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Multisensor Arrays for Environmental Monitoring
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Multisensor Arrays for Environmental Monitoring

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

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 40850

Special Issue Editor

Prof. Dr. Victor Sysoev

E-Mail Website
Guest Editor
Department of Physics, Yuri Gagarin State Technical University of Saratov, 410054 Saratov, Russia
Interests: chemiresistor; multisensor array; gas sensor; electronic nose; oxide nanostructures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Trends to develop devices mimicking all the mammalian senses have produced both basic and applied research in corresponding directions since the 20th century. So far, we have widely employed sensor units, which have yielded signals regarding electromagnetic radiation (vision), acoustic waves (audition), pressure, temperature, and motion (somatosensation). However, gustation, and olfaction in particular, are extremely difficult to simulate for machine detection due to a variety of substances and interference effects. In many tasks where the selectivity is not demanded as much, chemical sensors have found a market niche because of the high sensitivity obtained recently due to great success in material science and micro- and nano-electronics technologies. To approach the selectivity issue in the same way as the human olfaction system, we have used vector signals or patterns generated by multisensor arrays or single sensors operated under varying conditions. Analyte-specific multisensor patterns are processed by corresponding algorithms recently developed by information technologies. However, s so far these multisensor units have not found a significant market that requires new breakthroughs in the field.

Therefore, we invite applicants to look over the recent advances in multisensor arrays and call for innovative works that explore frontiers and challenges in the field.

The topics of interest include but are not limited to the following:

  • Fundamentals of multisensor arrays
  • Multisensor array technologies
  • Emerging materials for multisensor arrays
  • Integration of sensors to multisensor arrays, features, and challenges
  • Innovative pattern recognition approaches to multisensor signals
  • Interfaces for multisensor arrays
  • Packaging for multisensor array chips
  • Multisensor array networks and IoT
  • Applications of multisensor arrays and artificial intelligence

Prof. Dr. Victor Sysoev
Guest Editor

Manuscript Submission Information

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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

  • Multisensor array
  • Electronic nose
  • Electronic tongue
  • Chemical sensor
  • Nanotechnology
  • Pattern recognition
  • Artificial intelligence

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Published Papers (5 papers)

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Research

Jump to: Review

8 pages, 1558 KiB  
Article
CO2 and O2 Detection by Electric Field Sensors
by Marco Santonico, Alessandro Zompanti, Anna Sabatini, Luca Vollero, Simone Grasso, Carlo Di Mezza and Giorgio Pennazza
Sensors 2020, 20(3), 668; https://doi.org/10.3390/s20030668 - 25 Jan 2020
Cited by 9 | Viewed by 4884
Abstract
In this work an array of chemical sensors for gas detection has been developed, starting with a commercial sensor platform developed by Microchip (GestIC), which is normally used to detect, trace, and classify hand movements in space. The system is based on electric [...] Read more.
In this work an array of chemical sensors for gas detection has been developed, starting with a commercial sensor platform developed by Microchip (GestIC), which is normally used to detect, trace, and classify hand movements in space. The system is based on electric field changes, and in this work, it has been used as mechanism revealing the adsorption of chemical species CO2 and O2. The system is composed of five electrodes, and their responses were obtained by interfacing the sensors with an acquisition board based on an ATMEGA 328 microprocessor (Atmel MEGA AVR microcontroller). A dedicated measurement chamber was designed and prototyped in acrylonitrile butadiene styrene (ABS) using an Ultimaker3 3D printer. The measurement cell size is 120 × 85 mm. Anthocyanins (red rose) were used as a sensing material in order to functionalize the sensor surface. The sensor was calibrated using different concentrations of oxygen and carbon dioxide, ranging from 5% to 25%, mixed with water vapor in the range from 50% to 90%. The sensor exhibits good repeatability for CO2 concentrations. To better understand the sensor response characteristics, sensitivity and resolution were calculated from the response curves at different working points. The sensitivity is in the order of magnitude of tens to hundreds of µV/% for CO2, and of µV/% in the case of O2. The resolution is in the range of 10−1%–10−3% for CO2, and it is around 10−1% for O2. The system could be specialized for different fields, for environmental, medical, and food applications. Full article
(This article belongs to the Special Issue Multisensor Arrays for Environmental Monitoring)
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Figure 1

