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Chemical Sensors for Measurement Systems
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Chemical Sensors for Measurement Systems

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

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 19872

Special Issue Editors

Prof. Dr. Marco Mugnaini

E-Mail Website
Guest Editor
Department of Information Engineering and Mathematics, University of Siena, Via Roma, 56, 53100 Siena, Italy
Interests: availability; reliability; safety; sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ada Fort

E-Mail Website
Guest Editor
Department of Information Engineering and Mathematics, University of Siena, Via Roma, 56, 53100 Siena, Italy
Interests: electronics and measurement systems; sensors; sensors modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proposed Special Issue will aim to address advancements in chemical sensors research field and to spread the most recent outcomes on gas and liquid detection and detection systems. At present, technology is pushing the market to face new challenges in terms of materials, sensitivity, long-term stability, and re-usability of chemical gas sensors and platforms. Researchers can present their most recent findings and discoveries to make the scientific community aware of the most updated technologies and performance related to chemical sensing.

Chemical measurement system reliability evaluation and industrial exploitation are welcome in this issue to try to link research areas with practical exploitation of results to allow for easy technology transfer. Testing protocols and sensor modeling are also hot topics which can allow a better knowledge of materials and fabrication processes, allowing a more confident use of new emerging technologies.

Prof. Dr. Marco Mugnaini
Prof. Dr. Ada Fort
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

  • chemical sensors
  • new materials
  • sensing systems
  • sensor modelling
  • sensors reliability
  • wireless chemical sensors
  • industrial applications
  • sensors for safety applications
  • new sensing principles

Published Papers (7 papers)

