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Nanomaterial-Optimized Device Construction and AI-Enhanced Signal Analysis of Gas Sensors
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Nanomaterial-Optimized Device Construction and AI-Enhanced Signal Analysis of Gas Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Materials".

Deadline for manuscript submissions: 10 March 2026 | Viewed by 4006

Special Issue Editors

Dr. Tao Wang

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Guest Editor
Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: MEMS sensors; gas sensors; pattern recognition; machine learning; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Tetsuya Shimamura

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Guest Editor
Graduate School of Science and Engineering, Saitama University, Saitama 338-8570, Japan
Interests: signal processing for voice; image; communication and sensors
Prof. Dr. Jia Yan

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: machine olfaction; electronic noses; sensor array signal processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Mingzhi Jiao

E-Mail Website
Guest Editor
CUMT-IoT perception Mine Research Center, China University of Mining and Technology, Xuzhou 221000, China
Interests: electrochemical electrode; battery safety monitoring; gas sensors and sensing systems; sensing signal recognition; low-power and high-performance sensors and sensor arrays
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of gas sensors, as the core hardware components of artificial olfactory systems for odor digitization, has been a continuous hotspot in both academic research and industrial applications in recent years. The most widely used semiconductor-based gas sensors have been suffering from characteristics of poor selectivity, excessive power consumption, and poor stability, which severely limit their use in quantitative analysis, high-reliability scenarios, and small smart devices. The rise of nanofunctional materials and artificial intelligence technologies in recent decades has delivered new approaches and further possibilities to address the above gas sensor-associated dilemmas. Novel nanomaterials, such as MXenes, TMDs, MOFs, COFs, etc., have attracted significant attention from many scholars and demonstrated their outstanding properties in gas sensing applications, such as controllable specific modifications, flexibility, and room-temperature applications. Artificial intelligence techniques, especially deep learning methods that have emerged in recent years, have proven to be powerful tools for further enhancing sensor performance and expanding their applications. This Special Issue will focus on the latest research progress in two aspects: One is the modulation mechanism of nanosensitive materials on the response and selectivity of gas sensors. The other is the enhancement of data analysis methods for encoding and decoding output signals. We anticipate that this Special Issue will discuss the development of gas sensors from a range of unique perspectives and inspire the innovation of multidisciplinary technologies to promote the advancement of sensor technologies for a sustainable future.

We are looking forward to your contributions.

Dr. Tao Wang
Dr. Tetsuya Shimamura
Dr. Jia Yan
Dr. Mingzhi Jiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • gas sensor
  • chemiresistive sensor
  • nanomaterials
  • constructing heterojunctions
  • two-dimensional materials
  • electronic sensitization
  • machine learning
  • deep learning
  • electronic nose
  • signal processing

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Published Papers (3 papers)

