Journal Description
Chemosensors
Chemosensors
is an international, scientific, peer-reviewed, open access journal on the science and technology of chemical sensors and related analytical methods and systems, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Engineering Village and other databases.
- Journal Rank: JCR - Q1 (Instruments and Instrumentation) / CiteScore - Q2 (Analytical Chemistry)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.1 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.7 (2023);
5-Year Impact Factor:
3.7 (2023)
Latest Articles
Near-Infrared Multiwavelength Raman Anti-Stokes/Stokes Thermometry of Titanium Dioxide
Chemosensors 2024, 12(9), 191; https://doi.org/10.3390/chemosensors12090191 - 17 Sep 2024
Abstract
The use of multiple wavelengths to excite Titanium Dioxide Raman scattering in the near-infrared was investigated for optical nanothermometry. Indeed, Raman spectroscopy can be a very interesting technique for this purpose, as it offers non-disruptive contactless measurements with a high spatial resolution, down
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The use of multiple wavelengths to excite Titanium Dioxide Raman scattering in the near-infrared was investigated for optical nanothermometry. Indeed, Raman spectroscopy can be a very interesting technique for this purpose, as it offers non-disruptive contactless measurements with a high spatial resolution, down to a few µm. A method based on the ratio between the anti-Stokes and Stokes peaks of Anatase Titanium Dioxide was proposed and tested at three different wavelengths, 785, 800 and 980 nm, falling into the first biological transparency window (BTW-I). Using a temperature-controller stage, the temperature response of the sample was measured between 20 and 50 °C, allowing the thermal sensitivity for this range to be estimated. The use of sufficiently high laser power results in the generation of local heating. A proof of concept of the proposed thermometric method was performed by determining the extent of local heating induced by increasing laser power. By exciting with an 800 nm laser at low power intensities, a temperature equal to room temperature (RT) was found, while a maximum temperature increase of 15 °C was detected using the anti-Stokes/Stokes method.
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(This article belongs to the Special Issue Recent Advances in Optical Chemo- and Biosensors)
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O2-Sensitive Inks for Measuring Total (Aerobic) Viable Count Using Micro-Respirometry
by
Sean Cross, Dilidaer Yusufu, Christopher O’Rourke and Andrew Mills
Chemosensors 2024, 12(9), 190; https://doi.org/10.3390/chemosensors12090190 - 15 Sep 2024
Abstract
The popular method of micro-respirometry (μR) for measuring total viable (aerobic) count (TVC) utilises luminescence-based O2 sensors that are difficult to fabricate and therefore expensive. A simple method is described for making inexpensive, ink-based potential substitutes that utilise the same O2
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The popular method of micro-respirometry (μR) for measuring total viable (aerobic) count (TVC) utilises luminescence-based O2 sensors that are difficult to fabricate and therefore expensive. A simple method is described for making inexpensive, ink-based potential substitutes that utilise the same O2-sensitive dyes. The sensitivity of such inks is readily increased by using dyes with a long lifetime in the absence of O2, τo, and/or an ink resin/polymer with a high O2 permeability, Pm(O2). Response modelling of the μR-based TVC system and subsequent testing using a range of O2 sensors of different sensitivity show that there is little to be gained by making the O2 sensor either very sensitive or insensitive, and that the best O2 sensors are dyes such as Pt(II) tetraphenyltetrabenzoporphyrin (PtBP), with τo = ca. 40–50 μs. Further work shows that a simple-to-make PtBP ink can be used as a direct replacement for the expensive O2 sensor used in commercial instruments for measuring TVC based on μR. In addition, the PtBP can be replaced by an even less expensive O2-sensitive dye, Pt(II) meso-tetra(pentafluorophenyl)porphyrin (PtTFPP). The potential use of inexpensive O2-sensitive inks as an alternative to any expensive commercial counterpart based on the same O2-sensitive dye is discussed briefly.
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(This article belongs to the Special Issue Recent Advances in Optical Chemo- and Biosensors)
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Open AccessReview
Research Progress of Taste Biosensors in Simulating Taste Transduction Mechanism
by
Jingjing Liu, Jiale Kuang, Yan Zhang, Yizhou Chen, Shikun Liu, Yanfeng Li, Lixin Qiao, Zhenbo Wei, Shui Jiang and Jie Meng
Chemosensors 2024, 12(9), 189; https://doi.org/10.3390/chemosensors12090189 - 14 Sep 2024
Abstract
The simulation of human sensory functions is a key trend in the field of sensor development. In taste sensing, taste biosensors emulate taste perception using biorecognition elements that participate in taste transduction, such as taste receptors, cells, tissues, etc. This approach obtains high
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The simulation of human sensory functions is a key trend in the field of sensor development. In taste sensing, taste biosensors emulate taste perception using biorecognition elements that participate in taste transduction, such as taste receptors, cells, tissues, etc. This approach obtains high selectivity and a wide detection range of human taste perception, making taste biosensors widely used in food analysis and taste perception studies. By combining biorecognition elements with suitable data processing and analysis techniques, the taste information generated during the process of taste transduction, obtained by the sensing elements of the sensor, can be accurately captured. In this paper, we explore current available solutions to stability and sensitivity, and other challenges in taste biosensors using taste receptors, cells, and tissues as sensing elements. We also outline the applied signal processing techniques based on the signal characteristics from different types of taste biosensors. Finally, it is proposed that the development of taste biosensing sensors will further promote the application of intelligent sensory evaluation and human perception analysis systems in food, medicine, and other fields.