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Research

13 pages, 5506 KiB  
Article
A Distributed IoT Air Quality Measurement System for High-Risk Workplace Safety Enhancement
by Lorenzo Parri, Marco Tani, David Baldo, Stefano Parrino, Elia Landi, Marco Mugnaini and Ada Fort
Sensors 2023, 23(11), 5060; https://0-doi-org.brum.beds.ac.uk/10.3390/s23115060 - 25 May 2023
Cited by 2 | Viewed by 1025
Abstract
The safety of an operator working in a hazardous environment is a recurring topic in the technical literature of recent years, especially for high-risk environments such as oil and gas plants, refineries, gas depots, or chemical industries. One of the highest risk factors [...] Read more.
The safety of an operator working in a hazardous environment is a recurring topic in the technical literature of recent years, especially for high-risk environments such as oil and gas plants, refineries, gas depots, or chemical industries. One of the highest risk factors is constituted by the presence of gaseous substances such as toxic compounds such as carbon monoxide and nitric oxides, particulate matter or indoors, in closed spaces, low oxygen concentration atmospheres, and high concentrations of CO2 that can represent a risk for human health. In this context, there exist many monitoring systems for lots of specific applications where gas detection is required. In this paper, the authors present a distributed sensing system based on commercial sensors aimed at monitoring the presence of toxic compounds generated by a melting furnace with the aim of reliably detecting the insurgence of dangerous conditions for workers. The system is composed of two different sensor nodes and a gas analyzer, and it exploits commercial low-cost commercially available sensors. Full article
(This article belongs to the Special Issue Chemical Sensors for Measurement Systems)
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13 pages, 1556 KiB  
Article
An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology
by Juan Liu, Yang Luo, Liang Ge, Wen Zeng, Ziyang Rao and Xiaoting Xiao
Sensors 2022, 22(21), 8460; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218460 - 03 Nov 2022
Cited by 3 | Viewed by 4751
Abstract
Since drunk driving poses a significant threat to road traffic safety, there is an increasing demand for the performance and dependability of online drunk driving detection devices for automobiles. However, the majority of current detection devices only contain a single sensor, resulting in [...] Read more.
Since drunk driving poses a significant threat to road traffic safety, there is an increasing demand for the performance and dependability of online drunk driving detection devices for automobiles. However, the majority of current detection devices only contain a single sensor, resulting in a low degree of detection accuracy, erroneous judgments, and car locking. In order to solve the problem, this study firstly designed a sensor array based on the gas diffusion model and the characteristics of a car steering wheel. Secondly, the data fusion algorithm is proposed according to the data characteristics of the sensor array on the steering wheel. The support matrix is used to improve the data consistency of the single sensor data, and then the adaptive weighted fusion algorithm is used for multiple sensors. Finally, in order to verify the reliability of the system, an online intelligent detection device for drunk driving based on multi-sensor fusion was developed, and three people using different combinations of drunk driving simulation experiments were conducted. According to the test results, a drunk person in the passenger seat will not cause the system to make a drunk driving determination. When more than 50 mL of alcohol is consumed and the driver is seated in the driver’s seat, the online intelligent detection of drunk driving can accurately identify drunk driving, and the car will lock itself as soon as a real-time online voice prompt is heard. This study enhances and complements theories relating to data fusion for online automobile drunk driving detection, allowing for the online identification of drivers who have been drinking and the locking of their vehicles to prevent drunk driving. It provides technical support for enhancing the accuracy of online systems that detect drunk driving in automobiles. Full article
(This article belongs to the Special Issue Chemical Sensors for Measurement Systems)
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18 pages, 1748 KiB  
Article
A Statistical Analysis of Response and Recovery Times: The Case of Ethanol Chemiresistors Based on Pure SnO2
by Andrea Ponzoni
Sensors 2022, 22(17), 6346; https://doi.org/10.3390/s22176346 - 23 Aug 2022
Cited by 3 | Viewed by 1973
Abstract
Response and recovery times are among the most important parameters for gas sensors. Their optimization has been pursued through several strategies, including the control over the morphology of the sensitive material. The effectiveness of these approaches is typically proven by comparing different sensors [...] Read more.
Response and recovery times are among the most important parameters for gas sensors. Their optimization has been pursued through several strategies, including the control over the morphology of the sensitive material. The effectiveness of these approaches is typically proven by comparing different sensors studied in the same paper under the same conditions. Additionally, tables comparing the results of the considered paper with those available in the literature are often reported. This is fundamental to frame the results of individual papers in a more general context; nonetheless, it suffers from the many differences occurring at the experimental level between different research groups. To face this issue, in the present paper, we adopt a statistical approach to analyze the response and recovery times reported in the literature for chemiresistors based on pure SnO2 for ethanol detection, which was chosen as a case study owing to its available statistic. The adopted experimental setup (of the static or dynamic type) emerges as the most important parameter. Once the statistic is split into these categories, morphological and sensor-layout effects also emerge. The observed results are discussed in terms of different diffusion phenomena whose balance depends on the testing conditions adopted in different papers. Full article
(This article belongs to the Special Issue Chemical Sensors for Measurement Systems)
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24 pages, 7265 KiB  
Article
Modeling the Conductivity Response to NO2 Gas of Films Based on MWCNT Networks
by Ada Fort, Marco Mugnaini, Enza Panzardi, Anna Lo Grasso, Ammar Al Hamry, Anurag Adiraju, Valerio Vignoli and Olfa Kanoun
Sensors 2021, 21(14), 4723; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144723 - 10 Jul 2021
Cited by 9 | Viewed by 2703
Abstract
