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Advanced Sensors for Gas Monitoring: 2nd Edition

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

Deadline for manuscript submissions: 10 June 2026 | Viewed by 383

Special Issue Editors


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Guest Editor
Istituto Nazionale di Ricerca Metrologica, Turin, Italy
Interests: turbulence; metrology; flow measurement; flow calibration; particle image velocimetry; boundary layer; vorticity
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Istituto Nazionale di Ricerca Metrologica, Turin, Italy
Interests: metrology; flow measurement; flow calibration; anemometry; airspeed
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Gas measurement is a topic of long-lasting interest because of the extremely vast range of technological applications of gas flow, ranging from the exchange of energy to medical, industrial, and environmental applications. Flow measurement and analysis instruments are, therefore, in continuous development. This Special Issue will focus on the sensing elements of instruments used for measuring the flow rate and/or the composition of flowing gases. Additionally, developments in artificial intelligence are maturing, with important implications on the digital treatment of raw sensor output.

The topics of the Special Issue will include the following:

  • Developments and innovative applications of existing sensors;
  • New/improved mechanical flow rate sensors;
  • Developments in thermal sensors;
  • Developments in ultrasonic sensors;
  • Gas composition sensing elements;
  • Micro- and nano-sensors for flow rate and composition;
  • Integrated sensors;
  • “Lab-on-a-chip” applications;
  • Monitoring of gas energy content;
  • Algorithms for the elaboration of sensor outputs;
  • Sensors for atmospheric monitoring.

Papers concerning related topics can be included if deemed to be consistent with the general topic of the Special Issue. This Special Issue is devoted to developments in sensors for measuring gas flow rate and composition.

Dr. Pier Giorgio Spazzini
Dr. Aline Piccato
Dr. Francesca Rolle
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 flow rate
  • gas composition
  • energy vector
  • lab-on-a-chip
  • integration of sensors
  • digital elaboration
  • gas sensors
  • amount-of-substance

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Published Papers (1 paper)

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Research

22 pages, 3826 KB  
Article
Short-Term Forecast of Indoor CO2 Using Attention-Based LSTM: A Use Case of a Hospital in Greece
by Christos Mountzouris, Grigorios Protopsaltis and John Gialelis
Sensors 2025, 25(17), 5382; https://doi.org/10.3390/s25175382 - 1 Sep 2025
Viewed by 259
Abstract
Given the significant implications of indoor air pollution for physical and mental health, well-being and productivity, indoor air quality (IAQ) is of critical importance. CO2 is a prevalent indoor air contaminant and represents a key determinant for IAQ characterization. This study collected [...] Read more.
Given the significant implications of indoor air pollution for physical and mental health, well-being and productivity, indoor air quality (IAQ) is of critical importance. CO2 is a prevalent indoor air contaminant and represents a key determinant for IAQ characterization. This study collected sensed air pollution and climatic data from a hospital environment in Greece and employed Long Short-Term Memory (LSTM) neural network variants with progressively increased architectural complexity to predict indoor CO2 concentration across future horizons ranging from 15 min up to 180 min. Among the examined variants, the attention-based LSTM exhibited the most consistent performance across the forecasting horizons. Incorporating additional predictors reflecting climatic conditions, air pollution and occupancy status within the hospital settings, the multivariate attention-based LSTM further enhanced its predictive performance with an MAE of 8.9 ppm, 16.7 ppm, 31.2 ppm, 38.9 and 39.5 ppm for 15 min, 30 min, 60 min, 120 min, and 180 min ahead, respectively. Full article
(This article belongs to the Special Issue Advanced Sensors for Gas Monitoring: 2nd Edition)
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