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Recent Advances in Sensors for Chemical Detection Applications (2nd Edition)

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 12913

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Special Issue Editor

Prof. Dr. Michele Penza

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Guest Editor

ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability, Division of Technologies and Advanced Materials for Sustainable Manufacturing Industry—Brindisi Research Center, Brindisi, Italy
Interests: sensor materials; functional materials; gas sensors; chemical sensors; air-quality sensor systems; sensor technology development; environmental measurements; urban air-quality sensor networks; smart city applications; environmental sustainability; environmental sensing
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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 research 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 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

chemical detection
gas sensors
low-cost sensor technology
functional materials
sensor systems
wireless sensor networks
IoT applications
sensor calibration

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Published Papers (14 papers)

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Research

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21 pages, 4034 KB

Open AccessArticle

Low-Cost Portable Sensor Node for Gas and Chemical Leak Detection with Kalman-Filtering-Based UWB Localization

by Mohammed Faeik Ruzaij Al-Okby, Thomas Roddelkopf and Kerstin Thurow

Sensors 2026, 26(10), 2921; https://doi.org/10.3390/s26102921 - 7 May 2026

Viewed by 309

Abstract

The work environment in automated laboratories and industrial sites exposes workers to the risks associated with chemical gas and vapor leaks caused by unforeseen incidents. Such leaks may result in severe health hazards as well as damage to equipment or infrastructure at the leak site. Therefore, the development of systems capable of early detection and highly accurate localization of chemical leaks is of high importance for occupational safety. In this work, a low-cost, portable sensor node based on the Internet of Things (IoT) is proposed for the detection and localization of gas and chemical leaks in indoor environments. The sensor node features a modular design that enables flexible integration and replacement of gas and environmental sensors depending on the target application. In addition, the system includes an ultra-wideband (UWB)-based positioning and tracking unit, allowing operation across multiple indoor zones. The main contribution of this work lies in the combined integration of (i) multi-sensor-based environmental event detection and prediction and (ii) high-precision location within a dynamic multi-zone tracking architecture. The system automatically selects the most relevant anchors in each zone and applies trilateration and least-squares estimation, enhanced by Kalman filtering techniques. In particular, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are employed, with sensor fusion incorporating inertial measurement unit (IMU) data to mitigate the effects of on-line-of-sight (NLoS) conditions and signal degradation caused by obstacles. Experimental results demonstrate that both the EKF and UKF significantly reduce positioning errors and improve tracking stability compared to baseline methods under challenging indoor conditions. The UKF shows superior performance in highly nonlinear scenarios. A quantitative evaluation using manually surveyed reference points showed that the UKF achieved the best overall performance, with a mean error of 39.72 cm and an RMSE of 43.03 cm. These findings confirm the effectiveness of Kalman filter-based sensor fusion for reliable indoor positioning and highlight the suitability of the proposed system for real-time safety monitoring applications. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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19 pages, 20184 KB

Open AccessArticle

A Fluorescence-Based Sensor Combined with Chemometric and Deep Learning Approaches for Detecting and Quantifying Coconut Milk Fraud in Bovine Milk