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(This article belongs to the Special Issue Electrochemical Sensor Array for Food Detection and Human Perception)
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Open AccessArticle
Evaluation of an Enzyme-Linked Magnetic Electrochemical Assay for Hepatitis a Virus Detection in Drinking and Vegetable Processing Water
by
Cristine D’Agostino, Rocco Cancelliere, Antonio Ceccarelli, Danila Moscone, Loredana Cozzi, Giuseppina La Rosa, Elisabetta Suffredini and Laura Micheli
Chemosensors 2024, 12(9), 188; https://doi.org/10.3390/chemosensors12090188 - 14 Sep 2024
Abstract
Globally, waterborne viral infections significantly threaten public health. While current European Union regulations stipulate that drinking water must be devoid of harmful pathogens, they do not specifically address the presence of enteric viruses in water used for irrigation or food production. Traditional virus
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Globally, waterborne viral infections significantly threaten public health. While current European Union regulations stipulate that drinking water must be devoid of harmful pathogens, they do not specifically address the presence of enteric viruses in water used for irrigation or food production. Traditional virus detection methods rely on molecular biology assays, requiring specialized personnel and laboratory facilities. Here, we describe an electrochemical sandwich enzyme-linked immunomagnetic assay (ELIME) for the detection of the hepatitis A virus (HAV) in water matrices. This method employed screen-printed electrodes as the sensing platform and utilized commercially available pre-activated magnetic beads to provide a robust foundation for the immunological reaction. The ELIME assay demonstrated exceptional analytical performance in only 185 min achieving a detection limit of 0.5 genomic copies per milliliter (g.c./mL) and exhibiting good reproducibility with a relative standard deviation (RSD) of 7% in HAV-spiked drinking and processing water samples. Compared with the real-time RT-qPCR method described in ISO 15216-1, the ELIME assay demonstrated higher sensitivity, although the overall linearity of the method was moderate. These analytical attributes highlight the potential of the ELIME assay as a rapid and viable alternative for HAV detection in water used for agriculture and food processing.
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(This article belongs to the Special Issue Electrochemical Sensors for Food Control, Environmental Analysis, and Diagnosis in Medicine)
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Open AccessArticle
The Efficient and Sensitive Detection of Serum Dopamine Based on a MOF-199/Ag@Au Composite SERS Sensing Structure
by
Yuyu Peng, Chunyan Wang, Gen Li, Jianguo Cui, Yina Jiang, Xiwang Li, Zhengjie Wang and Xiaofeng Zhou
Chemosensors 2024, 12(9), 187; https://doi.org/10.3390/chemosensors12090187 - 13 Sep 2024
Abstract
In this study, a MOF-199/Ag@Au SERS sensing structure was successfully synthesized by combining metal–organic frameworks (MOFs) with surface-enhanced Raman scattering (SERS) technology for the efficient detection of dopamine (DA), a biomarker for neurological diseases, in serum. Using electrochemical methods, a copper-based MOF (MOF-199)
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In this study, a MOF-199/Ag@Au SERS sensing structure was successfully synthesized by combining metal–organic frameworks (MOFs) with surface-enhanced Raman scattering (SERS) technology for the efficient detection of dopamine (DA), a biomarker for neurological diseases, in serum. Using electrochemical methods, a copper-based MOF (MOF-199) was synthesized in situ on copper substrates and further deposited with silver nanoparticles (AgNPs). Subsequently, gold nanoshells were encapsulated around these silver cores by in situ chemical deposition. This preparation process is simple, controllable, and inexpensive. Furthermore, a novel Azo reaction-based DA SERS method was proposed to detect 1 pM DA, which represents an improvement in sensitivity by two orders of magnitude compared to previous unlabeled SERS detection methods and by four orders of magnitude compared to another SERS approach proposed in this work. There was an excellent linear relationship (R2 = 0.976) between the SERS signal at 1140 cm−1 and the DA concentration (0.001 M~1 pM). The results indicate that the MOF-199/Ag@Au sensor structure can successfully achieve both the qualitative and quantitative detection of DA in serum, thus providing a robust technical basis for the application of SERS technology in the field of clinical neurological disease screening.
