A Smart Gas Sensor Using Machine Learning Algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review

Over the past decade, machine learning (ML) and artificial intelligence (AI) have attracted great interest in research and various practical applications. Currently, smart, fast, and high sensitivity with excellent selectivity are becoming increasingly interesting due to the high need for environmental safety and medical applications. The main challenge is to improve sensor selectivity, which requires the combination of interdisciplinary research areas to successfully develop smart gas/chemical sensing devices with better performance. In this review, we present a few principles of gas sensing based on low-cost interdigital electrodes (IDEs), such as electrochemical, resistive, capacitive, and acoustic sensors. In addition, the most important current methods for improving gas sensing performance, the different materials, the different techniques used to fabricate IDE gas sensors, and their advantages and limitations are presented. In addition, a comparison between different ML and AI algorithms for pattern recognition and classification algorithms is also discussed. The discussion then establishes application cases of smart ML algorithms, which provide efficient data processing methods, for the design of smart gas sensors that are highly selective. In addition, the challenges and limitations of ML in gas sensor applications are critically discussed. The study shows the importance of ML with the need for structural optimization to develop and improve smart, sensitive, and selective sensors.

[1]  Bin Wang,et al.  Research on the construction of portable electrochemical sensors for environmental compounds quality monitoring , 2023, Materials Today Advances.

[2]  Benzhen Li,et al.  Hierarchical porous NiO doped ZnO nanocomposite for formaldehyde gas sensor with high sensitivity, fast response/recovery and good selectivity , 2023, Surfaces and Interfaces.

[3]  A. Kalboussi,et al.  An electronic nose using conductometric gas sensors based on P3HT doped with triflates for gas detection using computational techniques (PCA, LDA, and kNN) , 2022, Journal of Materials Science: Materials in Electronics.

[4]  A. Baldi,et al.  Compact fluidic electrochemical sensor platform for on-line monitoring of chemical oxygen demand in urban wastewater , 2022, Chemical Engineering Journal.

[5]  D. Huo,et al.  Gas Recognition in E-Nose System: A Review , 2022, IEEE Transactions on Biomedical Circuits and Systems.

[6]  Iqbal H. Sarker.  Smart City Data Science: Towards data-driven smart cities with open research issues , 2022, Internet Things.

[7]  K. Kaliyaperumal,et al.  Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis , 2022, Security and Communication Networks.

[8]  Shaohua Wu,et al.  The Combination of Two-Dimensional Nanomaterials with Metal Oxide Nanoparticles for Gas Sensors: A Review , 2022, Nanomaterials.

[9]  A. Sarkar,et al.  PrGO decorated TiO2 nanoplates hybrid nanocomposite for augmented NO2 gas detection with faster gas kinetics under UV light irradiation , 2022, Sensors and Actuators B: Chemical.

[10]  M. C. Horrillo,et al.  Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2 , 2022, Sensors.

[11]  O. Kanoun,et al.  Development of an Efficient Voltammetric Sensor for the Monitoring of 4-Aminophenol Based on Flexible Laser Induced Graphene Electrodes Modified with MWCNT-PANI , 2022, Sensors.

[12]  A. A. Altaf,et al.  Advanced polymeric/inorganic nanohybrids: An integrated platform for gas sensing applications. , 2022, Chemosphere.

[13]  Yi-qun Wu,et al.  Ultrafast NH3 gas sensor based on phthalocyanine-optimized non-covalent hybrid of carbon nanotubes with pyrrole† , 2022, Sensors and Actuators B: Chemical.

[14]  P. Lugli,et al.  Flexible Screen-Printed Electrochemical Sensors Functionalized with Electrodeposited Copper for Nitrate Detection in Water , 2021, ACS Omega.

[15]  H. Haick,et al.  Techniques for Wearable Gas Sensors Fabrication , 2021, Sensors and Actuators B: Chemical.

