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Recent Advances in Sensors for Chemical Detection Applications
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Recent Advances in Sensors for Chemical Detection Applications

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 5210

Special Issue Editor

Dr. Michele Penza

E-Mail Website
Guest Editor
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability, Division of Sustainable Materials, Laboratory Functional Materials and Technologies for Sustainable Applications, Brindisi Research Center, km 706, Strada Statale 7, Appia, I-72100 Brindisi, Italy
Interests: sensor materials; functional materials; gas sensors; air quality sensor systems; sensor technology development; environmental measurements; urban air quality sensor networks; smart cities applications; environmental sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Chemical detection based on low-cost sensor technologies has become increasingly popular for several emerging applications, such as industrial process control, chemical threat monitoring, green chemistry, environmental sustainability, smart cities, hydrogen economy, energy saving, wearable devices, IoT applications, public health protection, sustainable mobility, autonomous vehicles, and community sensing.

Functional materials are cross-cutting technologies used for chemical detection to provide advanced gas sensors at a laboratory level and real-world testing in many industrial applications. Low-power consumption, high-quality data, and optimal performance are some important parameters used for a new generation of low-cost chemical sensors. Portable sensor systems and wireless sensor networks are typical approaches used to monitor chemical threats in long-term operation.

Current low-cost sensor technologies include numerous types of transducers, such as chemiresistor, electrochemical, transistor, optical, mass-sensitive, catalytic, and other hybrid configurations, evolving quickly with different open questions and considerable challenges, such as sensitivity, selectivity, stability, detection limits, calibration, accuracy, and so on. Understanding the limitations and capabilities of current low-cost sensor technologies for chemical detection is a key issue for future applications.

This Special Issue will focus on low-cost sensor technology, gas sensors, chemical sensors, advanced active materials, sensor nodes, hardware innovations, data communications, system integration, sensor testing, processing/corrections algorithms, machine learning, new solutions, and applications for chemical detection issues. Proper calibration techniques of chemical sensors are necessary, both in laboratory and field applications. Wireless sensor networks will be considered in the context of chemical detection applications.

In this Special Issue, we kindly invite front-line scientists to submit original researches and review articles on Recent Advances in Sensors for Chemical Detection Applications.

Potential topics include, but are not limited to, the following:

  • Gas sensors;
  • Chemical detection;
  • Advanced materials for chemical sensing;
  • Novel gas sensor materials;
  • Sensor calibration;
  • Sensor systems;
  • Machine Learning algorithms;
  • Wireless sensor networks;
  • Chemical threats monitoring;
  • Environmental measurements;
  • Sensors for smart city applications;
  • Sensors for environmental sustainability;
  • Sensors for energy applications;
  • Sensors for IoT applications;
  • Sensors for industrial applications;
  • Sensors for sustainable mobility;
  • Case-studies of chemical detection campaigns;
  • New concepts and trends in chemical sensing.

Prof. Dr. Michele Penza
Guest Editor

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 sensors
  • chemical sensors
  • sensor active materials
  • advanced functional nanomaterials
  • portable chemical sensor-systems
  • chemical sensor modelling
  • IoT devices
  • machine learning engineering
  • chemical sensor applications
  • new concepts in chemical sensing

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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

13 pages, 4728 KiB  
Article
Graphene/TiO2 Heterostructure Integrated with a Micro-Lightplate for Low-Power NO2 Gas Detection
by Paniz Vafaei, Margus Kodu, Harry Alles, Valter Kiisk, Olga Casals, Joan Daniel Prades and Raivo Jaaniso
Sensors 2025, 25(2), 382; https://doi.org/10.3390/s25020382 - 10 Jan 2025
Viewed by 401
Abstract
Low-power gas sensors that can be used in IoT (Internet of Things) systems, consumer devices, and point-of-care devices will enable new applications in environmental monitoring and health protection. We fabricated a monolithic chemiresistive gas sensor by integrating a micro-lightplate with a 2D sensing [...] Read more.
Low-power gas sensors that can be used in IoT (Internet of Things) systems, consumer devices, and point-of-care devices will enable new applications in environmental monitoring and health protection. We fabricated a monolithic chemiresistive gas sensor by integrating a micro-lightplate with a 2D sensing material composed of single-layer graphene and monolayer-thick TiO2. Applying ultraviolet (380 nm) light with quantum energy above the TiO2 bandgap effectively enhanced the sensor responses. Low (<1 μW optical) power operation of the device was demonstrated by measuring NO2 gas at low concentrations, which is typical in air quality monitoring, with an estimated limit of detection < 0.1 ppb. The gas response amplitudes remained nearly constant over the studied light intensity range (1–150 mW/cm2) owing to the balance between the photoinduced adsorption and desorption processes of the gas molecules. The rates of both processes followed an approximately square-root dependence on light intensity, plausibly because the electron–hole recombination of photoinduced charge carriers is the primary rate-limiting factor. These results pave the way for integrating 2D materials with micro-LED arrays as a feasible path to advanced electronic noses. Full article
(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)
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Figure 1