Full article
(This article belongs to the Special Issue Chemical and Biosensors Based on Metal-Organic Frames (MOFs))
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Open AccessArticle
Sensitive Detection of Fungicide Folpet by Surface-Enhanced Raman Scattering: Experimental and Theoretical Approach
by
Oumaima Douass, Bousselham Samoudi and Santiago Sanchez-Cortes
Chemosensors 2024, 12(9), 186; https://doi.org/10.3390/chemosensors12090186 - 12 Sep 2024
Abstract
In this work, Surface-Enhanced Raman Spectroscopy (SERS) was employed as an effective detection technique for folpet, characterized by its notable specificity and sensitivity. The investigation involved the use of UV–Vis, Raman, and SERS spectroscopy of folpet at different concentrations for a comprehensive study
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In this work, Surface-Enhanced Raman Spectroscopy (SERS) was employed as an effective detection technique for folpet, characterized by its notable specificity and sensitivity. The investigation involved the use of UV–Vis, Raman, and SERS spectroscopy of folpet at different concentrations for a comprehensive study of plasmon-driven effects such as plasmon resonance, plasmon hybridization, and electric field enhancement resulting in the SERS’ intensification. Specifically, SERS detection of folpet solutions at concentrations below 100 µM is presented in detail by using Ag nanoparticles prepared with hydroxylamine reduction. The experimentation encompassed diverse conditions to optimize the detection process, with Raman spectra acquired for both folpet powder and aqueous solution of folpet at the natural pH. SERS analyses were conducted across a concentration range of 9.5 × 10−8 to 1.61 × 10−4 M, employing 532 nm excitation. The differences in the spectral profiles observed for folpet Raman powder and SERS are ascribed to N–S cleavage; these changes are attributed to plasmon catalysis induced by the used Ag nanoparticles. Transmission electron microscopy (TEM) was also important in the present analysis to better understand which mechanism of nanoparticles aggregation is more favorable for the SERS detection regarding the formation of hot spots in the suspension. Complementing the experimental data, the molecular structure and theoretical Raman spectra of the folpet molecule were calculated through density functional theory (DFT) methods. The outcomes of these calculations were crucial in the elucidation of folpet’s vibrational modes. The culmination of this research resulted in the successful detection of folpet, achieving a notable limit of detection at 4.78 × 10−8 M. This comprehensive approach amalgamates experimental and theoretical methodologies, offering significant insights into the detection capabilities and molecular characteristics of folpet via SERS analysis.
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(This article belongs to the Special Issue Raman and Surface-Enhanced Raman Scattering Techniques in Analytical and Biomedical Fields)
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Open AccessArticle
Wireless Sensor Node for Chemical Agent Detection
by
Zabdiel Brito-Brito, Jesús Salvador Velázquez-González, Fermín Mira, Antonio Román-Villarroel, Xavier Artiga, Satyendra Kumar Mishra, Francisco Vázquez-Gallego, Jung-Mu Kim, Eduardo Fontana, Marcos Tavares de Melo and Ignacio Llamas-Garro
Chemosensors 2024, 12(9), 185; https://doi.org/10.3390/chemosensors12090185 - 11 Sep 2024
Abstract
In this manuscript, we present in detail the design and implementation of the hardware and software to produce a standalone wireless sensor node, called SensorQ system, for the detection of a toxic chemical agent. The proposed wireless sensor node prototype is composed of
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In this manuscript, we present in detail the design and implementation of the hardware and software to produce a standalone wireless sensor node, called SensorQ system, for the detection of a toxic chemical agent. The proposed wireless sensor node prototype is composed of a micro-controller unit (MCU), a radio frequency (RF) transceiver, a dual-band antenna, a rechargeable battery, a voltage regulator, and four integrated sensing devices, all of them integrated in a package with final dimensions and weight of 200 × 80 × 60 mm and 0.422 kg, respectively. The proposed SensorQ prototype operates using the Long-Range (LoRa) wireless communication protocol at 2.4 GHz, with a sensor head implemented on a hetero-core fiber optic structure supporting the surface plasmon resonance (SPR) phenomenon with a sensing section (L = 10 mm) coated with titanium/gold/titanium and a chemically sensitive material (zinc oxide) for the detection of Di-Methyl Methyl Phosphonate (DMMP) vapor in the air, a simulant of the toxic nerve agent Sarin. The transmitted spectra with respect to different concentrations of DMMP vapor in the air were recorded, and then the transmitted power for these concentrations was calculated at a wavelength of 750 nm. The experimental results indicate the feasibility of detecting DMMP vapor in air using the proposed optical sensor head, with DMMP concentrations in the air of 10, 150, and 150 ppm in this proof of concept. We expect that the sensor and wireless sensor node presented herein are promising candidates for integration into a wireless sensor network (WSN) for chemical warfare agent (CWA) detection and contaminated site monitoring without exposure of armed forces.