Figure 1
<p>(<b>a</b>) Frame Shape electrodes, (<b>b</b>) electrode system with characteristic capacitance (V<sub>Tx</sub>: Tx electrode voltage; V<sub>Rx</sub>: Rx electrode voltage; C<sub>RxTx</sub>: capacitance between the receive and transmit electrodes; C<sub>TxG</sub>: capacitance of the transmit (Tx) electrode to the system ground; C<sub>RxG</sub>: capacitance of the receive (Rx) electrode to the system ground; C<sub>H</sub>: capacitance between the receive electrode and the hand (earth ground); e<sub>Rx</sub>: Rx electrode; e<sub>Tx</sub>: Tx electrode).</p> Full article ">Figure 2
<p>Measurement setup.</p> Full article ">Figure 3
<p>Schematic representation of the sensing mechanism and transduction.</p> Full article ">Figure 4
<p>Raw sensor responses are shown: functionalized and non-functionalized sensors were exposed to different CO<sub>2</sub> and O<sub>2</sub> concentrations with a fixed relative humidity (RH) level of 50%. Each measurement lasted 2 minutes and each recovery phase lasted 5 minutes.</p> Full article ">Figure 5
<p>Sensor response to CO<sub>2</sub>: calibration data points were fitted using linear models reported in <a href="#sensors-20-00668-t001" class="html-table">Table 1</a>. Each error bar has been calculated with mean value and standard deviation based on five measurements.</p> Full article ">Figure 6
<p>Sensor response to O<sub>2</sub>: calibration data points were fitted using linear models reported in <a href="#sensors-20-00668-t002" class="html-table">Table 2</a>. Each error bar has been calculated with mean value and standard deviation based on five measurements.</p> Full article ">
14 pages, 2548 KiB  
Article
Gas Sensing Properties of Perovskite Decorated Graphene at Room Temperature
by Juan Casanova-Cháfer, Rocío García-Aboal, Pedro Atienzar and Eduard Llobet
Sensors 2019, 19(20), 4563; https://doi.org/10.3390/s19204563 - 20 Oct 2019
Cited by 43 | Viewed by 6736
Abstract
This paper explores the gas sensing properties of graphene nanolayers decorated with lead halide perovskite (CH3NH3PbBr3) nanocrystals to detect toxic gases such as ammonia (NH3) and nitrogen dioxide (NO2). A chemical-sensitive semiconductor film [...] Read more.
This paper explores the gas sensing properties of graphene nanolayers decorated with lead halide perovskite (CH3NH3PbBr3) nanocrystals to detect toxic gases such as ammonia (NH3) and nitrogen dioxide (NO2). A chemical-sensitive semiconductor film based on graphene has been achieved, being decorated with CH3NH3PbBr3 perovskite (MAPbBr3) nanocrystals (NCs) synthesized, and characterized by several techniques, such as field emission scanning electron microscopy, transmission electron microscopy and X-ray photoelectron spectroscopy. Reversible responses were obtained towards NO2 and NH3 at room temperature, demonstrating an enhanced sensitivity when the graphene is decorated by MAPbBr3 NCs. Furthermore, the effect of ambient moisture was extensively studied, showing that the use of perovskite NCs in gas sensors can become a promising alternative to other gas sensitive materials, due to the protective character of graphene, resulting from its high hydrophobicity. Besides, a gas sensing mechanism is proposed to understand the effects of MAPbBr3 sensing properties. Full article
(This article belongs to the Special Issue Multisensor Arrays for Environmental Monitoring)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) HR-TEM image showing an example of the graphene layers size used. (<b>b</b>) HR-TEM image showing the graphene crystallinity. (<b>c</b>) Lead halide perovskites (MAPbBr3<sub>3</sub>) nanocrystals. (<b>d</b>) HR-TEM image showing the MAPbBr<sub>3</sub> NCs crystallinity.</p> Full article ">Figure 2
<p>(<b>a</b>) FESEM image showing the sensor surface composed only by graphene. (<b>b</b>) FESEM image recorded with Back-Scattered Electron (BSE) detector, showing the graphene (black background) decorated with perovskite nanocrystals (bright spots).</p> Full article ">Figure 3
<p>Deconvolution of the C 1s (<b>a</b>) and O 1s (<b>b</b>) core level peak for graphene.</p> Full article ">Figure 4
<p>Example of resistance response when detecting NO<sub>2</sub> at room temperature in the range of 250–1000 ppb (<b>a</b>) and 25–100 ppb (<b>b</b>). In both figures, black and red line corresponds to bare graphene and perovskite doped graphene, respectively. The concentration of NO<sub>2</sub> applied is shown in right-Y, represented by a blue dashed line.</p> Full article ">Figure 5
<p>Calibration curves obtained for bare graphene (black) and perovskite doped graphene (red) detecting NO<sub>2</sub> at ppb range (<b>a</b>). Stability study of the sensor based on graphene loaded with perovskite nanocrystals, 500 ppb of NO<sub>2</sub> are measured over a 6-month period (<b>b</b>).</p> Full article ">Figure 6
<p>Example of resistance response when detecting NH<sub>3</sub> at room temperature in ppm range (<b>a</b>). Calibration curves obtained for bare graphene and perovskite doped graphene, black and red lines, respectively (<b>b</b>).</p> Full article ">Figure 7
<p>Comparison of the response for 500 ppb of NO<sub>2</sub> under dry and humid (50% of relative humidity) conditions for bare and perovskite-decorated graphene.</p> Full article ">Figure 8
<p>Schematic illustration of perovskite decorated graphene sensor showing the mechanism proposed after interaction with different gases. Two adsorption processes are proposed, one at the graphene surface and another at the perovskite NCs. (<b>A</b>) During the exposure to an electron-donating gas, an excess of positive charges is neutralized at the defective perovskite surface and the local hole concentration of the p-type graphene is decreased, which results in an increase in film resistance. (<b>B</b>) During the exposure to an electron-withdrawing gas, positive charges (holes) in the NCs are formed, which are transferred to the graphene layers from the NCs, decreasing the overall resistance of the hybrid film.</p> Full article ">