Figure 1
<p>(<b>a</b>) Schematic cross-section of the device, (<b>b</b>) photograph of the μLP with a magnified area of interdigitated electrodes, and (<b>c</b>) sequence of sensor layer fabrication on the μLP.</p> Full article ">Figure 2
<p>Schematic illustration of the gas sensing setup.</p> Full article ">Figure 3
<p>(<b>a</b>) Image of the μLP with a working LED, its above-threshold (<b>b</b>) volt–ampere characteristic, (<b>c</b>) electroluminescence (EL) spectrum, and (<b>d</b>) dependence of the μLP optical power and surface intensity on the applied electrical power.</p> Full article ">Figure 4
<p>(<b>a</b>,<b>b</b>) SEM images of CVD graphene on μLP. (<b>c</b>) Raman spectra of graphene before and after the PLD of TiO<sub>2</sub>. (<b>d</b>) SEM image of the sensor material after coating the graphene with a TiO<sub>2</sub> nanolayer.</p> Full article ">Figure 5
<p>(<b>a</b>) Dynamic responses of pristine graphene to NO<sub>2</sub> gas at concentrations of 20, 50, and 150 ppb at different irradiation intensities on μLP. (<b>b</b>) The same for the Gr/TiO<sub>2</sub> μLP sensor, recorded in the dark and under incremental UV illumination with the μLED optical power of 0.46, 1.9, and 5.5 μW (corresponding to 0.8, 3.3, and 9.7 mW/cm<sup>2</sup>). Synthetic air was used as the background gas. (<b>c</b>) Sensor conductance during the exposures to 150 ppb of NO<sub>2</sub> gas at different levels of μLP optical power. The power levels in μW units are shown in the gray area at the bottom. Synthetic air with a relative humidity (RH) of 20% was used as the background gas.</p> Full article ">Figure 6
<p>The dependence of average response and recovery rates on light intensity. Approximations with power functions and power exponents of the intensity (I) dependence are shown in red. The inset shows the response curves approximated with Equation (5).</p> Full article ">Figure 7
<p>Relative responses to (<b>a</b>) 150 ppb of NO<sub>2</sub> at different levels of relative humidity and (<b>b</b>) different toxic gases at concentrations as indicated.</p> Full article ">
14 pages, 2867 KiB  
Article
Non-Invasive Malaria Detection in Sub-Saharan Africa Using a DNA-Based Sensor System
by Trine Juul-Kristensen, Celine Thiesen, Line Wulff Haurum, Josephine Geertsen Keller, Romeo Wenceslas Lendamba, Rella Zoleko Manego, Madeleine Eunice Betouke Ongwe, Birgitta Ruth Knudsen, Eduardo Pareja, Eduardo Pareja-Tobes, Rodrigo Labouriau, Ghyslain Mombo-Ngoma and Cinzia Tesauro
Sensors 2024, 24(24), 7947; https://doi.org/10.3390/s24247947 - 12 Dec 2024
Viewed by 832
Abstract
Malaria poses a serious global health problem, with half the world population being at risk. Regular screening is crucial for breaking the transmission cycle and combatting the disease spreading. However, current diagnostic tools relying on blood samples face challenges in many malaria-epidemic areas. [...] Read more.
Malaria poses a serious global health problem, with half the world population being at risk. Regular screening is crucial for breaking the transmission cycle and combatting the disease spreading. However, current diagnostic tools relying on blood samples face challenges in many malaria-epidemic areas. In the present study, we demonstrate the detection of the malaria-causing Plasmodium parasite in non-invasive saliva samples (N = 61) from infected individuals by combining a DNA-based Rolling-circle-Enhanced-Enzyme-Activity-Detection (REEAD) sensor system with a chemiluminescence readout that could be detected with an in-house-developed affordable and battery-powered portable reader. We successfully transferred the technology to sub-Saharan Africa, where the malaria burden is high, and demonstrated a proof of concept in a small study (N = 40) showing significant differences (p < 0.00001) between malaria-positive individuals (N = 33) and presumed asymptomatic negative individuals (N = 7) all collected in Gabon. This is the first successful application of the REEAD sensor system for the detection of malaria in saliva in a high-epidemic area and holds promise for the potential future use of REEAD for malaria diagnosis or surveillance based on non-invasive specimens in sub-Saharan Africa. Full article
(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)
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<p>The REEAD sensor system. (<b>A</b>) The advantage of using pTOP1 as a biomarker for the detection of <span class="html-italic">Plasmodium</span> infections. Each parasite contains a high number of pTOP1 enzymes that each generate multiple DNA products without being consumed in the process. (<b>B</b>) The top panel shows the sequence and structure of the pTOP1-specific DNA substrate with the primer annealing site shown in blue. Cleavage–ligation by pTOP1 converts the substrate to a closed circle that is hybridized to a glass slide and amplified by RCA in the presence of (i) dNTPs with biotin-conjugated dCTPs for chemiluminescence readout (<b>left lower panel</b>) or (ii) without modified dNTPs followed by hybridization to fluorescently labeled probes for readout in a fluorescence microscope (<b>right lower panel</b>).</p> Full article ">Figure 2