by Stella Maria Dyah Cahyarani and Hoonsoo Lee

Sensors 2026, 26(9), 2872; https://doi.org/10.3390/s26092872 - 4 May 2026

Viewed by 985

Abstract

Bovine milk adulteration with coconut milk poses a significant threat to food safety, as both liquids are visually similar yet nutritionally distinct. This study presents an integrated analytical framework combining excitation–emission matrix (EEM) fluorescence spectroscopy with chemometric and deep learning techniques to detect and quantify coconut milk adulteration in bovine milk across nine concentration levels (0–100% v/v). Parallel factor analysis (PARAFAC) resolved two dominant fluorescent components, tryptophan (λ ex/em: 290/350 nm) and riboflavin (λ ex/em: 450/525 nm), whose scores decreased monotonically with increasing adulteration, confirming their role as key chemical biomarkers. For quantitative prediction, PLSR and 1D-CNN models were developed using emission spectra at three excitation wavelengths, with best performances achieved at 450 nm (PLSR: R²P = 0.97, RMSEP = 5.00%; 1D-CNN: R²P = 0.94, RMSEP = 6.75%). A lightweight 2D-CNN utilizing full EEM contour maps as image inputs outperformed all quantitative models, achieving R²P = 0.99, RMSEP = 2.36%, and RPD = 12.97, demonstrating the advantage of preserving the full two-dimensional fluorescence topology over discrete wavelength selection. These results confirm that EEM combined with 2D-CNN provides a highly accurate and non-destructive tool for dairy authentication. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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12 pages, 6621 KB

Open AccessArticle

Electronic Nose-Based Exhaled Volatile Organic Compound Pattern Recognition and Multivariate Signal Analysis for Discriminating Idiopathic Pulmonary Fibrosis from Autoimmune Usual Interstitial Pneumonia

by Marcin Di Marco, Alessio Marinelli, Vitaliano Nicola Quaranta, Andrea Portacci, Esterina Boniello, Luciana Labate, Agnese Caringella, Anna Violante, Giovanna Elisiana Carpagnano and Silvano Dragonieri

Sensors 2026, 26(9), 2624; https://doi.org/10.3390/s26092624 - 23 Apr 2026

Viewed by 861

Abstract

Idiopathic pulmonary fibrosis (IPF) and autoimmune usual interstitial pneumonia (aUIP) share overlapping clinico-radiological features, complicating differential diagnosis. Electronic nose (eNose) technology characterizes exhaled breath profiles (“breathprints”) and may offer a non-invasive diagnostic approach in fibrotic interstitial lung diseases. To evaluate whether eNose breathprint analysis can discriminate between IPF and aUIP. In this cross-sectional study of 60 patients (34 IPF, 26 aUIP), breathprints were analyzed using principal component analysis (PCA, retaining eigenvalues > 1). Group differences were assessed via independent t-tests. Linear discriminant analysis (LDA) with leave-one-out cross-validation evaluated the discriminatory performance of PC combinations. PCA identified four principal components, with PC1 explaining 96% of the total variance. PC1 scores were significantly higher in aUIP compared to IPF (mean difference −0.53; 95% CI −1.04 to −0.02; p = 0.04); PC2-PC4 showed no significant differences (p > 0.3). LDA utilizing PC1 and PC3 achieved a cross-validated classification accuracy of 73.3% (95% CI 60.7–84.4, p < 0.05). eNose-derived breathprints showed preliminary discriminatory potential between IPF and autoimmune UIP, supporting further validation of this non-invasive adjunctive approach. Breathomics represents a promising non-invasive adjunctive tool for phenotyping fibrotic interstitial lung diseases, though larger validation studies integrating clinical and biological data are warranted. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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18 pages, 607 KB

Open AccessArticle

Infrared Spectroscopy for Variety Identification and Authenticity Analysis of Tobacco Samples

by Eric Deconinck, Imad Adahchour, Yasmina Naïmi and Maarten Dill

Sensors 2026, 26(8), 2544; https://doi.org/10.3390/s26082544 - 20 Apr 2026

Viewed by 352

Abstract

In authenticity checking of tobacco products, the identification of the varieties present is of primary importance. Nowadays the detection of illegal tobacco products is often based on package analysis and administrative verification, sometimes complemented with laboratory analysis. In this study an approach based on IR spectroscopy (MID-IR and NIR) for the identification of tobacco varieties in tobacco blends is proposed. Therefore, different blends were prepared, spectra were measured, and binary PLS-DA models were created. All models were evaluated and compared for their predictive performance, using both cross-validation (internal validation) and an external test set. For the best-performing model for each analyte the limit of detection was estimated. Finally, quantitative models were created to estimate the relative amount of one of the targeted varieties in the mixtures and a proof of concept using five commercial tobacco blends was performed. NIR proved to outperform MID-IR with maximum values of correct classification rate, precision, specificity, and accuracy for four varieties and only one misclassification for the two remaining ones. Indicative limit of detection values were obtained between 1 and 8%. Quantitative errors were all smaller than 5%. These values as well as the application to commercial samples proved the feasibility of the presented approach and its potential value as tool in the fight against fraud and counterfeited tobacco products. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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14 pages, 1596 KB