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(This article belongs to the Collection Recent Advances in Multifunctional Sensing Technology for Gas Analysis)
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Accurate Prediction of Tea Catechin Content with Near-Infrared Spectroscopy by Deep Learning Based on Channel and Spatial Attention Mechanisms
by
Mingzan Zhang, Tuo Zhang, Yuan Wang, Xueyi Duan, Lulu Pu, Yuan Zhang, Qin Li and Yabing Liu
Chemosensors 2024, 12(9), 184; https://doi.org/10.3390/chemosensors12090184 - 11 Sep 2024
Abstract
The assessment of catechin content stands as a pivotal determinant of tea quality. In tea production and quality grading, the development of accurate and non-destructive techniques for the accurate prediction of various catechin content is paramount. Near-infrared spectroscopy (NIRS) has emerged as a
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The assessment of catechin content stands as a pivotal determinant of tea quality. In tea production and quality grading, the development of accurate and non-destructive techniques for the accurate prediction of various catechin content is paramount. Near-infrared spectroscopy (NIRS) has emerged as a widely employed tool for analyzing the chemical composition of tea. Nevertheless, the spectral information obtained from NIRS faces challenges when discerning different types of catechins in black tea, owing to their similar physical and chemical properties. Moreover, the vast number of NIRS wavelengths exceeds the available tea samples, further complicating the accurate assessment of catechin content. This study introduces a novel deep learning approach that integrates specific wavelength selection and attention mechanisms to accurately predict the content of various catechins in black tea simultaneously. First, a wavelength selection algorithm is proposed based on feature interval combination sensitivity segmentation, which effectively extracts the NIRS feature information of tea. Subsequently, a one-dimensional convolutional neural network (CNN) incorporating channel and spatial–sequential attention mechanisms is devised to independently extract the key features from the selected wavelength variables. Finally, a multi-output predictor is employed to accurately predict the four main catechins in tea. The experimental results demonstrate the superiority of the proposed model over existing methods in terms of prediction accuracy and stability (R2 = 0.92, RMSE = 0.018 for epicatechin; R2 = 0.96, RMSE = 0.11 for epicatechin gallate; R2 = 0.97, RMSE = 0.14 for epigallocatechin; R2 = 0.97, RMSE = 0.32 for epigallocatechin gallate). This innovative deep learning approach amalgamates wavelength selection with attention mechanisms, provides a new perspective for the simultaneous assessment of the major components in tea, and contributes to the advancement of precision management in the tea industry’s production and grading processes.
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(This article belongs to the Special Issue Advanced Spectroscopy Technology for Chemical Qualitative and Quantitative Analysis)
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Voltammetric Investigation of Paracetamol Detection in Acidic Conditions by Using Cork-Modified Carbon Paste Electrodes
by
Mayra K. S. Monteiro, Mayara M. S. Monteiro, João M. M. Henrique, Carlos A. Martínez-Huitle, Sergio Ferro and Elisama Vieira dos Santos
Chemosensors 2024, 12(9), 183; https://doi.org/10.3390/chemosensors12090183 - 10 Sep 2024
Abstract
Developing new products that satisfy performance and durability expectations while also addressing environmental concerns is possible through the reuse of residues produced by industrial processes, aiming to fulfill the principles of circular economy. In this study, we improved the performance of a carbon
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Developing new products that satisfy performance and durability expectations while also addressing environmental concerns is possible through the reuse of residues produced by industrial processes, aiming to fulfill the principles of circular economy. In this study, we improved the performance of a carbon paste sensor by incorporating untreated (RC) and regranulated/thermally treated (RGC) cork, which are considered biomass residues from the cork industry. We explored the electroanalytical behavior of paracetamol in sulfuric acid solutions using cyclic voltammetry and differential pulse techniques. The cork-modified carbon paste sensors showed greater sensitivity towards paracetamol. Both modified sensors allowed for an excellent resolution in distinguishing the voltammetric responses of paracetamol in sulfuric acid, showing for both an increase in peak currents compared to the unmodified carbon paste electrode. The quantification of paracetamol without interference has proved to be a feasible operation for the RC- and RGC-modified carbon paste sensors; notably, the first showed the most favorable limits of detection (LD = 2.4112 µM) and quantification (LQ = 8.0373 µM) for paracetamol in the sulfuric acid solution, performing significantly better than the second (LD = 10.355 µM, and LQ = 34.518 µM). Finally, the practical utility of the proposed sensors was assessed by analyzing paracetamol in pharmaceutical samples, obtaining satisfactory results that were in line with those obtainable using high-performance liquid chromatography.