[16]  Qiyuan Liu,et al.  Antimony, beryllium, cobalt, and vanadium in urban park soils in Beijing: Machine learning-based source identification and health risk-based soil environmental criteria. , 2021, Environmental pollution.

[17]  Senthil Murugan Nagarajan,et al.  Integration of IoT based Routing Process for Food Supply Chain Management in Sustainable Smart Cities , 2021, Sustainable Cities and Society.

[18]  Charu,et al.  Machine learning‐based model for prediction of power consumption in smart grid‐ smart way towards smart city , 2021, Expert Syst. J. Knowl. Eng..

[19]  Sulaiman A. Alarifi,et al.  A systematic review of data science and machine learning applications to the oil and gas industry , 2021, Journal of Petroleum Exploration and Production Technology.

[20]  A. Javed,et al.  Rapid Inkjet-Printed Miniaturized Interdigitated Electrodes for Electrochemical Sensing of Nitrite and Taste Stimuli , 2021, Micromachines.

[21]  Tong Zhang,et al.  Recent Progress of Nanostructured Sensing Materials from 0D to 3D: Overview of Structure-Property-Application Relationship for Gas Sensors. , 2021, Small methods.

[22]  A. Celzard,et al.  Gas sensing based on organic composite materials: Review of sensor types, progresses and challenges , 2021 .

[23]  Ricardo Cerri,et al.  Two-dimensional MoS2-based impedimetric electronic tongue for the discrimination of endocrine disrupting chemicals using machine learning , 2021, Sensors and Actuators B: Chemical.

[24]  Yi-Feng Lin,et al.  H2S-Sensing Studies Using Interdigitated Electrode with Spin-Coated Carbon Aerogel-Polyaniline Composites , 2021, Polymers.

[25]  Yanchao Wang,et al.  Unexpected and enhanced electrostatic adsorption capacity of oxygen vacancy-rich cobalt-doped In2O3 for high-sensitive MEMS toluene sensor , 2021 .

[26]  F. Keynia,et al.  Air pollution forecasting application based on deep learning model and optimization algorithm , 2021, Clean Technologies and Environmental Policy.

[27]  Uduak Idio Akpan,et al.  Review of classification algorithms with changing inter-class distances , 2021 .

[28]  Qiusheng He,et al.  Palm-Sized Laser Spectrometer with High Robustness and Sensitivity for Trace Gas Detection Using a Novel Double-Layer Toroidal Cell. , 2021, Analytical chemistry.

[29]  T. Hsueh,et al.  A La2O3-NPs/ZnO/MEMS SO2 Gas Sensor , 2021 .

[30]  Sanjeev Verma,et al.  Artificial intelligence in marketing: Systematic review and future research direction , 2021, Int. J. Inf. Manag. Data Insights.

[31]  A. Juliet,et al.  A computational study of a chemical gas sensor utilizing Pd–rGO composite on SnO2 thin film for the detection of NOx , 2021, Scientific reports.

[32]  Nathalie Redon,et al.  Facile Fabrication of an Ammonia-Gas Sensor Using Electrochemically Synthesised Polyaniline on Commercial Screen-Printed Three-Electrode Systems , 2020, Sensors.

[33]  H. Saha,et al.  Enhanced Response of Co-Planar MEMS Microheater-Based Methane Gas Sensor , 2020, IEEE Sensors Journal.

[34]  S. M. Moosavi,et al.  The Role of Machine Learning in the Understanding and Design of Materials , 2020, Journal of the American Chemical Society.

[35]  M. Niwano,et al.  Response characteristics of a highly sensitive gas sensor using a titanium oxide nanotube film decorated with platinum nanoparticles , 2020 .

[36]  A. Gandomi,et al.  Data science in economics: comprehensive review of advanced machine learning and deep learning methods , 2020, Mathematics.

[37]  Qi Zhou,et al.  Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature , 2020 .

[38]  Ranjit T. Koodali,et al.  Machine learning in experimental materials chemistry , 2020, Catalysis Today.

