J. Semicond. > Volume 40?>?Issue 11?> Article Number: 111601

Smart gas sensor arrays powered by artificial intelligence

Zhesi Chen 1, 2, , Zhuo Chen 1, 2, , Zhilong Song 1, 2, , Wenhao Ye 1, 2, and Zhiyong Fan 1, 2, ,

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Abstract: Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision, hearing, touch, smell, and taste. A gas sensor array (GSA), also known as electronic nose, is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules. Artificial intelligence (AI) applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA. A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring, gas leakage detection, food and beverage production and storage, and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability, low-power-consumption and ease-of-operation. It is exciting to envisage robots equipped with a "nose" acting as family doctor who will guard every family member's health and keep their home safe. In this review, we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems, exploring their potential applications in disease diagnosis, environmental monitoring, and explosive detection. We also discuss the key limitations of gas sensor units and their possible solutions. Finally, we present the outlook of GSAs over the horizon of smart homes and cities.

Key words: mobile robotsgas sensor arrayelectronic noseartificial intelligenceenvironmental monitoringdisease diagnosis

Abstract: Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision, hearing, touch, smell, and taste. A gas sensor array (GSA), also known as electronic nose, is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules. Artificial intelligence (AI) applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA. A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring, gas leakage detection, food and beverage production and storage, and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability, low-power-consumption and ease-of-operation. It is exciting to envisage robots equipped with a "nose" acting as family doctor who will guard every family member's health and keep their home safe. In this review, we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems, exploring their potential applications in disease diagnosis, environmental monitoring, and explosive detection. We also discuss the key limitations of gas sensor units and their possible solutions. Finally, we present the outlook of GSAs over the horizon of smart homes and cities.

Key words: mobile robotsgas sensor arrayelectronic noseartificial intelligenceenvironmental monitoringdisease diagnosis



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[1]

Ko H C, Stoykovich M P, Song J, et al. A hemispherical electronic eye camera based on compressible silicon optoelectronics. Nature, 2008, 454(7205), 748

[2]

Song Y M, Xie Y, Malyarchuk V, et al. Digital cameras with designs inspired by the arthropod eye. Nature, 2013, 497(7447), 95

[3]

Yang Y, da Costa R C, Fuchter M J, et al. Circularly polarized light detection by a chiral organic semiconductor transistor. Nat Photonics, 2013, 7(8), 634

[4]

Lorenzo N, Wan T L, Harper R J, et al. Laboratory and field experiments used to identify Canis lupus var. familiaris active odor signature chemicals from drugs, explosives, and humans. Anal Bioanal Chem, 2003, 376, 1212

[5]

Bartolozzi C, Natale L, Nori F, et al. Robots with a sense of touch. Nat Mater, 2016, 15, 921

[6]

Sundaram S, Kellnhofer P, Li Y, et al. Learning the signatures of the human grasp using a scalable tactile glove. Nature, 2019, 569(7758), 698

[7]

Romano J, Hsiao K, Niemeyer G, et al. Human-inspired robotic grasp control with tactile sensing. IEEE Trans Robot, 2011, 27, 1067

[8]

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8), 1735

[9]

Persaud K, Dodd G. Olfactory system using a model nose. Nature, 1982, 299, 352

[10]

Güntner A T, Koren V, Chikkadi K, et al. E-nose sensing of low-ppb formaldehyde in gas mixtures at high relative humidity for breath screening of lung cancer. ACS Sens, 2016, 1(5), 528

[11]

Chen J, Chen Z, Boussaid F, et al. Ultra-low-power smart electronic nose system based on three-dimensional tin oxide nanotube arrays. ACS Nano, 2018, 12(6), 6079

[12]

Peng G, Tisch U, Adams O, et al. Diagnosing lung cancer in exhaled breath using gold nanoparticles. Nature Nanotechnol, 2009, 4(10), 669

[13]