Open AccessArticle

Optimization-Driven Engineering of Electrodeposited Nanographenide–Conductive Polymer/Prussian Blue Nanoarchitectures for Robust Electrochemical Sensing

by Seung Joo Jang, Hong Chul Lim and Tae Hyun Kim

Sensors 2026, 26(8), 2427; https://doi.org/10.3390/s26082427 - 15 Apr 2026

Viewed by 435

Abstract

The development of high-performance electrochemical sensors requires precise integration of electrode active materials that provide both superior electrocatalytic activity and long-term structural stability. Herein, we report a systematically optimized, one-pot electrochemical deposition approach for the fabrication of nanographenide-based nanoarchitectures, incorporating either a conducting polymer (PEDOT-NG) or Prussian blue (PB-NG). Derived from optimization-driven structural refinement—including applied potential, electrodeposition time, and precursor concentration—the robust nanoarchitecture exhibits a hierarchical morphology that provides an expanded electroactive surface area, accelerating charge transfer and enhancing electrochemical catalytic activity. The optimized PEDOT-NG exhibits exceptional sensitivity for the simultaneous determination of ascorbic acid (AA), dopamine (DA), and uric acid (UA), achieving wide linear ranges with low detection limits of 4.1, 0.12, and 0.18 μM, respectively. The PB-NG achieves a limit of detection of 4.39 μM, driven by highly reversible and stable redox kinetics. This performance is underpinned by narrowed peak-to-peak separations (ΔE) and reduced redox potentials. These results underscore the pivotal role of precise parametric control in developing high-performance electrochemical sensors. Furthermore, this work establishes a comprehensive strategy for designing resilient electrode active materials, thereby paving the way for next-generation electrochemical platforms tailored for diverse and robust sensing environments. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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19 pages, 2676 KB

Open AccessArticle

Periodically Pulsed Polarization Gas Sensors Based on Au|YSZ: Mechanism of NO_x Detection

by Nils Donker, Jens Zosel, Ralf Moos and Daniela Schönauer-Kamin

Sensors 2026, 26(7), 2280; https://doi.org/10.3390/s26072280 - 7 Apr 2026

Viewed by 458

Abstract

Pulsed polarization of Au|YSZ gas sensors is examined to clarify the mechanism of NO_x detection under dynamic operation and to disentangle catalytic surface effects from electrochemical relaxation. Using gold electrodes with substantially lower catalytic activity than platinum explicitly enables this mechanistic separation. During pulsed polarization, periodic voltage pulses are followed by self-discharge under open-circuit conditions, and the response is measured based on the self-discharge rate. NO₂ consistently accelerates the self-discharge from the beginning, whereas NO slows the relaxation predominantly at later times. CO and H₂ produce similar delaying effects, and C₃H₆ shows no measurable influence under the tested conditions. Decreasing ambient O₂ slows the discharge and amplifies the NO₂ effect, which indicates that oxygen supply and surface exchange at the triple-phase boundary are rate determining. A Pt-containing catalytic overlayer drives local NO/NO₂ interconversion toward equilibrium so that both gases yield to an accelerated self-discharge. These findings support a mechanistic picture in which NO₂ provides effective oxygen equivalents that accelerate discharge, whereas NO, CO, and H₂ consume oxygen and slow down discharge. Overall, this establishes a materials-based approach for distinguishing between NO and NO₂ and evaluating the underlying mechanism during pulsed polarization. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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17 pages, 3154 KB