13 pages, 3584 KiB  
Article
The Multisensor Array Based on Grown-On-Chip Zinc Oxide Nanorod Network for Selective Discrimination of Alcohol Vapors at Sub-ppm Range
by Anton Bobkov, Alexey Varezhnikov, Ilya Plugin, Fedor S. Fedorov, Vanessa Trouillet, Udo Geckle, Martin Sommer, Vladimir Goffman, Vyacheslav Moshnikov and Victor Sysoev
Sensors 2019, 19(19), 4265; https://doi.org/10.3390/s19194265 - 1 Oct 2019
Cited by 39 | Viewed by 4519
Abstract
We discuss the fabrication of gas-analytical multisensor arrays based on ZnO nanorods grown via a hydrothermal route directly on a multielectrode chip. The protocol to deposit the nanorods over the chip includes the primary formation of ZnO nano-clusters over the surface and secondly [...] Read more.
We discuss the fabrication of gas-analytical multisensor arrays based on ZnO nanorods grown via a hydrothermal route directly on a multielectrode chip. The protocol to deposit the nanorods over the chip includes the primary formation of ZnO nano-clusters over the surface and secondly the oxide hydrothermal growth in a solution that facilitates the appearance of ZnO nanorods in the high aspect ratio which comprise a network. We have tested the proof-of-concept prototype of the ZnO nanorod network-based chip heated up to 400 °C versus three alcohol vapors, ethanol, isopropanol and butanol, at approx. 0.2–5 ppm concentrations when mixed with dry air. The results indicate that the developed chip is highly sensitive to these analytes with a detection limit down to the sub-ppm range. Due to the pristine differences in ZnO nanorod network density the chip yields a vector signal which enables the discrimination of various alcohols at a reasonable degree via processing by linear discriminant analysis even at a sub-ppm concentration range suitable for practical applications. Full article
(This article belongs to the Special Issue Multisensor Arrays for Environmental Monitoring)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The scheme of ZnO nanorods growth over the multielectrode chip by hydrothermal process. See the text for details.</p> Full article ">Figure 2
<p>The scheme of experimental setup to measure the gas response of ZnO NR network-based chip. See the text for details.</p> Full article ">Figure 3
<p>The electron microscopy characterization of the ZnO NRs grown over the multielectrode chip: (<b>a</b>) SEM image of the exemplary area of the chip surface; (<b>b</b>) EDX spectrum recorded from the chip surface comprising electrodes; the insert shows the Gauss distribution of NR length in the network analyzed from the SEM image.</p> Full article ">Figure 4
<p>The XPS characterization of the ZnO NRs grown over the multielectrode chip. The chosen energy ranges correspond to Zn 2p (<b>a</b>), O 1s (<b>b</b>) XP peaks, and Zn LMM Auger line (<b>c</b>). Blue points are experimental data; red curves indicate fitting calculations with @Thermo Avantage software.</p> Full article ">Figure 5
<p>The electrical characterization of the ZnO NRs network at the multielectrode chip in background air conditions under heating to ca. 400 °C. Data for exemplary segment are shown: (<b>a</b>) I–V curve taken in DC mode in two opposite direction of electrical potential variation, U<sub>DC</sub> = [−5,+5] V; insert shows the scheme of measurement; (<b>b</b>) Nyquist plot, [1:10<sup>6</sup>] Hz range, the empty blue circles and filled red circles identify the experimental points recorded under U<sub>AC</sub> = 0.1 V and U<sub>AC</sub> = 5.0 V, respectively. The corresponding curves going around the points are built accounting for the equivalent electric scheme of the chemiresistors shown in the inset; (<b>c</b>) the ratio between full impedance, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>Z</mi> <mo>|</mo> </mrow> </mrow> </semantics></math>, its imaginary, Z<sub>im</sub>, and real, Z<sub>real</sub>, components recorded under U<sub>AC</sub> = 0.1 V and U<sub>AC</sub> = 5.0 V in dependence on applied AC frequency.</p> Full article ">Figure 6
<p>The gas-sensing characterization of the ZnO NR network-based multielectrode chip heated up to ca. 400 °C at DC mode: (<b>a</b>) the typical resistance variation of exemplary segment upon chip exposure to isopropanol vapors mixed with air at concentration of 0.4 ppm, 1 ppm and 5 ppm; (<b>b</b>) the dependence of chemiresistive response of the segments in the multisensor array to three alcohol vapors on their concentration; the error bar shows the scatter of data over the multisensor array.</p> Full article ">Figure 7
<p>The processing of vector signal generated by ZnO NR network-based multisensor array by LDA: (<b>a</b>) the recognition of the signals to vapors at sub-ppm concentrations (0.7 ppm for ethanol, 0.4 ppm for isopropanol, 0.2 ppm for butanol) in mixture with background air, the circles are built around the cluster gravity centers with 0.95 confidence probability based on sampling of 20 training vector points; (<b>b</b>) the average distance between vapor-related clusters in LDA space when processing vector signals to vapors at sub-ppm, 1 ppm, 5 ppm, and all-range concentrations; (<b>c</b>) the recognition of vapors at all concentrations in range from sub-ppm to 5 ppm, the spheres are drawn to indicate the areas in the LDA space related to test vapors.</p> Full article ">