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(This article belongs to the Special Issue Carbon Nanomaterials and Related Materials for Sensing Applications, Volume II)
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Open AccessCommunication
The Time Is Ripe: Olive Drupe Maturation Can Be Simply Evidenced by a Miniaturized, Portable and Easy-to-Use MicroNIR Green Sensor
by
Giuseppina Gullifa, Chiara Albertini, Marialuisa Ruocco, Roberta Risoluti and Stefano Materazzi
Chemosensors 2024, 12(9), 182; https://doi.org/10.3390/chemosensors12090182 - 10 Sep 2024
Abstract
The analytical study described in this work, based on NIR spectroscopy with a handheld device, allowed the development of a chemometric prediction model that has been validated for the objective evaluation of the ripening of olive drupes. The miniaturized, portable NIR spectrometer is
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The analytical study described in this work, based on NIR spectroscopy with a handheld device, allowed the development of a chemometric prediction model that has been validated for the objective evaluation of the ripening of olive drupes. The miniaturized, portable NIR spectrometer is proposed here as an easy-to-use sensor able to estimate the best harvesting time for ripening of olive drupes. The MicroNIR/chemometrics approach was developed for on-site identification of olive drupe ripening directly on plants, avoiding collection and successive laboratory analysis steps. A supporting parallel characterization by chromatographic techniques validated the spectroscopic prediction. The novelty of this approach consists in the possibility of investigating the olive drupe maturation point by collecting spectra in the near-infrared region and processing them using a chemometric model. The fast and accurate device allows one to easily follow the spectrum profile changes of olive drupes during ripening, thus preserving the fruits from being harvested too early or too late. The results of this study demonstrate the possibility of using the MicroNIR/chemometrics approach to determine the optimal ripening time of olives regardless of the plant variety, age and cultivation location. The results consequently demonstrated that the MicroNIR/chemometrics approach can be proposed as a new method to perform on-site evaluation of ripening by a single-click device. It can be conveniently used by any operator, who does not necessarily have to be expert but must simply be trained to use spectroscopy and a prediction model.
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(This article belongs to the Special Issue Recent Advances in Optical Chemo- and Biosensors)
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Open AccessArticle
Low-Drift NO2 Sensor Based on Polyaniline/Black Phosphorus Composites at Room Temperature
by
Bolun Tang, Yunbo Shi, Jijiang Liu, Canda Zheng, Kuo Zhao, Jianhua Zhang and Qiaohua Feng
Chemosensors 2024, 12(9), 181; https://doi.org/10.3390/chemosensors12090181 - 5 Sep 2024
Abstract
In this paper, a room-temperature NO2 sensor based on a polyaniline (PANI)/black phosphorus (BP) composite material was proposed to solve the power consumption problem of traditional metal-oxide sensors operating at high temperatures. PANI was synthesized by chemical oxidative polymerization, whereas BP was
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In this paper, a room-temperature NO2 sensor based on a polyaniline (PANI)/black phosphorus (BP) composite material was proposed to solve the power consumption problem of traditional metal-oxide sensors operating at high temperatures. PANI was synthesized by chemical oxidative polymerization, whereas BP was synthesized by low-pressure mineralization. The PANI/BP composite materials were prepared via ultrasonic exfoliation and mixing. Various characterization techniques, including scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), confirmed the successful preparation of the PANI/BP composites and their excellent structural properties. The sensor demonstrated outstanding gas sensitivity in the NO2 concentration range of 2–60 ppm. In particular, the sensor showed a response exceeding 2200% at 60 ppm NO2 concentration when using a 1:1 mass ratio of PANI to BP in the composite material.
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(This article belongs to the Special Issue Advanced Chemical Sensors for Gas Detection)
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Open AccessArticle
Point-of-Care Testing Kit for the Detection of Hexavalent Chromium by Carbohydrazide-Derived Graphitic Carbon Nitride
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Muniyandi Maruthupandi and Nae Yoon Lee
Chemosensors 2024, 12(9), 180; https://doi.org/10.3390/chemosensors12090180 - 5 Sep 2024
Abstract
Hexavalent chromium (Cr(VI)) ions are among the most common hazardous metals that pose a serious risk to human health, causing human carcinogenesis and chronic kidney damage. In this study, a point-of-care testing (POCT) kit is proposed for Cr(VI) ions detection at room temperature.
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Hexavalent chromium (Cr(VI)) ions are among the most common hazardous metals that pose a serious risk to human health, causing human carcinogenesis and chronic kidney damage. In this study, a point-of-care testing (POCT) kit is proposed for Cr(VI) ions detection at room temperature. The kit contains a hydrophobic parafilm, a nylon membrane to resist outflow, and a hydrophilic Whatman filter paper suitable for coating the fluorescent graphitic carbon nitride sheet (g-C3N4). Crystalline, nano-porous, blue-emitting g-C3N4 was produced by pyrolysis utilizing carbohydrazide. The electrostatic interactions between the g-C3N4 and Cr(VI) ions inhibit the fluorescence behavior. The POCT kit can be used for on-site Cr(VI) ion detection dependent upon the blue emission value. The detection limit was attained at 4.64 nM of Cr(VI) ions. This analytical methodology was utilized on real samples from tap, pond, river, and industrial wastewater. This POCT kit can be a useful alternative for on-site detection of Cr(VI) ions.