Open AccessArticle

Embedded MOX-Based Volatilomic Sensing for Real-Time Classification of Plant-Based Milk Beverages

by Elisabetta Poeta, Veronica Sberveglieri and Estefanía Núñez-Carmona

Sensors 2026, 26(6), 1976; https://doi.org/10.3390/s26061976 - 21 Mar 2026

Viewed by 1270

Abstract

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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13 pages, 11610 KB

Open AccessArticle

Single and Dual Mode SMR Sensors for Pest Detection in Plant Health Monitoring

by Usman Yaqoob, Barbara Urasinska-Wojcik, Siavash Esfahani, Marina Cole and Julian W. Gardner

Sensors 2026, 26(5), 1708; https://doi.org/10.3390/s26051708 - 8 Mar 2026

Viewed by 448

Abstract

This study presents the development and evaluation of surface functionalized solidly mounted resonators (SMRs), including custom developed at the University of Warwick (UWAR) devices and commercial Sorex sensors, for the detection and classification of plant-emitted volatile organic compounds (VOCs). The sensors were tested against linalool, trans-2-hexenal (T2H), and D-limonene at different concentrations under both dry and humid conditions (30% ± 3% RH). A Python-based (v3.13.5) signal-processing workflow was established to filter frequency responses and extract key features, such as baseline, saturation point, and frequency shift (

Δ f

). Adsorption behaviour was modelled using the Freundlich isotherm, showing good agreement with experimental data and suggesting heterogeneous, multilayer adsorption on CH₃-terminated EC surfaces. A 2D polar classification framework combining vector-normalized

Δ f

values from UWAR and Sorex sensors enabled a clear separation of the VOCs. The results highlight the complementary performance of the two types of SMR sensors and demonstrate that feature-engineered resonant devices, combined with computational classification, offer strong potential for future use in plant health monitoring systems. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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19 pages, 7556 KB

Open AccessArticle

Laser-Induced Graphene Dual Optical/Electrochemical Platform for In-Chip Sensing Applications

by Bengisu D. Gok, Nuno F. Santos, Sónia O. Pereira, Ana S. Ferreira, José C. Germino, Ana R. Soares, António J. S. Fernandes, Florinda M. Costa and Luis Baptista-Pires

Sensors 2026, 26(4), 1128; https://doi.org/10.3390/s26041128 - 10 Feb 2026

Viewed by 693

Abstract

The present study addresses the development and characterization of an in-chip laser-induced graphene (LIG)-based sensor that combines optical and electrochemical transduction techniques as a proof of concept for the advancement of novel point-of-care (POC) devices. In recent years, LIG has emerged as a suitable material for next-generation diagnostic devices due to the increasing need for effective and easily accessible biosensing platforms. In this context, the presented sensors were fabricated and tested with an increasing number of laser exposures to understand how the resulting morphology, degree of graphitization, defects, and electrical resistance of LIG electrodes affect the electrochemical and optical sensing performance. To validate the dual sensor, ferrocyanide ([Fe(CN)₆]⁴⁻) was used as a redox probe and [(4-Dicyanomethylene)-2-methyl-6-(4-dimethylaminostyryl)-4H-pyran] (DCM) was used as model dye to explore the electrochemical and optical sensing capabilities. Finally, we showcase the sensor’s ability to perform simultaneous optical and electrochemical on-time detection and analysis of the ferrocyanide electro-oxidation process, underscoring its potential to be used as a dual optical/electrochemical POC device. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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22 pages, 3049 KB

Open AccessArticle

Octachlorinated Metal Phthalocyanines (M = Co, Zn, VO): Crystal Structures, Thin-Film Properties, and Chemiresistive Sensing of Ammonia and Hydrogen Sulfide

by Tatiana Kamdina, Darya Klyamer, Aleksandr Sukhikh, Pavel Popovetskiy, Pavel Krasnov and Tamara Basova

