Chemosensors

13 pages, 6812 KB

Open AccessArticle

Preventing Drug-Facilitated Sexual Assault: A Smartphone-Readout Lateral Flow Assay for GBL Detection in Water and Saliva

by Jordi Roig-Rubio, Carmen Coll, Salvador Gil, Pau Arroyo, José A. Sáez and Pablo Gaviña

Chemosensors 2026, 14(7), 169; https://doi.org/10.3390/chemosensors14070169 - 21 Jul 2026

Viewed by 636

Abstract

The rising prevalence of Drug-Facilitated Sexual Assault (DFSA) underscores the urgent need for rapid, sensitive, and selective analytical tools. Among the most concerning substances are γ-hydroxybutyric acid (GHB) and its precursor γ-butyrolactone (GBL), both of which pose significant forensic challenges due to their [...] Read more.

The rising prevalence of Drug-Facilitated Sexual Assault (DFSA) underscores the urgent need for rapid, sensitive, and selective analytical tools. Among the most concerning substances are γ-hydroxybutyric acid (GHB) and its precursor γ-butyrolactone (GBL), both of which pose significant forensic challenges due to their rapid metabolism and narrow detection windows. While detection methods for GHB have advanced, the monitoring of GBL remains less explored. This study reports the extended application of a fluorescein-based chemosensor previously validated for GHB for the selective detection of GBL in aqueous media and synthetic saliva. The probe operates via fluorescence quenching proportional to GBL concentration, achieving detection limits well below toxicological thresholds. Mechanistic investigations reveal that the recognition process is driven by an acid–base equilibrium and the reversible opening of the fluorescein lactone ring, with the 2-aminonaphthoxazole moiety playing a pivotal role. The sensor exhibits high selectivity against common DFSA-related interferents. Furthermore, the system was integrated into a portable lateral flow assay coupled with a smartphone-based readout, providing a cost-effective and user-friendly platform. Meeting the WHO “ASSURED” criteria, this methodology represents a promising tool for forensic, clinical, and preventive applications against chemical submission. Full article

(This article belongs to the Section Applied Chemical Sensors)

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14 pages, 4539 KB

Open AccessArticle

A Co²⁺ Fluorescent Probe Based on Surface Complexation for On-Site Feed Detection

by Jingjing He, Min Ye, Huiting Lian and Xuexia Lin

Chemosensors 2026, 14(7), 168; https://doi.org/10.3390/chemosensors14070168 - 16 Jul 2026

Viewed by 341

Abstract

As a core component of vitamin B12, Co²⁺ is closely associated with the health, life performance, and productivity of ruminants. Therefore, the selective and sensitive detection of Co²⁺ is of great significance. In this work, using the Au–S bond as an [...] Read more.

As a core component of vitamin B12, Co²⁺ is closely associated with the health, life performance, and productivity of ruminants. Therefore, the selective and sensitive detection of Co²⁺ is of great significance. In this work, using the Au–S bond as an “anchor”, a ternary nanocomposite (CDs-AuNPs-GSH) with synergistic functions was constructed by combining gold nanoparticles (AuNPs) with glutathione (GSH) through hydrogen bonding and electrostatic interactions with functional groups on the surface of carbon dots (CDs). The resulting nanocomposite was employed as a fluorescence sensor for Co²⁺ detection. Under optimal conditions at pH 6.0, the sensor exhibited a good linear relationship over the Co²⁺ concentration range of 0.5–125.0 mM, with a limit of detection (LOD) of 0.38 mM. The interaction mechanism between Co²⁺ and the composite was systematically investigated using various characterization methods. The results indicated that Co²⁺, owing to its strong coordination ability, enriches on the surface of the composite, subsequently triggering dynamic fluorescence quenching via an electron transfer pathway. The sensor was successfully applied to determine Co²⁺ in mixed livestock and poultry feed samples, with recovery rates ranging from 89.4% to 103.9%, demonstrating its potential for on-site detection in feed analysis. Full article

(This article belongs to the Special Issue Fluorescent Probes for Highly Sensitive Ion and Compound Detection)

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16 pages, 830 KB

Open AccessArticle

Geographical and Seasonal Differentiation Along with Quality Evaluation of Lavender Essential Oil Through Volatile Profiling

by Cornelia Veronica Floare-Avram, Olivian Marincas, Dana Alina Magdas and Ioana Feher

Chemosensors 2026, 14(7), 167; https://doi.org/10.3390/chemosensors14070167 - 16 Jul 2026

Viewed by 577

Abstract

The study aimed to characterize Romanian lavender essential oils (LEOs) according to geographical regions and harvested years by combining gas chromatography–mass spectrometry (GC/MS) with chemometric analysis and quality evaluation. Sixty-four LEO samples were collected from three Romanian areas (Moldova, Transylvania, Muntenia) during three [...] Read more.