[39]  Shiwei Zhang,et al.  Impact of Improved Design on Knudsen Force for Micro Gas Sensor , 2020, Micromachines.

[40]  Weigen Chen,et al.  SWCNTs-based MEMS gas sensor array and its pattern recognition based on deep belief networks of gases detection in oil-immersed transformers , 2020, Sensors and Actuators B: Chemical.

[41]  Eduard Llobet,et al.  Metal Oxide Nanoparticle-Decorated Few Layer Graphene Nanoflake Chemoresistors for the Detection of Aromatic Volatile Organic Compounds , 2020, Sensors.

[42]  Abhishek Motayed,et al.  Nanowire-Based Sensor Array for Detection of Cross-Sensitive Gases Using PCA and Machine Learning Algorithms , 2020, IEEE Sensors Journal.

[43]  Shijie Cheng,et al.  Artificial Intelligence to Power the Future of Materials Science and Engineering , 2020, Adv. Intell. Syst..

[44]  Fadi Al-Turjman,et al.  Applications of Artificial Intelligence and Machine learning in smart cities , 2020, Comput. Commun..

[45]  S. Noor,et al.  Graphene-based Materials in Gas Sensor Applications: A Review , 2020 .

[46]  C. Zorman,et al.  Review—Inkjet Printing of Metal Structures for Electrochemical Sensor Applications , 2020, Journal of The Electrochemical Society.

[47]  H. Manuspiya,et al.  Fabrication of hybrid thin film based on bacterial cellulose nanocrystals and metal nanoparticles with hydrogen sulfide gas sensor ability. , 2020, Carbohydrate polymers.

[48]  M. E. Azim Araghi,et al.  Performance of interdigitated capacitive-type CO2 sensor based on polypyrrole/copper phthalocyanine nanocomposite , 2020, Journal of Materials Science: Materials in Electronics.

[49]  Ali Rodan,et al.  A Classification Model for Gas Drift problem , 2019, 2019 Sixth HCT Information Technology Trends (ITT).

[50]  Long Zhang,et al.  A nanocomposite consisting of ZnO decorated graphene oxide nanoribbons for resistive sensing of NO2 gas at room temperature , 2019, Microchimica Acta.

[51]  J. Covington,et al.  Resistance-Capacitance Gas Sensor Based on Fractal Geometry , 2019, Chemosensors.

[52]  Dong Liu,et al.  Bridging interdigitated electrodes by electrochemical-assisted deposition of graphene oxide for constructing flexible gas sensor , 2019, Sensors and Actuators B: Chemical.

[53]  Haibin Li,et al.  A Printed and Flexible NO2 Sensor Based on a Solid Polymer Electrolyte , 2019, Front. Chem..

[54]  Ziheng Li,et al.  Room-temperature high-performance ammonia gas sensor based on hydroxyapatite film , 2019, Materials Research Express.

[55]  S. Chatzandroulis,et al.  A fabrication process of flexible IDE capacitive chemical sensors using a two step lift-off method based on PVA patterning , 2019, Sensors and Actuators B: Chemical.

[56]  A. Agarwal,et al.  MEMS based tin oxide thin film gas sensor for diabetes mellitus applications , 2018, Microsystem Technologies.

[57]  M. A. Jabbar,et al.  Machine Learning in Healthcare: A Review , 2018, 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).

[58]  Pai Peng,et al.  Gas Classification Using Deep Convolutional Neural Networks , 2018, Sensors.

[59]  Eric C. Nallon,et al.  Graphene Nanoplatelet-Polymer Chemiresistive Sensor Arrays for the Detection and Discrimination of Chemical Warfare Agent Simulants. , 2017, ACS sensors.

[60]  Inkyu Park,et al.  Localized Liquid-Phase Synthesis of Porous SnO2 Nanotubes on MEMS Platform for Low-Power, High Performance Gas Sensors. , 2017, ACS applied materials & interfaces.