Sensors 2026, 26(1), 8; https://doi.org/10.3390/s26010008 - 19 Dec 2025

Cited by 1 | Viewed by 764

Abstract

Octachlorinated metal phthalocyanines (MPcCl₈, M = Co, Zn, VO) represent an underexplored class of functional materials with promising potential for chemiresistive sensing applications. This work is the first to determine the structure of single crystals of CoPcCl₈, revealing a triclinic (P-1) packing motif with cofacial molecular stacks and an interplanar distance of 3.381 Å. Powder XRD, vibrational spectroscopy, and elemental analysis confirm phase purity and isostructurality between CoPcCl₈ and ZnPcCl₈, while VOPcCl₈ adopts a tetragonal arrangement similar to its tetrachlorinated analogue. Thin films were fabricated via physical vapor deposition (PVD) and spin-coating (SC), with SC yielding highly crystalline films and PVD resulting in poorly crystalline or amorphous layers. Electrical measurements demonstrate that SC films exhibit n-type semiconducting behavior with conductivities 2–3 orders of magnitude higher than PVD films. Density functional theory (DFT) calculations corroborate the experimental findings, predicting band gaps of 1.19 eV (Co), 1.11 eV (Zn), and 0.78 eV (VO), with Fermi levels positioned near the conduction band, which is consistent with n-type character. Chemiresistive sensing tests reveal that SC-deposited MPcCl₈ films respond reversibly and selectively to ammonia (NH₃) and hydrogen sulfide (H₂S) at room temperature. ZnPcCl₈ shows the highest NH₃ response (45.3% to 10 ppm), while CoPcCl₈ exhibits superior sensitivity to H₂S (LOD = 0.3 ppm). These results suggest that the films of octachlorinated phthalocyanines produced by the SC method are highly sensitive materials for gas sensors designed to detect toxic and corrosive gases. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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16 pages, 2574 KB

Open AccessArticle

Tetracycline Molecularly Imprinted Fluorescent Sensor Based on Tomato Stalk-Derived Carbon Dots

by Xuejing Wang, Jing Wang, Guanya Ji, Yihua Zhu, Jun Shi, Mengge Zhang, Chengshun Tang, Hongwei Duan, Xiuxiu Dong, Oluwafunmilola Ola, Qian Liu and Qijian Niu

Sensors 2025, 25(22), 6993; https://doi.org/10.3390/s25226993 - 15 Nov 2025

Viewed by 1242

Abstract

In this work, novel biomass-derived carbon dots (CDs) with superior fluorescent properties were prepared from tomato straws. A selective, eco-friendly tetracycline (TC) sensor was fabricated by immobilizing a SiO₂ molecularly imprinted polymer (MIP) layer onto CDs, forming a CDs@SiO₂-MIP composite. This sensor combined highly selective adsorption properties with the sensitivity of fluorescence detection, with the sensing mechanism stemming from the off-fluorescent signal after molecular imprinting specifically recognizing the target substance. Under optimal conditions, the sensor exhibited a linear response to TC concentrations ranging from 1.00 × 10⁻⁷ to 5.00 × 10⁻⁴ mol/L, with fluorescence intensity decreasing as concentration increased. The detection limit of TC was 9.33 × 10⁻⁸ mol/L. This work provides novel biomass-derived CDs and a simple molecularly imprinted fluorescence sensing method for the detection of environmental organic pollutants. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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18 pages, 2036 KB

Open AccessArticle

Broccoli to the Lab: Green-Synthesized N-CQDs for Ultrasensitive “Turn-On” Detection of Norfloxacin in Food

by Zubair Akram, Anam Arshad, Sajida Noureen, Muhammad Mehdi, Ali Raza, Nan Wang and Feng Yu