Fahad H M, Hiroshi H, Amani M, et al. Room temperature multiplexed gas sensing using chemical-sensitive 3.5-nm-thin silicon transistors. Sci Adv, 2017, 3, e1602557

[14]

Penza M, Cassano G. Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks. Food Chem, 2004, 86(2), 283

[15]

Capelli L, Sironi S, Del Rosso R. Electronic noses for environmental monitoring applications. Sensors, 2014, 14(11), 19979

[16]

Hakim M, Broza Y Y, Barash O, et al. Volatile organic compounds of lung cancer and possible biochemical pathways. Chem Rev, 2012, 112, 5949

[17]

Konvalina G, Haick H. Sensors for breath testing: from nanomaterials to comprehensive disease detection. Accounts Chem Res, 2014, 47(1), 66

[18]

Peng G, Hakim M, Broza Y Y, et al. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. British J Cancer, 2010, 103(4), 542-551

[19]

Derek R M, Sheikh A A, Patricia A M. nanoscale metal oxide-based heterojunctions for gas sensing: a review. Sens Actuators B, 2014, 204, 250

[20]

Chen X, Wong C K Y, Yuan C A, et al. Nanowire-based gas sensors. Sens Actuators B, 2013, 177, 178

[21]

Yeow J T W, Wang Y. A review of carbon nanotubes-based gas sensors. J Sens, 2009, 2009, 493904

[22]

Abdellah A, Abdelhalim A, Loghin F, et al. Flexible carbon nanotube based gas sensors fabricated by large-scale spray deposition. IEEE Sens J, 2013, 13(10), 4014

[23]

Park H J, Kim W J, Lee H K, et al. Highly flexible, mechanically stable, and sensitive NO2 gas sensors based on reduced graphene oxide nanofibrous mesh fabric for flexible electronics. Sens Actuators B, 2018, 257, 846

[24]

Peng P, Zhao X J, Pan X F, et al. Gas classification using deep convolutional neural networks. Sensors, 2018, 18, 157

[25]

Yoon J W, Lee J H. Toward breath analysis on a chip for disease diagnosis using semiconductor-based chemiresistors: Recent progress and future perspectives. Lab on a Chip, 2017, 17(21), 3537

[26]

Yang Z, Sassa F, Hayashi K. A robot equipped with a high-speed LSPR gas sensor module for collecting spatial odor information from on-ground invisible odor sources. ACS Sens, 2018, 3(6), 1174

[27]

Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553), 436

[28]

Donahue J, Hendricks L A, Rohrbach M, et al. Long-term recurrent convolutional networks for visual recognition and description. IEEE Trans Patt Anal Mach Intell, 2017, 39(4), 677

[29]

Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning. Nature, 2015, 518(7540), 529

[30]

Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks, 1-16. Retrieved from http://arxiv.org/abs/1511.06434

[31]

He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, 770

[32]

Lledo P M, Gheusi G, Vincent J D. Information processing in the mammalian olfactory system. Physiol Rev, 2005, 85, 281

[33]

Korsching S I. Olfactory receptors. In: Encyclopedia of Biological Chemistry. 2nd ed. 2003, 61, 149

[34]

Suslick K S, Rakow N A, Sen A. Colorimetric sensor arrays for molecular recognition. Tetrahedron, 2004, 60, 11133

[35]

Nugroho F A A, Darmadi I, Cusinato L, et al. Metal-polymer hybrid nanomaterials for plasmonic ultrafast hydrogen detection. Nat Mater, 2019, 18(5), 489

[36]

Gan T, Hu S. Electrochemical sensors based on graphene materials. Microchimica Acta, 2011, 175, 1

[37]

Pavia L, Lampman G M, Kritz G S, et al. Introduction to organic laboratory techniques. 4th ed. Thomson Brooks/Cole, 2006, 797

[38]

