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Recent Advances in Gas Sensors
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Recent Advances in Gas Sensors

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

Deadline for manuscript submissions: 30 August 2026 | Viewed by 1878

Special Issue Editors

Prof. Dr. Cristian Fabrega Gallego

E-Mail Website
Guest Editor
Departament de Ciència de Materials i Química Física, Universitat de Barcelona, c/Martí i Franquès 1, E-08028 Barcelona, Spain
Interests: gas sensors; IoT; IAQ; RISC-V; chemometrics; colorimetry; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Caroline Duc

E-Mail Website
Guest Editor
Centre for Education, Research and Innovation in Energy Environment (CERI EE), Institut Mines-Télécom Nord Europe (IMT Nord Europe), 59508 Douai, France
Interests: conductive polymer; organic gas sensors; chemiresitive sensors; printing electronic; organic electronic

Special Issue Information

Dear Colleagues,

Gas sensors are at the core of a wide range of applications, from environmental monitoring and industrial safety to healthcare and smart cities. This Special Issue aims to showcase the latest breakthroughs in gas sensing technologies, materials, and integration strategies. We welcome contributions that highlight novel sensing mechanisms, advanced materials (e.g., nanostructures, MOFs, and 2D materials), data-driven approaches (e.g., AI-assisted calibration or selectivity), sensor miniaturization, and system-level innovations. Both fundamental studies and applied research are encouraged, especially those addressing selectivity, stability, and real-world deployment challenges.

Prof. Dr. Cristian Fabrega Gallego
Dr. Caroline Duc
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 250 words) can be sent to the Editorial Office for assessment.

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 sensing
  • nanomaterials
  • sensor selectivity
  • chemoresistive sensors
  • optical gas sensors
  • electrochemical sensors
  • AI-assisted calibration
  • VOC detection
  • environmental monitoring
  • IoT-enabled gas sensors
  • sensor stability
  • e-nose
  • multi-sensors array
  • field calibration

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Published Papers (2 papers)

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Research

29 pages, 23295 KB  
Article
Improving the Robustness of Odour Recognition with Odour-Image Data Fusion in Open-Air Settings
by Fanny Monori and Alin Tisan
Sensors 2026, 26(8), 2493; https://doi.org/10.3390/s26082493 - 17 Apr 2026
Viewed by 305
Abstract
Odour recognition with low-cost gas sensors is challenging in open-air settings due to the non-specificity of the sensors and environmental variability. This can be mitigated by incorporating additional information into the classification process. This paper investigates odour-image multimodality in two case-studies of increasing [...] Read more.
Odour recognition with low-cost gas sensors is challenging in open-air settings due to the non-specificity of the sensors and environmental variability. This can be mitigated by incorporating additional information into the classification process. This paper investigates odour-image multimodality in two case-studies of increasing complexity: banana ripening in open-air environment and strawberry ripening in a glasshouse environment. Data were collected using custom acquisition platforms equipped with cameras and MOX gas sensors operated with temperature modulation. For the visual modality, image classification (MobileNetV3) and object detection (YoloV5) models are trained. For the odour modality, established classical machine learning methods (Random Forest, XGBoost, SVM and Logistic Regression) and neural networks (1D-CNN, LSTM, MLP, and ELM) are employed. Each modality is analysed independently and together to critically assess scenarios in which combining modalities provides a clear advantage over using either modality alone. Results show that models trained on odour data achieve high accuracy in controlled environments but underperform in more dynamic open-air settings. Image-based models are sensitive to the image quality in all environments; however, they are more robust when deployed in different environments. Lastly, it is demonstrated that decision fusion consistently increases the accuracy, by as much as +12.36% in the banana ripening and +3.63% in the strawberry ripening scenario. Where decision fusion does not improve classification accuracy significantly, it is shown that the multimodal approach can still be leveraged to identify high-confidence predictions by selecting samples where both modalities agree on the label. Full article
(This article belongs to the Special Issue Recent Advances in Gas Sensors)
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21 pages, 13386 KB  
Article
Enhanced Gas Sensitivity Characteristics of NO2 Sensor Based on a Silicon Micropillar Design Strategy at Room Temperature
by Zhiyuan Zhang, An Ning, Jian-Jun Zhu, Yi-Yu Yue, Zhi-Qiang Fan and Sai Chen
Sensors 2025, 25(20), 6406; https://doi.org/10.3390/s25206406 - 17 Oct 2025
Cited by 1 | Viewed by 1028
Abstract
In this study, two types of gas sensors—silicone-based interdigital electrode and silicon micropillar sensors based on rGO and rGO/SnO2—were fabricated. Their gas-sensing performance was investigated at room temperature. First, interdigital electrodes of different channel widths were fabricated to investigate the impact [...] Read more.
In this study, two types of gas sensors—silicone-based interdigital electrode and silicon micropillar sensors based on rGO and rGO/SnO2—were fabricated. Their gas-sensing performance was investigated at room temperature. First, interdigital electrodes of different channel widths were fabricated to investigate the impact of the channel width parameter. Subsequently, the rGO/SnO2 doping ratio in the composite material was varied to identify the optimal composition for gas sensitivity. Additionally, triangular and square-arrayed silicon micropillar substrates were fabricated via photolithography and inductively coupled plasma etching. The rGO/SnO2-based gas sensor on a silicon micropillar substrate exhibited an ultra-high specific surface area. The triangular micropillar arrangement of rGO/SnO2-160 demonstrates the best performance, showing approximately 14% higher response and a 106 s reduction in response time compared with interdigital electrode sensors spray-coated with the same concentration of rGO/SnO2 when tested at room temperature under 250 ppm NO2. The optimized sensor achieves a detection limit as low as 5 ppm and maintains high responsiveness, even in conditions of 60% relative humidity (RH). Additionally, the repeatability, selectivity, and stability of the sensor were evaluated. Finally, structural and morphological characterization was conducted using XRD, SEM, TEM, and Raman spectroscopy, which confirmed the successful modification of rGO with SnO2. Full article
(This article belongs to the Special Issue Recent Advances in Gas Sensors)
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