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(This article belongs to the Special Issue Rapid Point-of-Care Testing Technology and Application)
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Open AccessArticle
Carbon-Based FET-Type Gas Sensor for the Detection of ppb-Level Benzene at Room Temperature
by
Risheng Cao, Zhengyu Lu, Jinyong Hu and Yong Zhang
Chemosensors 2024, 12(9), 179; https://doi.org/10.3390/chemosensors12090179 - 4 Sep 2024
Abstract
Benzene, as a typical toxic gas and carcinogen, is an important detection object in the field of environmental monitoring. However, it remains challenging for the conventional resistance-type gas sensor to effectively detect low-concentration (ppb-level) benzene gas molecules, owing to their insufficient reaction activation
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Benzene, as a typical toxic gas and carcinogen, is an important detection object in the field of environmental monitoring. However, it remains challenging for the conventional resistance-type gas sensor to effectively detect low-concentration (ppb-level) benzene gas molecules, owing to their insufficient reaction activation energy, especially when operating at room temperature. Herein, a field-effect transistor (FET)-type gas sensor using carbon nanotubes as a channel material is proposed for the efficient detection of trace benzene, where carbon nanotubes (CNTs) with high semiconductor purity act as the main channel material, and ZnO/WS2 nanocomposites serve as the gate sensitive material. On the basis of the remarkable amplification effect in CNTs-based FET, the proposed gas sensor manifests desirable sensitive ability with the detection limit as low as 500 ppb for benzene even working at room temperature, and the sensor also exhibits fast response speed (90 s), high consistency with a response deviation of less than 5%, and long-term stability of up to 30 days. Furthermore, utilizing Tenax TA as the screening unit, the as-proposed gas sensor can achieve the feasible selective detection of benzene. These experimental results demonstrate that the strategy proposed here can provide significant guidance for the development of high-performance gas sensors to detect trace benzene gas at room temperature.
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(This article belongs to the Special Issue Functional Nanomaterial-Based Gas Sensors and Humidity Sensors)
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Open AccessArticle
Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O
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Dominik Baier, Alexander Krüger, Thorsten Wagner, Michael Tiemann and Christian Weinberger
Chemosensors 2024, 12(9), 178; https://doi.org/10.3390/chemosensors12090178 - 3 Sep 2024
Abstract
Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In2O3 can monitor gas concentrations down to the ppm range,
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Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In2O3 can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H2O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In2O3 layer creates identifiable changes in a gas sensor’s electronic resistance that can be linked to H2 and H2O concentrations via machine learning. We exposed nanostructured In2O3 with a large surface area of 95 m2 g−1 to H2 concentrations (0–800 ppm) and relative humidity (0–70%) under cyclic activation utilizing blue light. The sensors were tested for 20 classes of gas combinations. A support vector machine achieved classification rates up to 92.0%, with reliable reproducibility (88.2 ± 2.7%) across five individual sensors using 10-fold cross-validation. Our findings suggest that cyclic optical activation can be used as a tool to classify H2 and H2O concentrations.
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(This article belongs to the Collection Recent Advances in Multifunctional Sensing Technology for Gas Analysis)
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Open AccessArticle
An Optimization of the Extraction of Phenolic Compounds from Grape Marc: A Comparison between Conventional and Ultrasound-Assisted Methods
by
Ziyao Liu, Hanjing Wu, Brendan Holland, Colin J. Barrow and Hafiz A. R. Suleria
Chemosensors 2024, 12(9), 177; https://doi.org/10.3390/chemosensors12090177 - 2 Sep 2024
Abstract
The green extraction of total phenolic compounds, flavonoids, anthocyanins, and tannins from grape marc was optimized using response surface methodology. The extracts were characterized and analyzed using LC-ESI-QTOF-MS/MS, and free radical scavenging capacity was evaluated. An efficient green extraction method is crucial for
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The green extraction of total phenolic compounds, flavonoids, anthocyanins, and tannins from grape marc was optimized using response surface methodology. The extracts were characterized and analyzed using LC-ESI-QTOF-MS/MS, and free radical scavenging capacity was evaluated. An efficient green extraction method is crucial for improving the recovery rates of these high-value phytochemicals and for sustainably reusing wine by-products. Our study optimized parameters for both conventional and ultrasound-assisted extraction methods, including solution pH, extraction temperature, liquid-to-solvent ratio, and ultrasonic amplitude. The optimized conditions for conventional extraction were identified as 60% ethanol with a pH of 2, a solvent-to-solid ratio of 50:1, extraction time of 16 h at a temperature of 49.2 °C. For ultrasound-assisted extraction, the optimized conditions were determined as 60% ethanol with a pH of 2, a solvent-to-solid ratio of 50:1, and an amplitude of 100% for 5.05 min at a temperature of 60 °C. We also demonstrated that lowering the temperature to 49.5 °C improves the energy efficiency of the extraction process with a minor reduction in recovery rates. Considering all factors, ultrasound-assisted extraction is more suitable for efficiently recovering bioactive compounds from grape marc.