Sensors 2025, 25(20), 6284; https://doi.org/10.3390/s25206284 - 10 Oct 2025

Cited by 3 | Viewed by 1286

Abstract

The widespread presence of antibiotic residues, particularly norfloxacin (NFX), in food products and the environment has raised concern, underscoring the need for sensitive and selective detection methods. In this study, a novel broccoli-derived nitrogen-doped carbon quantum dots (N-CQDs) was synthesized via a green hydrothermal approach, 4-dimethylaminopyridine (DMAP) as both a nitrogen dopant and a functionalizing agent. The synthesized N-CQDs exhibit an average diameter of approximately ~4.2 nm and emit bright blue fluorescence, with a maximum emission at 445 nm upon excitation at 360 nm. A “Turn-ON” response toward NFX was achieved with a detection limit of 0.30 nM, attributed to hydrogen bonding and π–π stacking interactions that suppressed non-radiative decay. Moreover, the sensor demonstrates high selectivity for NFX, effectively distinguishing it from common interfering substances, including other antibiotics, organic acids, and biomolecules. The N-CQDs also exhibit excellent stability under diverse conditions, such as varying pH levels, high ionic strength, and prolonged irradiation. Finally, the practical applicability of the developed sensor was validated by detecting NFX in spiked broccoli extract and milk samples, with recovery rates ranging from 98.2% to 100.1% and relative standard deviations of less than 2.0%. This work presents a sustainable and efficient N-CQD-based fluorescent sensing platform, offering significant potential for rapid and reliable detection of NFX in food safety and environmental monitoring. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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22 pages, 3151 KB

Open AccessReview

Integrating Metabolomics and Machine Learning for Advanced Chemical Detection

by Gianfranco Picone

Sensors 2026, 26(10), 3001; https://doi.org/10.3390/s26103001 - 10 May 2026

Viewed by 800

Abstract

Metabolomics has emerged as a powerful analytical approach for comprehensive chemical profiling in complex biological and environmental systems. The increasing volume, dimensionality, and complexity of metabolomics data have driven the adoption of machine learning (ML) techniques to enhance chemical detection, classification, and interpretation. This narrative review critically discusses the integration of metabolomics and machine learning for advanced chemical detection, with particular emphasis on analytical workflows, data preprocessing strategies, supervised and unsupervised learning models, and validation approaches. In this context, advanced chemical detection refers to the data-driven identification, classification, and quantification of chemical signatures in complex matrices with improved sensitivity, selectivity, robustness, and interpretability. Current applications across food science, environmental monitoring, clinical diagnostics, and exposomics are discussed, along with key challenges related to data quality, interpretability, and reproducibility. Finally, future perspectives on explainable AI, multimodal data integration, and standardized pipelines are highlighted. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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28 pages, 4574 KB

Open AccessReview

Flatland Metasurfaces for Optical Gas Sensing

by Muhammad A. Butt

Sensors 2026, 26(4), 1293; https://doi.org/10.3390/s26041293 - 17 Feb 2026

Cited by 1 | Viewed by 1673

Abstract

Flatland metasurfaces provide a fundamentally distinct approach to optical gas sensing by confining light–matter interaction to planar, subwavelength interfaces, where resonant energy storage and near-field enhancement replace extended optical path lengths. This review presents a physics-driven perspective on metasurface-enabled gas sensing, focusing on how gaseous analytes perturb the complex eigenmodes of engineered planar resonators. Diverse sensing modalities, including enhanced molecular absorption, refractive index-induced resonance shifts, loss modulation, polarization conversion, and chemo-optical transduction, are unified within a common perturbative framework that links sensitivity to mode confinement, quality factor, and analyte overlap. The analysis highlights fundamental trade-offs imposed by material dispersion, intrinsic loss, and radiation balance across plasmonic, dielectric, polaritonic, and hybrid metasurface platforms operating from the visible to the terahertz regime. Attention is given to the limits of chemical selectivity in flatland architectures and to the role of functional materials, multimodal transduction, and computational inference in addressing these constraints. System-level considerations, including thermal stability, fabrication tolerance, and integration with detectors and electronics, are identified as critical determinants of real-world performance. By consolidating disparate approaches within a unified flatland framework, this review provides physical insight and design guidance for the development of compact, integrable, and application-specific optical gas sensing systems. Full article

(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications (2nd Edition))

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