Liu H, Zhang L, Li K H H, et al. Microhotplates for metal oxide semiconductor gas sensor applications-towards the CMOS-MEMS monolithic approach. Micromachines, 2018, 9(11), 557

[39]

Bodenh?fer K, Hierlemann A, Noetzel G, et al. Performances of mass-sensitive devices for gas sensing: Thickness shear mode and surface acoustic wave transducers. Analyt Chem, 1996, 68(13), 2210

[40]

Punetha D, Pandey S K. CO gas sensor based on e-beam evaporated ZnO, MgZnO, and CdZnO thin films: a comparative study. IEEE Sens J, 2019, 19(7), 2450

[41]

Di Giulio M, D’Amico A, Di Natale C, et al. SnO2 thin films for gas sensor prepared by RF reactive sputtering. Sens Actuators B, 1995, 25(1), 465

[42]

Park J, Mun J, Shin J S, et al. Highly sensitive two dimensional MoS2 gas sensor decorated with Pt nanoparticles. Royal Soc Open Sci, 2018, 5(12), 1

[43]

Kim I, Choi W Y. Hybrid gas sensor having TiO2 nanotube arrays and SnO2 nanoparticles. Int J Nanotechnol, 2017, 14, 155

[44]

Guo B, Bermak A, Chan P C, et al. Characterization of integrated tin oxide gas sensors with metal additives and ion implantations. IEEE Sens J, 2008, 8, 1397

[45]

Yamazoe N. Grain size effects on gas sensitivity of porous SnO2-based elements. Sens Actuat B, 1991, 3, 147

[46]

Huang J, Wan Q. Gas sensors based on semiconducting metal oxide one-dimensional nanostructures. Sensors, 1991, 9(12), 9903

[47]

Seiyama T, Futada H, Era F, et al. Gas detection by activated semiconductive sensor. Denki Kagaku, 1972, 40(3), 244

[48]

Shankar P, Bosco J, Rayappan B. Gas sensing mechanism of metal oxides: The role of ambient atmosphere, type of semiconductor and gases - A review. Sci Lett J, 2015, 4, 126

[49]

Wang C, Yin L, Zhang L, et al. Metal oxide gas sensors: Sensitivity and influencing factors. Sensors, 2010, 10(3), 2088

[50]

Das S, Jayaraman V. SnO2: A comprehensive review on structures and gas sensors. Prog Mater Sci, 2014, 66, 112

[51]

Xu F, Ho H. Light-activated metal oxide gas sensors- a review. Micromachine, 2017, 8, 333

[52]

Fan S W, Srivastava A K, Dravid V P. UV-activated room-temperature gas sensing mechanism of polycrystalline ZnO. Appl Phys Lett, 2009, 95, 142106

[53]

Korotcenkov G. Metal oxides for solid-state gas sensors: what determines our choice. Mater Sci Eng B, 2007, 139, 1

[54]

Jin C G, Kurzawski P, Hierlemann A, et al. Evaluation of multitransducer arrays for the determination of organic vapor mixtures. Anal Chem, 2008, 80, 227

[55]

Wang D, Ma Z, Dai S, et al. Low-temperature synthesis of tunable mesoporous crystalline transition metal oxides and applications as Au catalyst supports. J Phys Chem C, 2008, 112, 13499

[56]

Korotcenkov G, Brinzari V, Boris Y, et al. Surface Pd doping influence on gas sensing characteristics of SnO2 thin films deposited by spray pyrolysis. Thin Solid Films, 2003, 436, 119

[57]

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Z S Chen, Z Chen, Z L Song, W H Ye, Z Y Fan, Smart gas sensor arrays powered by artificial intelligence[J]. J. Semicond., 2019, 40(11): 111601. doi: 10.1088/1674-4926/40/11/111601.

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Manuscript received: 01 August 2019 Manuscript revised: 23 August 2019 Online: Accepted Manuscript: 29 September 2019 Uncorrected proof: 04 November 2019 Published: 08 November 2019

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