This work proposes a model describing the dynamic behavior of sensing films based on functionalized MWCNT networks in terms of conductivity when exposed to time-variable concentrations of NO2 and operating with variable working temperatures. To test the proposed model, disordered networks of [...] Read more.
This work proposes a model describing the dynamic behavior of sensing films based on functionalized MWCNT networks in terms of conductivity when exposed to time-variable concentrations of NO2 and operating with variable working temperatures. To test the proposed model, disordered networks of MWCNTs functionalized with COOH and Au nanoparticles were exploited. The model is derived from theoretical descriptions of the electronic transport in the nanotube network, of the NO2 chemisorption reaction and of the interaction of these two phenomena. The model is numerically implemented and then identified by estimating all the chemical/physical quantities involved and acting as parameters, through a model fitting procedure. Satisfactory results were obtained in the fitting process, and the identified model was used to further the analysis of the MWCNT sensing in dynamical conditions. Full article
(This article belongs to the Special Issue Chemical Sensors for Measurement Systems)
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13 pages, 4507 KiB  
Article
Fully Transparent and Sensitivity-Programmable Amorphous Indium-Gallium-Zinc-Oxide Thin-Film Transistor-Based Biosensor Platforms with Resistive Switching Memories
by Hyeong-Un Jeon and Won-Ju Cho
Sensors 2021, 21(13), 4435; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134435 - 28 Jun 2021
Cited by 2 | Viewed by 2393
Abstract
This paper presents a fully transparent and sensitivity-programmable biosensor based on an amorphous-indium-gallium-zinc-oxide (a-IGZO) thin-film transistor (TFT) with embedded resistive switching memories (ReRAMs). The sensor comprises a control gate (CG) and a sensing gate (SG), each with a resistive switching (RS) [...] Read more.
This paper presents a fully transparent and sensitivity-programmable biosensor based on an amorphous-indium-gallium-zinc-oxide (a-IGZO) thin-film transistor (TFT) with embedded resistive switching memories (ReRAMs). The sensor comprises a control gate (CG) and a sensing gate (SG), each with a resistive switching (RS) memory connected, and a floating gate (FG) that modulates the channel conductance of the a-IGZO TFT. The resistive coupling between the RS memories connected to the CG and SG produces sensitivity properties that considerably exceed the limit of conventional ion-sensitive field-effect transistor (ISFET)-based sensors. The resistances of the embedded RS memories were determined by applying a voltage to the CG–FG and SG–FG structures independently and adjusting the compliance current. Sensors constructed using RS memories with different resistance ratios yielded a pH sensitivity of 50.5 mV/pH (RCG:RSG = 1:1), 105.2 mV/pH (RCG:RSG = 2:1), and 161.9 mV/pH (RCG:RSG = 3:1). Moreover, when the RCG:RSG = 3:1, the hysteresis voltage width (VH) and drift rate were 54.4 mV and 32.9 mV/h, respectively. As the increases in VH and drift rate are lower than the amplified sensitivity, the sensor performs capably. The proposed device is viable as a versatile sensing device capable of detecting various substances, such as cells, antigens, DNA, and gases. Full article
(This article belongs to the Special Issue Chemical Sensors for Measurement Systems)
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12 pages, 12511 KiB  
Article
Highly Sensitive and Selective Sodium Ion Sensor Based on Silicon Nanowire Dual Gate Field-Effect Transistor
by Seong-Kun Cho and Won-Ju Cho
Sensors 2021, 21(12), 4213; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124213 - 19 Jun 2021
Cited by 10 | Viewed by 3123
Abstract
In this study, a highly sensitive and selective sodium ion sensor consisting of a dual-gate (DG) structured silicon nanowire (SiNW) field-effect transistor (FET) as the transducer and a sodium-selective membrane extended gate (EG) as the sensing unit was developed. The SiNW channel DG [...] Read more.
In this study, a highly sensitive and selective sodium ion sensor consisting of a dual-gate (DG) structured silicon nanowire (SiNW) field-effect transistor (FET) as the transducer and a sodium-selective membrane extended gate (EG) as the sensing unit was developed. The SiNW channel DG FET was fabricated through the dry etching of the silicon-on-insulator substrate by using electrospun polyvinylpyrrolidone nanofibers as a template for the SiNW pattern transfer. The selectivity and sensitivity of sodium to other ions were verified by constructing a sodium ion sensor, wherein the EG was electrically connected to the SiNW channel DG FET with a sodium-selective membrane. An extremely high sensitivity of 1464.66 mV/dec was obtained for a NaCl solution. The low sensitivities of the SiNW channel FET-based sodium ion sensor to CaCl2, KCl, and pH buffer solutions demonstrated its excellent selectivity. The reliability and stability of the sodium ion sensor were verified under non-ideal behaviors by analyzing the hysteresis and drift. Therefore, the SiNW channel DG FET-based sodium ion sensor, which comprises a sodium-selective membrane EG, can be applied to accurately detect sodium ions in the analyses of sweat or blood. Full article
(This article belongs to the Special Issue Chemical Sensors for Measurement Systems)
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15 pages, 9993 KiB  
Article
Characterization and Neural Modeling of a Microwave Gas Sensor for Oxygen Detection Aimed at Healthcare Applications
by Zlatica Marinković, Giovanni Gugliandolo, Mariangela Latino, Giuseppe Campobello, Giovanni Crupi and Nicola Donato
Sensors 2020, 20(24), 7150; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247150 - 13 Dec 2020
Cited by 18 | Viewed by 2727
Abstract
The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO2). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is [...] Read more.
The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO2). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is electrically characterized using the complex scattering parameters measured with a vector network analyzer (VNA). The experimental investigation is performed over a frequency range of 1.5 GHz to 2.9 GHz by placing the sensor inside a polytetrafluoroethylene (PTFE) test chamber with a binary gas mixture composed of oxygen and nitrogen. The frequency-dependent response of the sensor is investigated in detail and further modelled using an artificial neural network (ANN) approach. The proposed modelling procedure allows mimicking the measured sensor performance over the whole range of oxygen concentration, going from 0% to 100%, and predicting the behavior of the resonant frequencies that can be used as sensing parameters. Full article
(This article belongs to the Special Issue Chemical Sensors for Measurement Systems)
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