The study aimed to characterize Romanian lavender essential oils (LEOs) according to geographical regions and harvested years by combining gas chromatography–mass spectrometry (GC/MS) with chemometric analysis and quality evaluation. Sixty-four LEO samples were collected from three Romanian areas (Moldova, Transylvania, Muntenia) during three consecutive years (2023–2025). GC–MS profiling identified 45 volatile compounds, with linalool, linalyl acetate, 1,8-cineole as predominant constituents. Oil quality was assessed using both a quality scoring algorithm based on characteristic volatile compounds and compliance with the ISO 3515:2002 standard. Most samples were classified as very good quality according to the proposed quality score, whereas only three samples fully complied with all ISO requirements. Pearson correlation analysis revealed significant positive associations among several biosynthetically related compounds, indicating identical variation within the volatile fingerprint. Linear discriminant analysis (LDA) successfully discriminated samples according to both geographical origin and harvest year, identifying characteristic combinations of volatile compounds responsible for sample classification. The results demonstrate that the overall volatile fingerprint, rather than individual compounds, provides a reliable basis for the authentication and differentiation of Romanian lavender essential oils. The combined GC–MS and chemometric approach represents an effective tool for quality evaluation, geographical traceability and authenticity assessment of lavender essential oils. Full article

(This article belongs to the Special Issue Advanced Chemometric Methods for Analytical Applications)

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18 pages, 18384 KB

Open AccessArticle

Enhanced Oxygen Vacancies in Ni-Doped SnO₂ Nanorods via Aerosol-Assisted Chemical Vapor Deposition for Low-Concentration Hydrogen Detection

by Peng Chen, Xin Zhang, Jiacheng Liu, Xu Li, Min Chen and Qingji Wang

Chemosensors 2026, 14(7), 166; https://doi.org/10.3390/chemosensors14070166 - 15 Jul 2026

Viewed by 629

Abstract

Hydrogen is a clean energy carrier essential for carbon neutrality, but its invisible and odorless nature poses significant safety risks, particularly during low-concentration leaks. Although metal oxide semiconductor (MOS) sensors offer fast response and high sensitivity, their ability to detect ppb-level hydrogen remains [...] Read more.

Hydrogen is a clean energy carrier essential for carbon neutrality, but its invisible and odorless nature poses significant safety risks, particularly during low-concentration leaks. Although metal oxide semiconductor (MOS) sensors offer fast response and high sensitivity, their ability to detect ppb-level hydrogen remains limited. In this work, we present a high-performance hydrogen gas sensor based on nickel-doped tin dioxide (Ni-SnO₂) nanorods, directly grown on planar electrodes via aerosol-assisted chemical vapor deposition (AACVD). By optimizing the Ni doping ratio and nanorod morphology, the 3 wt% Ni-SnO₂ sensor achieves a low detection limit of 100 ppb for H₂, demonstrating promising potential for low-concentration hydrogen detection. Moreover, the sensor exhibits outstanding selectivity, with a response to 100 ppm H₂ nearly six times higher than that to the next most responsive interfering gas (NH₃). Comprehensive XPS and Raman analyses reveal that Ni doping introduces abundant oxygen vacancies and lattice defects, which are the key origins of the enhanced sensing performance. Notably, the 3 wt% Ni-SnO₂ sensor strikes an optimal balance between lattice defects and structural stability, delivering both high sensitivity and good moisture resistance with minimal baseline drift over weeks of operation. This work establishes a facile and scalable AACVD strategy for engineering defect-rich SnO₂ nanostructures, enabling sub-ppm hydrogen detection with high selectivity and long-term stability—addressing a critical gap in practical hydrogen safety monitoring. Full article

(This article belongs to the Section Materials for Chemical Sensing)

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4 pages, 167 KB

Open AccessEditorial

Colorimetric and Fluorescent Sensors: Current Status and Future Development

by Kien Wen Sun and Muthaiah Shellaiah

Chemosensors 2026, 14(7), 165; https://doi.org/10.3390/chemosensors14070165 - 15 Jul 2026

Viewed by 580

Abstract

Chemosensors for diverse analytes have become a significant research topic experiencing innovative real-time development [...] Full article

(This article belongs to the Special Issue Colorimetric and Fluorescent Sensors: Current Status and Future Development)