[61]  K. Yao,et al.  Dielectric nanocomposite of diphenylethylenediamine and P-type multi-walled carbon nanotube for capacitive carbon dioxide sensors , 2017 .

[62]  Heyu Yin,et al.  Rapid Measurement of Room Temperature Ionic Liquid Electrochemical Gas Sensor using Transient Double Potential Amperometry. , 2017, Sensors and actuators. B, Chemical.

[63]  Dongzhi Zhang,et al.  Room-temperature high-performance ammonia gas sensor based on layer-by-layer self-assembled molybdenum disulfide/zinc oxide nanocomposite film , 2017 .

[64]  Sachin Ahuja,et al.  Machine learning and its applications: A review , 2017, 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC).

[65]  Wenbo Ding,et al.  Design and fabrication of a MEMS-based gas sensor containing WO3 sensitive layer for detection of NO2 , 2017 .

[66]  P. Bhattacharyya,et al.  Resonant Frequency Tuning Technique for Selective Detection of Alcohols by TiO2 Nanorod-Based Capacitive Device , 2016, IEEE Transactions on Nanotechnology.

[67]  Abbes Amira,et al.  An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification , 2016, IEEE Sensors Journal.

[68]  Alberto J. Palma,et al.  Printed electrodes structures as capacitive humidity sensors: A comparison , 2016 .

[69]  P. Bhattacharyya,et al.  Ti/TiO2 Nanotube Array/Ti Capacitive Device for Non-polar Aromatic Hydrocarbon Detection , 2016, IEEE Transactions on Device and Materials Reliability.

[70]  Subhasis Chaudhuri,et al.  Optimal selection of SAW sensors for E-Nose applications , 2015 .

[71]  G. P. Alcantara,et al.  A short review of gas sensors based on interdigital electrode , 2015, 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI).

[72]  D. K. Aswal,et al.  Ammonia sensing properties of polyaniline/α-Fe2O3 hybrid nanocomposites , 2015 .

[73]  Jingjing Liu,et al.  Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil , 2014, J. Sensors.

[74]  G. Shi,et al.  Graphene-based gas sensors , 2013 .

[75]  Nguyen Van Hieu,et al.  Electrochemical synthesis of polyaniline nanowires on Pt interdigitated microelectrode for room temperature NH3 gas sensor application , 2012 .

[76]  Ingemar Lundström,et al.  Robust gas detection at sub ppm concentrations , 2011 .

[77]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[78]  Mahesh Pal,et al.  Ensemble Learning with Decision Tree for Remote Sensing Classification , 2007 .

[79]  William Stafford Noble,et al.  Support vector machine , 2013 .

[80]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[81]  B. Bhushan,et al.  Machine Learning Approaches for Smart City Applications: Emergence, Challenges and Opportunities , 2022, Intelligent Systems Reference Library.

[82]  Y. T. Mustafa,et al.  A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges , 2022, IEEE Access.

[83]  A. Sarkar,et al.  PrGO Decorated TiO 2 Nanoplates Hybrid Nanocomposite for Augmented NO 2 Gas Detection with Faster Gas Kinetics Under UV Light Irradiation , 2021, SSRN Electronic Journal.

[84]  Guangyu Xu,et al.  Benchmarking Small-Dataset Structure-Activity-Relationship Models for Prediction of Wnt Signaling Inhibition , 2020, IEEE Access.

[85]  Jie Xu,et al.  Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review , 2020 .

[86]  Bin Shen,et al.  A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis , 2020, IEEE Access.

[87]  Dhiya Al-Jumeily,et al.  A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science , 2019, Unsupervised and Semi-Supervised Learning.

[88]  S. Zeinali,et al.  Comparative investigation of interdigitated and parallel-plate capacitive gas sensors based on Cu-BTC nanoparticles for selective detection of polar and apolar VOCs indoors , 2019, Sensors and Actuators B: Chemical.

[89]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.