With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

https://www.mdpi.com/1424-8220/19/17/3760/pdf

Review on Smart Gas Sensing Technology.

Detection and quantification of hydrogen sulfide (H2S) gas is important as it influences directly human health, our environment, and operations of several industries including food and beverages, oil, construction, and medicine. It also acts as biomarker in diagnosis of halitosis at early stage. We report herein liquid exfoliated MoSe2 nanoflakes based stable chemiresistive H2S gas sensor which operate at moderate temperature of 200℃. The response of p-type MoSe2 gas sensor device (when operated in ambient environment) was found to be varying between 15.87%–53.04% when the concentration of H2S was varied between 50 ppb – 5.45 ppm. The response of the device decreases when the measurements were done in synthetic air environment and it varies between 7.13%–19.87% for the concentration range of 500 ppb - 5.45 ppm. The response (%), recovery rate (%), hysteresis, experimental lowest detection limit etc. of the device suggest that the device performs better when operated in ambient than in synthetic air which suggest its real time device application. The response time and recovery time of the sensor are 15 s and 43 s respectively for 100 ppb of H2S. The sensor performance was found to be highly repeatable with sensitivity of 5.57%/ppm of H2S. The theoretical limit of detection and limit of quantization of the device were found to be 6.73 ppb and 22.44 ppb respectively. Based on chemical analysis, a plausible mechanism based on charge transfer phenomenon has been proposed for this sensor.

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MoSe2 nanoflakes based chemiresistive sensors for ppb-level hydrogen sulfide gas detection

Conducting polymer-based nanostructures for gas sensors

Porous silicon/α-MoO3 nanohybrid based fast and highly sensitive CO2 gas sensors

Surface acoustic wave (SAW) gas sensors are of continuous development interest to researchers due to their sensitivity, short detection time, and reliability. Among the most used materials to achieve the sensitive film of SAW sensors are metal oxide semiconductors, which are highlighted by thermal and chemical stability, by the presence on their surface of free electrons and also by the possibility of being used in different morphologies. For different types of gases, certain metal oxide semiconductors are used, and ZnO is an important representative for this category of materials in the field of sensors. Having a great potential for the development of SAW sensors, the discussion related to the development of the sensitivity of metal oxide semiconductors, especially ZnO, by the synthesis method or by obtaining new materials, is suitable and necessary to have an overview of the latest results in this domain.

https://www.mdpi.com/1424-8220/20/18/5118/pdf

ZnO Metal Oxide Semiconductor in Surface Acoustic Wave Sensors: A Review

The development of a new type of hybrid material comprising naphthalene-based π-conjugated amine (NBA) and zinc oxide (ZnO) nanohybrid, grown in situ on polydimethylsiloxane (PDMS) flexible substra...

π-Conjugated Amine-ZnO Nanohybrids for the Selective Detection of CO2 Gas at Room Temperature

This paper demonstrates an electrochemically deposited hybrid gas sensor containing ZnO and functionalized oligo( $p$ -phenylenevinylene). The introduction of the organic moiety onto ZnO leads to an enhancement in the sensitivity. A systematic study using characterization techniques such as energy dispersive X-ray, field emission scanning electron microscope, and X-ray diffraction indicates the key role played by the lamellar structure. As a gas sensor, the response as high as 8.83 at 10 ppm of ammonia have been observed. Also, the excellent selectivity and the response time and recovery time of 9.8s and 17.3s, respectively, at room temperature can effectuate forthcoming high-performance hybrid gas sensor which could be cost-effective and environmentally benign.

Functionalized Oligo( $p$ -Phenylenevinylene) and ZnO-Based Nanohybrid for Selective Ammonia Sensing at Room Temperature

Transformation of Moo3nanobelts into Moo3 nanofibers with high surface defect has been achieved by the application of pulsed temperature during hydrothermal growth. Moo3nanobelts and nanofibers are characterized by field emission scanning electron microscopy (FESEM), x-ray diffraction (XRD), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HR-TEM), and surface area analysis by nitrogen adsorption-desorption technique. Results clearly revealed that high crystalline, lower thickness, high surface area, and high surface defects are present in the Moo3 nanofibers as compare to Moo3 nanobelts. Furthermore, ethanol sensing properties of these M003 nanobelts and Moo3 nanofibers were examined, where Moo3 nanofibers show superior ethanol sensing properties due to the above mention advantages in Moo3 nanofibers.

Defect Control in MoO 3 Nanostructures as Ethanol Sensor

In this report, we present a method for improving selectivity in methanol sensing, using MoO 3 micrograss encapsulated ZnO microcube based composite. MoO 3 micrograss and ZnO microcubes are synthesized by the conventional hydrothermal method, after that, a solution based synthesis approach to synthesize MoO 3 micrograss encapsulated ZnO microcube. Grown materials are characterized by FESEM, XRD, EDX, and BET surface area analysis by nitrogen adsorption-desorption technique. Volatile organic compounds sensing performance of these three type material are tested. Results indicate that MoO 3 micrograss encapsulated ZnO microcube composite has higher sensitivity and selectivity towards methanol compare to its individual component. Improvement in sensing performance in MoO 3 micrograss encapsulated ZnO microcube is attributed to the unique morphology and presence of n-n heterojunction between ZnO microcube and MoO 3 micrograss.

Daya Shankar Sharma

Papers

π-Conjugated Amine-ZnO Nanohybrids for the Selective Detection of CO2 Gas at Room Temperature

Functionalized Oligo( $p$ -Phenylenevinylene) and ZnO-Based Nanohybrid for Selective Ammonia Sensing at Room Temperature

Defect Control in MoO 3 Nanostructures as Ethanol Sensor

Enhancement in Methanol Selectivity Using MoO 3 Micrograss Encapsulated Zno Microcube