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Research

14 pages, 9159 KiB  
Article
Copper Nanoclusters Anchored on Crumpled N-Doped MXene for Ultra-Sensitive Electrochemical Sensing
by Hanxue Yang, Chao Rong, Shundong Ge, Tao Wang, Bowei Zhang and Fu-Zhen Xuan
Sensors 2025, 25(8), 2508; https://doi.org/10.3390/s25082508 - 16 Apr 2025
Viewed by 195
Abstract
Simultaneous detection of dopamine (DA) and uric acid (UA) is essential for diagnosing neurological and metabolic diseases but hindered by overlapping electrochemical signals. We present an ultrasensitive electrochemical sensor using copper nanoclusters anchored on nitrogen-doped crumpled Ti3C2Tx MXene [...] Read more.
Simultaneous detection of dopamine (DA) and uric acid (UA) is essential for diagnosing neurological and metabolic diseases but hindered by overlapping electrochemical signals. We present an ultrasensitive electrochemical sensor using copper nanoclusters anchored on nitrogen-doped crumpled Ti3C2Tx MXene (Cu-N/Ti3C2Tx). The engineered 3D crumpled architecture prevents MXene restacking, exposes active sites, and enhances ion transport, while Cu nanoclusters boost electrocatalytic activity via accelerated electron transfer. Structural analyses confirm uniform Cu dispersion (3.0 wt%), Ti-N bonding, and strain-induced wrinkles, synergistically improving conductivity. The sensor achieves exceptional sensitivity (1958.3 and 1152.7 μA·mM−1·cm−2 for DA/UA), ultralow detection limits (0.058 and 0.099 μM for DA/UA), rapid response (<1.5 s), and interference resistance (e.g., ascorbic acid). Differential pulse voltammetry enables independent linear detection ranges (DA: 2–60 μM; UA: 5–100 μM) in biofluids, with 94.4% stability retention over 7 days. The designed sensor exhibits excellent capabilities for DA and UA detection. This work provides a novel design strategy for developing high-performance electrochemical sensors. Full article
(This article belongs to the Special Issue Nanomaterial-Optimized Device Construction and AI-Enhanced Signal Analysis of Gas Sensors)
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14 pages, 3549 KiB  
Article
Pulse-Driven MEMS NO2 Sensors Based on Hierarchical In2O3 Nanostructures for Sensitive and Ultra-Low Power Detection
by Haixia Mei, Fuyun Zhang, Tingting Zhou and Tong Zhang
Sensors 2024, 24(22), 7188; https://doi.org/10.3390/s24227188 - 9 Nov 2024
Cited by 3 | Viewed by 1334
Abstract
As the mainstream type of gas sensors, metal oxide semiconductor (MOS) gas sensors have garnered widespread attention due to their high sensitivity, fast response time, broad detection spectrum, long lifetime, low cost, and simple structure. However, the high power consumption due to the [...] Read more.
As the mainstream type of gas sensors, metal oxide semiconductor (MOS) gas sensors have garnered widespread attention due to their high sensitivity, fast response time, broad detection spectrum, long lifetime, low cost, and simple structure. However, the high power consumption due to the high operating temperature limits its application in some application scenarios such as mobile and wearable devices. At the same time, highly sensitive and low-power gas sensors are becoming more necessary and indispensable in response to the growth of the environmental problems and development of miniaturized sensing technologies. In this work, hierarchical indium oxide (In2O3) sensing materials were designed and the pulse-driven microelectromechanical system (MEMS) gas sensors were also fabricated. The hierarchical In2O3 assembled with the mass of nanosheets possess abundant accessible active sites. In addition, compared with the traditional direct current (DC) heating mode, the pulse-driven MEMS sensor appears to have the higher sensitivity for the detection of low-concentrations of nitrogen dioxide (NO2). The limit of detection (LOD) is as low as 100 ppb. It is worth mentioning that the average power consumption of the sensor is as low as 0.075 mW which is one three-hundredth of that in the DC heating mode. The enhanced sensing performances are attributed to loose and porous structures and the reducing desorption of the target gas driven by pulse heating. The combination of morphology design and pulse-driven strategy makes the MEMS sensors highly attractive for portable equipment and wearable devices. Full article
(This article belongs to the Special Issue Nanomaterial-Optimized Device Construction and AI-Enhanced Signal Analysis of Gas Sensors)
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15 pages, 9682 KiB  
Article
In Situ Growth of COF/PVA-Carrageenan Hydrogel Using the Impregnation Method for the Purpose of Highly Sensitive Ammonia Detection
by Xiyu Chen, Min Zeng, Tao Wang, Wangze Ni, Jianhua Yang, Nantao Hu, Tong Zhang and Zhi Yang
Sensors 2024, 24(13), 4324; https://doi.org/10.3390/s24134324 - 3 Jul 2024
Cited by 1 | Viewed by 1697
Abstract
Flexible ammonia (NH3) gas sensors have gained increasing attention for their potential in medical diagnostics and health monitoring, as they serve as a biomarker for kidney disease. Utilizing the pre-designable and porous properties of covalent organic frameworks (COFs) is an innovative [...] Read more.
Flexible ammonia (NH3) gas sensors have gained increasing attention for their potential in medical diagnostics and health monitoring, as they serve as a biomarker for kidney disease. Utilizing the pre-designable and porous properties of covalent organic frameworks (COFs) is an innovative way to address the demand for high-performance NH3 sensing. However, COF particles frequently encounter aggregation, low conductivity, and mechanical rigidity, reducing the effectiveness of portable NH3 detection. To overcome these challenges, we propose a practical approach using polyvinyl alcohol-carrageenan (κPVA) as a template for in the situ growth of two-dimensional COF film and particles to produce a flexible hydrogel gas sensor (COF/κPVA). The synergistic effect of COF and κPVA enhances the gas sensing, water retention, and mechanical properties. The COF/κPVA hydrogel shows a 54.4% response to 1 ppm NH3 with a root mean square error of less than 5% and full recovery compared to the low response and no recovery of bare κPVA. Owing to the dual effects of the COF film and the particles anchoring the water molecules, the COF/κPVA hydrogel remained stable after 70 h in atmospheric conditions, in contrast, the bare κPVA hydrogel was completely dehydrated. Our work might pave the way for highly sensitive hydrogel gas sensors, which have intriguing applications in flexible electronic devices for gas sensing. Full article
(This article belongs to the Special Issue Nanomaterial-Optimized Device Construction and AI-Enhanced Signal Analysis of Gas Sensors)
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