Review

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22 pages, 1958 KiB  
Review
Application of Electrochemical Aptasensors toward Clinical Diagnostics, Food, and Environmental Monitoring: Review
by Zhanhong Li, Mona A. Mohamed, A. M. Vinu Mohan, Zhigang Zhu, Vinay Sharma, Geetesh K. Mishra and Rupesh K. Mishra
Sensors 2019, 19(24), 5435; https://doi.org/10.3390/s19245435 - 10 Dec 2019
Cited by 91 | Viewed by 7079
Abstract
Aptamers are synthetic bio-receptors of deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) origin selected by the systematic evolution of ligands (SELEX) process that bind a broad range of target analytes with high affinity and specificity. So far, electrochemical biosensors have come up as [...] Read more.
Aptamers are synthetic bio-receptors of deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) origin selected by the systematic evolution of ligands (SELEX) process that bind a broad range of target analytes with high affinity and specificity. So far, electrochemical biosensors have come up as a simple and sensitive method to utilize aptamers as a bio-recognition element. Numerous aptamer based sensors have been developed for clinical diagnostics, food, and environmental monitoring and several other applications are under development. Aptasensors are capable of extending the limits of current analytical techniques in clinical diagnostics, food, and environmental sample analysis. However, the potential applications of aptamer based electrochemical biosensors are unlimited; current applications are observed in the areas of food toxins, clinical biomarkers, and pesticide detection. This review attempts to enumerate the most representative examples of research progress in aptamer based electrochemical biosensing principles that have been developed in recent years. Additionally, this account will discuss various current developments on aptamer-based sensors toward heavy metal detection, for various cardiac biomarkers, antibiotics detection, and also on how the aptamers can be deployed to couple with antibody-based assays as a hybrid sensing platform. Aptamers can be used in various applications, however, this account will focus on the recent advancements made toward food, environmental, and clinical diagnostic application. This review paper compares various electrochemical aptamer based sensor detection strategies that have been applied so far and used as a state of the art. As illustrated in the literature, aptamers have been utilized extensively for environmental, cancer biomarker, biomedical application, and antibiotic detection and thus have been extensively discussed in this article. Full article
(This article belongs to the Special Issue Multisensor Arrays for Environmental Monitoring)
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Figure 1

