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Novel Gas Sensing Approaches: From Fabrication to Application

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A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Applied Chemical Sensors".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 3561

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Special Issue Editors

Dr. Guillem Domènech-Gil

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Guest Editor

Department of Electronics and Biomedical Engineering, University of Barcelona, Barcelona, Spain
Interests: electronic noses; machine learning; volatile organic compounds; greenhouse gases; chemical sensors

Dr. Donatella Puglisi

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Guest Editor

Division of Applied Sensor Science, Department of Physics, Chemistry and Biology, Linköping University, SE-58183 Linköping, Sweden
Interests: gas sensor systems; volatile organic compounds; hazardous chemicals; digital technologies; machine learning; applied research
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Special Issue Information

Dear Colleagues,

Novel methodological approaches and technological proofs of concept are the seed for the advancement of gas sensing research. These approaches can lead not simply to an incremental improvement but to a true paradigm shift with far-reaching impacts on society. Moreover, they have the crucial role of addressing existing limitations related to, for example, accuracy, device integration, miniaturization, power consumption, and costs and pave the way for new applications with expanded functionalities, such as wearable devices or remote and mobile environmental monitoring. Pushing the boundaries is what drives progress towards more advanced, efficient, and versatile gas sensor devices.

This Special Issue seeks review articles and original, innovative interdisciplinary research papers that could have a large societal impact and showcase the recent advancements in gas sensing. We welcome the submission of papers concerning the following topics, though this list is not exhaustive:

Proof of concept and development of new sensing materials;
Novel gas sensor fabrication and integration methodologies;
Improved gas sensor operation strategies;
The implementation of Internet of Things (IoT) and effective data management;
The implementation of AI/machine learning algorithms and advanced data processing;
Air pollutant monitoring in indoor and outdoor environments;
The deployment and evaluation of sensor systems and sensor networks;
Innovative applications with large societal impacts.

Dr. Guillem Domènech-Gil
Dr. Donatella Puglisi
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. Chemosensors is an international peer-reviewed open access monthly 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 2000 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

smart sensors
VOCs
machine learning
environmental monitoring
artificial intelligence
Internet of Things
air quality
leakage detection
industrial safety
public health and well-being

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Published Papers (2 papers)

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12 pages, 1897 KB

Open AccessArticle

Significance of Ammonia Dopant in the Analysis of Formaldehyde Solution and Its Headspace by Corona Discharge-Ion Mobility Spectrometry

by Vahideh Ilbeigi, Younes Valadbeigi and Štefan Matejčík

Chemosensors 2026, 14(5), 105; https://doi.org/10.3390/chemosensors14050105 - 1 May 2026

Viewed by 289

Abstract

Formalin, a commercial aqueous solution typically containing 37% formaldehyde, often includes a few percent methanol to inhibit polymerization. Nevertheless, formaldehyde readily forms polymerization products such as glycols, dimethoxy (acetal), and methoxyalcohol (hemiacetal) derivatives, making their analysis important. In this work, we employ ion mobility spectrometry (IMS) for qualitative and quantitative detection of these species and demonstrate that analysis is not feasible using the standard IMS reactant ion, H₃O⁺(H₂O)_n. Protonation by H₃O⁺(H₂O)_n induces loss of water or methanol, preventing stable detection of the intact derivatives. Hence, ammonia was introduced as a dopant to replace H₃O⁺(H₂O)_n with NH₄⁺(H₂O)_n in the ionization region, thereby shifting the ionization mechanism from proton transfer to ammonium attachment. A high-temperature injection port was also designed to enable the analysis of both liquid samples and their corresponding headspace. Using the developed method, we identified both acetal and hemiacetal derivatives in commercial formaldehyde solution, while only the more volatile acetal species were detected in the headspace. Quantitative analysis yielded a limit of detection (LOD) of 1.9 ppm and a linear range of 5.5–120 ppm for solution measurements. Importantly, the method provides reliable detection in the presence of substantial humidity, an environment in which many polymer-based sensors fail due to severe moisture interference. Overall, ammonia-doped IMS offers a robust and humidity-tolerant platform for characterizing formaldehyde polymerization products in both the gas and liquid phases. Full article

(This article belongs to the Special Issue Novel Gas Sensing Approaches: From Fabrication to Application)

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11 pages, 5810 KB

Open AccessArticle

Reading Dye-Based Colorimetric Inks: Achieving Color Consistency Using Color QR Codes

by Ismael Benito-Altamirano, Laura Engel, Ferran Crugeira, Miriam Marchena, Jürgen Wöllenstein, Joan Daniel Prades and Cristian Fàbrega

Chemosensors 2024, 12(12), 260; https://doi.org/10.3390/chemosensors12120260 - 13 Dec 2024

Cited by 3 | Viewed by 2239

Abstract

Color consistency when reading colorimetric sensors is a key factor for this technology. Here, we demonstrate how the usage of machine-readable patterns, like QR codes, can be used to solve the problem. We present our approach of using back-compatible color QR codes as colorimetric sensors, which are common QR codes that also embed a set of hundreds of color references as well as colorimetric indicators. The method allows locating the colorimetric sensor within the captured scene and to perform automated color correction to ensure color consistency regardless of the hardware used. To demonstrate it, a

C O_{2}

-sensitive colorimetric indicator was printed on top of a paper-based substrate using screen printing. This indicator was formulated for Modified Atmosphere Packaging (MAP) applications. To verify the method, the sensors were exposed to several environmental conditions (both in gas composition and light conditions). And, images were captured with an 8M pixel digital camera sensor, similar to those used in smartphones. Our results show that the sensors have a relative error of 9% when exposed with a

C O_{2}

concentration of 20%. This is a good result for low-cost disposable sensors that are not intended for permanent use. However, as soon as light conditions change (2500–6500 K), this error increases up to $ϵ_{20}$ = 440% (rel. error at 20%

C O_{2}

concentration) rendering the sensors unusable. Within this work, we demonstrate that our color QR codes can reduce the relative error to $ϵ_{20}$ = 14%. Furthermore, we show that the most common color correction, white balance, is not sufficient to address the color consistency issue, resulting in a relative error of $ϵ_{20}$ = 90%. Full article