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(This article belongs to the Special Issue Green Analytical Chemistry: Current Trends and Future Developments)
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Open AccessArticle
A Novel Colorimetric Biosensor for the Detection of Catalase-Positive Staphylococcus aureus Based on an Onion-like Carbon Nanozyme
by
Yining Fan, Guanyue Gao and Jinfang Zhi
Chemosensors 2024, 12(9), 176; https://doi.org/10.3390/chemosensors12090176 - 2 Sep 2024
Abstract
Staphylococcus aureus is one of the leading causes of skin and soft tissue infections, and it is even life-threatening if it enters the bloodstream, lung or heart. In the present work, we proposed a novel colorimetric biosensor for the detection of S. aureus
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Staphylococcus aureus is one of the leading causes of skin and soft tissue infections, and it is even life-threatening if it enters the bloodstream, lung or heart. In the present work, we proposed a novel colorimetric biosensor for the detection of S. aureus through hydrogen peroxide consumption. An onion-like carbon nanozyme with high peroxidase-like activity was prepared, which competed with the endogenous catalase of S. aureus in consuming hydrogen peroxide. This reaction was further characterized by the colorimetric reaction of 3,3′,5,5′-tetramethylbenzidine. The results showed that our approach allowed for the simple and rapid determination of S. aureus, with a linear range of 2 × 104 to 2 × 107 CFU/mL. Moreover, our method displayed good selectivity, with Bacillus subtilis and Escherichia coli showing negligible responses at the concentration of 2 × 105 CFU/mL. The application of the as-prepared biosensor to analyze S. aureus in real water samples yielded recovery rates ranging from 95% to 112%, with relative standard deviations less than 7%. The method demonstrated good accuracy and specificity, which offers a novel approach for the simple and selective detection of S. aureus.
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(This article belongs to the Collection pH Sensors, Biosensors and Systems)
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Open AccessArticle
Enhanced Sensitivity and Homogeneity of SERS Signals on Plasmonic Substrate When Coupled to Paper Spray Ionization–Mass Spectrometry
by
Adewale A. Adehinmoye, Ebenezer H. Bondzie, Jeremy D. Driskell, Christopher C. Mulligan and Jun-Hyun Kim
Chemosensors 2024, 12(9), 175; https://doi.org/10.3390/chemosensors12090175 - 2 Sep 2024
Abstract
This work reports on the development of an analyte sampling strategy on a plasmonic substrate to amplify the detection capability of a dual analytical system, paper spray ionization–mass spectrometry (PSI-MS) and surface-enhanced Raman spectroscopy (SERS). While simply applying only an analyte solution to
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This work reports on the development of an analyte sampling strategy on a plasmonic substrate to amplify the detection capability of a dual analytical system, paper spray ionization–mass spectrometry (PSI-MS) and surface-enhanced Raman spectroscopy (SERS). While simply applying only an analyte solution to the plasmonic paper results in a limited degree of SERS enhancement, the introduction of plasmonic gold nanoparticles (AuNPs) greatly improves the SERS signals without sacrificing PSI-MS sensitivity. It is initially revealed that the concentration of AuNPs and the type of analytes highly influence the SERS signals and their variations due to the “coffee ring effect” flow mechanism induced during sampling and the degree of the interfacial interactions (e.g., van der Waals, electrostatic, covalent) between the plasmonic substrate and analyte. Subsequent PSI treatment at high voltage conditions further impacts the overall SERS responses, where the signal sensitivity and homogeneity significantly increase throughout the entire substrate, suggesting the ready migration of adsorbed analytes regardless of their interfacial attractive forces. The PSI-induced notable SERS enhancements are presumably associated with creating unique conditions for local aggregation of the AuNPs to induce effective plasmonic couplings and hot spots (i.e., electromagnetic effect) and for repositioning analytes in close proximity to a plasmonic surface to increase polarizability (i.e., chemical effect). The optimized sampling and PSI conditions are also applicable to multi-analyte analysis by SERS and MS, with greatly enhanced detection capability and signal uniformity.