Figure 1
<p>The schematic illustration of various assays combining antibody and aptamer coupling on a sensor surface. (<b>a</b>) Antibody and aptamer originated sandwich bio-assay, (<b>b</b>) aptamer and antibody-bioassay, (<b>c</b>) binary aptamer originated sandwich-bioassay, (<b>d</b>) aptamer-based sandwich-bioassay based on smart nanomaterials.</p> Full article ">Figure 2
<p>Schematic representation of labeled and label-free approaches in electrochemical biosensors.</p> Full article ">Figure 3
<p>Schematic representation of target-induced variation in charge transfer resistance.</p> Full article ">Figure 4
<p>Schematic for the principle of the structure switching aptamer-based assay.</p> Full article ">Figure 5
<p>Schematic principle of the electrochemical aptasensor for food toxin detection via the redox probe attached aptamer and enzyme induced catalysis assay.</p> Full article ">
22 pages, 1455 KiB  
Review
Review on Smart Gas Sensing Technology
by Shaobin Feng, Fadi Farha, Qingjuan Li, Yueliang Wan, Yang Xu, Tao Zhang and Huansheng Ning
Sensors 2019, 19(17), 3760; https://doi.org/10.3390/s19173760 - 30 Aug 2019
Cited by 232 | Viewed by 16810
Abstract
With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of [...] Read more.
With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper. Full article
(This article belongs to the Special Issue Multisensor Arrays for Environmental Monitoring)
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Figure 1

Figure 1
<p>2018-2023 Gas Sensor Market in Value(<span>$</span>B) [<a href="#B12-sensors-19-03760" class="html-bibr">12</a>].</p> Full article ">Figure 2
<p>The step of Smart Gas Sensing.</p> Full article ">Figure 3
<p>Classification of Gas Sensitive Materials [<a href="#B18-sensors-19-03760" class="html-bibr">18</a>,<a href="#B19-sensors-19-03760" class="html-bibr">19</a>,<a href="#B20-sensors-19-03760" class="html-bibr">20</a>,<a href="#B21-sensors-19-03760" class="html-bibr">21</a>,<a href="#B22-sensors-19-03760" class="html-bibr">22</a>].</p> Full article ">Figure 4
<p>The Performance of Zn-OPV for Detecting NH<math display="inline"><semantics> <msub> <mrow/> <mn>3</mn> </msub> </semantics></math> at Room Temperature [<a href="#B33-sensors-19-03760" class="html-bibr">33</a>].</p> Full article ">Figure 5
<p>Typical Response of a Chemical Gas Sensor [<a href="#B89-sensors-19-03760" class="html-bibr">89</a>].</p> Full article ">Figure 6
<p>PCA Result of Each Sensor of the Array [<a href="#B95-sensors-19-03760" class="html-bibr">95</a>].</p> Full article ">Figure 7
<p>Unsynchronized Response and Recovery Curves [<a href="#B129-sensors-19-03760" class="html-bibr">129</a>].</p> Full article ">Figure 8
<p>Structure of Gas Detection Systems [<a href="#B140-sensors-19-03760" class="html-bibr">140</a>].</p> Full article ">Figure 9
<p>Smart Gas Sensing SOC [<a href="#B141-sensors-19-03760" class="html-bibr">141</a>].</p> Full article ">Figure 10
<p>A Structure of Centralized WSN [<a href="#B9-sensors-19-03760" class="html-bibr">9</a>].</p> Full article ">Figure 11
<p>A Distributed WSN Based on Fog Calculation [<a href="#B149-sensors-19-03760" class="html-bibr">149</a>].</p> Full article ">
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