<p>Detection of pfTOP1 with chemiluminescence REEAD. (<b>A</b>,<b>B</b>) Bar charts showing the results of analyzing 1, 0.5, 0.25, 0.125, 0.0625, 0.03125 ng/µL pfTOP1 spiked in saliva by REEAD using the fluorescence readout detected using a microscope (<b>A</b>) or the chemiluminescence readout detected using a commercial CCD camera (<b>B</b>). As controls, samples without pfTOP1 (one with and one without saliva) were included. As a positive control, a sample with 1 ng/µL pfTOP1 without saliva was used. The identity of the samples is indicated below the bar charts. The experiments were performed in triplicates (indicated by each dot). To compensate for slide-to-slide variations, the signals obtained by either the microscope or chemiluminescence readout were normalized to the average of the samples with saliva and 0 ng/µL pfTOP1 and plotted as mean +/− standard deviation (SD). (<b>C</b>) Graphic depiction of the results obtained when testing the effect of removing unreacted DNA substrate before chemiluminescence readout. The identity of the samples is shown below the graph. Each experiment was repeated four to six times (indicated by dots). To compensate for slide-to-slide variations, the chemiluminescence REEAD signals were normalized to the average intensity of samples without pfTOP1 without exonuclease digestion (Exo) and plotted as mean +/− SD.</p> Full article ">Figure 3
<p>Detection of <span class="html-italic">Plasmodium</span> in clinical saliva samples. (<b>A</b>) (<b>Left panel</b>) Graphical depiction of chemiluminescence REEAD results obtained when measuring extracts from two saliva samples from confirmed malaria positives and two saliva samples from presumed negative individuals prepared by 2–5 vortex repetitions with glass beads. The average of the results from two individual experiments is shown by horizontal lines. (<b>Right panel</b>) Raw data obtained with a CCD camera. (<b>B</b>) The results were obtained by analyzing 30 saliva samples from confirmed malaria-positive individuals and 31 saliva samples from presumed malaria negatives using chemiluminescence REEAD. The average of the results is shown by horizontal lines. Statistics are shown in <a href="#app1-sensors-24-07947" class="html-app">Supplementary Materials S1A</a>. To compensate for slide-to-slide variations, the chemiluminescence REEAD signals were normalized to the average of the signals obtained by analyzing negative samples vortexed 2 times (<b>A</b>) or to the average of the signals obtained by analyzing negative samples (<b>B</b>).</p> Full article ">Figure 4
<p>Comparison of CCD camera and portable chemiluminescence reader (VPCIReader). (<b>A</b>) Left panel, schematic showing the construction of the chemiluminescence VPCIReader. Right panel, photo of the VPCIReader. (<b>B</b>,<b>C</b>) Bar charts showing the results of analyzing titrations of test DNA circles (diluted 0 to 32 times and indicated) by capturing the results of chemiluminescence REEAD by a commercially available CCD camera (<b>B</b>) or by the developed chemiluminescence VPCIReader. The readings of each of the three individual experiments are shown by dots. The sample marked “Neg” contains non-circularized DNA with a sequence matching the test DNA circles. To compensate for slide-to-slide variations, the chemiluminescence REEAD signals were normalized to the “Neg” sample and plotted as mean +/− SD. **** = <span class="html-italic">p</span> &lt; 0.0001, ordinary one-way ANOVA. “E” refers to an empty well only containing 5′-Amine REEAD primer.</p> Full article ">Figure 5
<p>Detection of <span class="html-italic">Plasmodium</span> in clinical saliva samples by using VPCIReader. The results obtained by analyzing 33 saliva samples from confirmed malaria-positive individuals and 7 saliva samples from presumed malaria negatives using chemiluminescence REEAD. The average of the results is shown by horizontal lines. Statistics are shown in <a href="#app1-sensors-24-07947" class="html-app">Supplementary Materials S1B</a>. To compensate for slide-to-slide variations, the chemiluminescence REEAD signals were normalized to a well only containing 5′-Amine REEAD primer.</p> Full article ">
16 pages, 4785 KiB  
Article
Room-Temperature Ammonia Sensing Using Polyaniline-Coated Laser-Induced Graphene
by José Carlos Santos-Ceballos, Foad Salehnia, Frank Güell, Alfonso Romero, Xavier Vilanova and Eduard Llobet
Sensors 2024, 24(23), 7832; https://doi.org/10.3390/s24237832 - 7 Dec 2024
Viewed by 1899
Abstract
The reliable detection of ammonia at room temperature is crucial for not only maintaining environmental safety but also for reducing the risks of hazardous pollutants. In this study, the electrochemical modification of laser-induced graphene (LIG) with polyaniline (PANI) led to the development of [...] Read more.
The reliable detection of ammonia at room temperature is crucial for not only maintaining environmental safety but also for reducing the risks of hazardous pollutants. In this study, the electrochemical modification of laser-induced graphene (LIG) with polyaniline (PANI) led to the development of a chemo-resistive nanocomposite (PANI@LIG) for detecting ammonia levels at room temperature. The composite is characterized by field emission scanning electron microscopy, Fourier transforms infrared, and Raman and X-ray photoelectron spectroscopy. This work marks the first utilization of PANI@LIG for gas sensing and introduces a simple but effective approach for fabricating low-cost wearable gas sensors with high sensitivity and flexibility. Full article
(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)
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Figure 1

