Smart Gas Sensors

Dear Colleagues,

Development in the field of gas sensors has witnessed exponential growth, with multitude of applications. These diverse applications have led to unexpected challenges. Recent advances in data science have addressed challenges such as sensitivity, selectivity, drift, aging, the limit of detection, and response time. Data-driven techniques such as regression models, classifiers, deep learning techniques, and machine learning, to name a few, have paved the way for converting raw sensor features into actual and meaningful information. The incorporation of modern data analysis involving artificial intelligence (AI) is poised to enable a self-sustaining gas-sensing infrastructure without human intervention. This is an exciting time to be working in gas sensors to derive solutions that continue to improve the ability to accurately sense and control our environment.

The goal of this Special Issue is to collect research focusing on accurate and field-ready gas sensors empowered by artificial intelligence and modern data analysis techniques. We invite investigators to contribute both original and review articles, covering the breadth and depth of the research and development of artificial-intelligence-enabled smart gas sensors. Potential topics include, but are not limited to, the following:

• Electrochemical, optical, chemiresistive gas sensors;
• Pattern recognition using machine learning and deep learning;
• Machine-learning-based denoising algorithms;
• AI-enabled sensor performance enhancement;
• Modern data analysis in gas sensors;
• Data-efficient transfer learning;
• Breath analysis advancements;
• Exhaled breath biomarker sensing;
• Sensors for hydrogen safety, agriculture, environment, and threat reduction;
• Field testing of gas sensors;
• Embedded AI-based sensor systems;
• Gas sensor life-cycle: design to implementation.

Dr. Praveen Sekhar
Dr. Alessia Di Gilio
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. Gases is an international peer-reviewed open access quarterly 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 1000 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.

• artificial intelligence
• machine learning
• neural network
• gas sensors
• sensitivity, selectivity
• response time
• drift
• data analysis
• field ready
• embedded
• smart electronics