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(This article belongs to the Special Issue Raman and Surface-Enhanced Raman Scattering Techniques in Analytical and Biomedical Fields)
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Open AccessArticle
Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications
by
Caizhi Liao, Huaxing Wu and Luigi G. Occhipinti
Chemosensors 2024, 12(9), 174; https://doi.org/10.3390/chemosensors12090174 - 1 Sep 2024
Abstract
The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential
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The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential diagnostic applications. Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. We explore the fabrication and operational mechanisms of these transistors, the role of machine learning in metabolite analysis, and their potential clinical applications by analyzing practical human blood samples for hypernatremia syndrome. This synergy not only improves diagnostic precision but also holds potential for the development of personalized diagnostics, tailoring treatments for individual metabolic profiles.
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(This article belongs to the Special Issue Artificial Intelligence (AI)/Machine Learning (ML)-Assisted Chemical Sensors)
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Resolution of Glycerol, Ethanol and Methanol Employing a Voltammetric Electronic Tongue
by
João Pedro Jenson de Oliveira, Marta Bonet-San-Emeterio, Acelino Cardoso de Sá, Xavier Cetó, Leonardo Lataro Paim and Manel del Valle
Chemosensors 2024, 12(9), 173; https://doi.org/10.3390/chemosensors12090173 - 1 Sep 2024
Abstract
This paper reports the use of nanoparticles (NPs)-modified voltammetric sensors for the rapid determination of glycerol in the presence of ethanol and methanol, which are used in the transesterification reaction of biodiesel production. Two different modified electrodes have been prepared to form the
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This paper reports the use of nanoparticles (NPs)-modified voltammetric sensors for the rapid determination of glycerol in the presence of ethanol and methanol, which are used in the transesterification reaction of biodiesel production. Two different modified electrodes have been prepared to form the electronic tongue (ET): copper hexacyanoferrate NPs obtained by chemical synthesis and mixed into graphite/epoxy (GEC) electrode, and nickel hydroxide NPs electrodeposited in reduced graphene oxide onto a GEC electrode. The response characteristics of these electrodes were first evaluated by building the respective calibration against glycerol, ethanol, and methanol. The electrodes demonstrated good stability during their analytical characterization, while principal component analysis confirmed the differentiated response against the different alcohols. Finally, the quantification of mixtures of these substances was achieved by a genetic algorithm-artificial neural networks (GA-ANNs) model, showing satisfactory agreement between expected and obtained values.
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(This article belongs to the Special Issue An Exciting Journey of Chemical Sensors and Biosensors: A Theme Issue in Honor of Professor Ingemar Lundström)
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Detection of Harmful H2S Concentration Range, Health Classification, and Lifespan Prediction of CH4 Sensor Arrays in Marine Environments
by
Kai Zhang, Yongwei Zhang, Jian Wu, Tao Wang, Wenkai Jiang, Min Zeng and Zhi Yang
Chemosensors 2024, 12(9), 172; https://doi.org/10.3390/chemosensors12090172 - 29 Aug 2024
Abstract
Underwater methane (CH4) detection technology is of great significance to the leakage monitoring and location of marine natural gas transportation pipelines, the exploration of submarine hydrothermal activity, and the monitoring of submarine volcanic activity. In order to improve the safety of
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Underwater methane (CH4) detection technology is of great significance to the leakage monitoring and location of marine natural gas transportation pipelines, the exploration of submarine hydrothermal activity, and the monitoring of submarine volcanic activity. In order to improve the safety of underwater CH4 detection mission, it is necessary to study the effect of hydrogen sulfide (H2S) in leaking CH4 gas on sensor performance and harmful influence, so as to evaluate the health status and life prediction of underwater CH4 sensor arrays. In the process of detecting CH4, the accuracy decreases when H2S is found in the ocean water. In this study, we proposed an explainable sorted-sparse (ESS) transformer model for concentration interval detection under industrial conditions. The time complexity was decreased to O (n logn) using an explainable sorted-sparse block. Additionally, we proposed the Ocean X generative pre-trained transformer (GPT) model to achieve the online monitoring of the health of the sensors. The ESS transformer model was embedded in the Ocean X GPT model. When the program satisfied the special instructions, it would jump between models, and the online-monitoring question-answering session would be completed. The accuracy of the online monitoring of system health is equal to that of the ESS transformer model. This Ocean-X-generated model can provide a lot of expert information about sensor array failures and electronic noses by text and speech alone. This model had an accuracy of 0.99, which was superior to related models, including transformer encoder (0.98) and convolutional neural networks (CNN) + support vector machine (SVM) (0.97). The Ocean X GPT model for offline question-and-answer tasks had a high mean accuracy (0.99), which was superior to the related models, including long short-term memory–auto encoder (LSTM–AE) (0.96) and GPT decoder (0.98).
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(This article belongs to the Special Issue Functional Nanomaterial-Based Gas Sensors and Humidity Sensors)
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