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<p>Schematic of (<b>a</b>) fabrication process of the PANI@LIG gas sensor and (<b>b</b>) interactions between PANI and LIG.</p> Full article ">Figure 2
<p>Schematic illustration of measurement system used for gas sensing tests.</p> Full article ">Figure 3
<p>FESEM images of bare LIG (<b>a</b>,<b>b</b>), PANI@LIG (<b>c</b>–<b>f</b>).</p> Full article ">Figure 4
<p>Raman spectra of bare LIG and PANI@LIG (<b>a</b>); ATR-FTIR spectra of bare LIG and PANI@LIG (<b>b</b>).</p> Full article ">Figure 5
<p>X-ray photoelectron spectroscopy (XPS) survey spectra of PANI@LIG (<b>a</b>), and high-resolution spectra fitting results of C1s (<b>b</b>), N1s (<b>c</b>) and O1s (<b>d</b>) of PANI@LIG.</p> Full article ">Figure 6
<p>Gas sensing performance of PANI@LIG NCs gas sensors in dry ambient conditions. (<b>a</b>) Electrical resistance response to different concentrations (5, 10, 25, 50, and 100 ppm) of NH<sub>3</sub> at room temperature. (<b>b</b>) Regression curve. (<b>c</b>) Sensor repeatability testing at successive exposures of 25 ppm of NH<sub>3</sub>. (<b>d</b>) Response to 5 ppm of NH<sub>3</sub> and analysis of response/recovery time.</p> Full article ">Figure 7
<p>Calibration curves obtained for dry ambient conditions, 30%RH and 50%RH (<b>a</b>) and responses to different gas compounds (CO, C<sub>2</sub>H<sub>6</sub>O, C<sub>6</sub>H<sub>6</sub>, C<sub>7</sub>H<sub>8</sub>, NH<sub>3</sub>, H<sub>2</sub>, and NO<sub>2</sub>) (<b>b</b>).</p> Full article ">Figure 8
<p>Schematic of the interaction between ammonia and PANI@LIG.</p> Full article ">
20 pages, 1695 KiB  
Article
Comparison of Classical and Inverse Calibration Equations in Chemical Analysis
by Hsuan-Yu Chen and Chiachung Chen
Sensors 2024, 24(21), 7038; https://doi.org/10.3390/s24217038 - 31 Oct 2024
Viewed by 582
Abstract
Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique’s response and the target analyte’s standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials [...] Read more.
Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique’s response and the target analyte’s standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument’s response is dependent. The inverse equation is the opposite: the instrument’s response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument’s response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced. Full article
(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)
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Figure 1
<p>The distribution of the relative humidity data for reading values versus the standard humidity values for Vaisala HMP-143A capacitive sensors.</p> Full article ">Figure 2
<p>The distribution of the relative humidity data for reading values versus the standard humidity values for THT-B121 resistive sensors.</p> Full article ">Figure 3
<p>The distribution of the chloromethane data for the ratio of peak areas versus the standard concentrations for GC-MS.</p> Full article ">Figure 4
<p>The distribution of the albumin concentration data for the peak heights versus the standard concentrations with spectrophotometry.</p> Full article ">Figure 5
<p>The distribution of the standard deviation values of the response and the standard relative humidity values for two humidity sensors.</p> Full article ">Figure 6
<p>The distribution of the response’s CV values and the standard relative humidity values for two humidity sensors.</p> Full article ">
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