a sensor in response to acetone; confusion matrix for the actual class and predicted class; predicted accuracy, training loss and testing loss versus epochs; and predicted accuracy versus the number of sensors.
Source Data Fig. 4
Predicted concentrations of gas mixture and average relative error between predicted concentrations and actual concentrations.
Source Data Fig. 5
Real-time recorded resistance signal of sensor 61.
Source Data Extended Data Fig. 2
Statistics of resistance and responses to 1 ppm acetone from 12 BOCs.
Source Data Extended Data Fig. 3
Statistics for responses of ten diagonal sensors at different working temperatures.
Source Data Extended Data Fig. 4
Confusion matrices for the actual class and predicted class and predicted accuracy.
Source Data Extended Data Fig. 6
Response data for 400- and 10,000-pixel BOCs to 1 ppm acetone.
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Wang, C., Chen, Z., Chan, C.L.J. et al. Biomimetic olfactory chips based on large-scale monolithically integrated nanotube sensor arrays. Nat Electron 7, 157–167 (2024). https://doi.org/10.1038/s41928-023-01107-7
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DOI: https://doi.org/10.1038/s41928-023-01107-7