36 pages, 10377 KB

Open AccessReview

Sensing and Optical Imaging of Ferroptosis-Related Molecular Events in Acute Ischemic Stroke: Mechanisms, Technologies and Translational Perspectives

by Ru Wang, Jinghang Li, Siqi Huang, Yuguang Lv, Zhiling Hou and Nuan Wen

Chemosensors 2026, 14(7), 164; https://doi.org/10.3390/chemosensors14070164 - 14 Jul 2026

Viewed by 511

Abstract

Reperfusion after acute ischemic stroke (AIS) triggers a series of ferroptosis-related molecular events, including iron dyshomeostasis, oxidative/nitrative stress, antioxidant depletion, and membrane lipid peroxidation. Conventional ferroptosis assays mainly rely on ex vivo or endpoint measurements, limiting their ability to dynamically monitor the spatiotemporal [...] Read more.

Reperfusion after acute ischemic stroke (AIS) triggers a series of ferroptosis-related molecular events, including iron dyshomeostasis, oxidative/nitrative stress, antioxidant depletion, and membrane lipid peroxidation. Conventional ferroptosis assays mainly rely on ex vivo or endpoint measurements, limiting their ability to dynamically monitor the spatiotemporal evolution of these events during ischemia–reperfusion. Recent advances in chemical sensing and optical imaging have enabled in situ detection of key ferroptosis-related nodes, such as Fe²⁺/labile iron pool, ROS/ONOO⁻, GSH/Cys/GPX4, H₂S/Cys–Met metabolism, and lipid peroxidation. In this review, we summarize sensing targets, reaction-based probe design, near-infrared and two-photon imaging, photoacoustic imaging, and multimodal validation strategies for AIS-related ferroptosis. Representative probes for H₂O₂, ONOO⁻, H₂S, Fe²⁺, and lipid peroxidation are discussed in the context of cellular models, oxygen-glucose deprivation/reoxygenation, middle cerebral artery occlusion/reperfusion, and in vivo brain imaging. We emphasize that a single probe signal cannot independently confirm ferroptosis and should be interpreted together with GPX4/ACSL4 alterations, MDA/4-HNE levels, tissue injury, neurological outcomes, and Fer-1/Lip-1 rescue experiments. Finally, we discuss current challenges, including limited tissue penetration, blood–brain barrier delivery, quantitative stability, probe safety, and clinical translation, and highlight future directions involving ratiometric, NIR/NIR-II, two-photon, multitarget, and imaging-guided validation strategies. Full article

(This article belongs to the Special Issue Advanced Optical Imaging Technologies and Fluorescent Probes)

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23 pages, 10805 KB

Open AccessReview

Functional Materials for Molecular POCT in Infectious Disease Detection: Advances and Regulatory Perspectives

by Yan Tan, Haonan Wu, Junjun Lan, Ting Qian, Xin Zhou, Shiyang Zhao and Hui Wang

Chemosensors 2026, 14(7), 163; https://doi.org/10.3390/chemosensors14070163 - 14 Jul 2026

Cited by 1 | Viewed by 690

Abstract

On-site nucleic acid analysis for infectious diseases can be rapidly achieved through molecular point-of-care testing (molecular POCT), which plays an indispensable role in early pathogen identification, timely clinical intervention, and public health emergency response. Performance improvements in such systems are largely driven by [...] Read more.

On-site nucleic acid analysis for infectious diseases can be rapidly achieved through molecular point-of-care testing (molecular POCT), which plays an indispensable role in early pathogen identification, timely clinical intervention, and public health emergency response. Performance improvements in such systems are largely driven by innovations in functional materials that refine nucleic acid extraction, amplification, and signal output. This article reviews recent developments in functional materials deployed in molecular POCT, with emphasis on nucleic acid capture matrices, amplification-promoting agents, and signal transduction components. From a medical device regulatory standpoint, we examine how material characteristics shape key analytical indicators, including sensitivity and specificity, and discuss critical risks such as off-target amplification and batch inconsistency. Finally, we outline future directions, highlighting cross-disciplinary cooperation to reconcile technological innovation with risk control for translating advanced materials into high-performance molecular POCT products. Full article

(This article belongs to the Special Issue Rapid Point-of-Care Testing Technology and Application (Second Edition))

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14 pages, 3157 KB

Open AccessArticle

COC Chip-Integrated Zinc Finger Protein Array for PCR-Free Detection of RASSF1A Promoter Methylation

by Hye Yeon Jang, Sthitodhi Ghosh, Chong H. Ahn, Narendhar Chandrasekar, Michael Taeyoung Hwang and Moon-Soo Kim

Chemosensors 2026, 14(7), 162; https://doi.org/10.3390/chemosensors14070162 - 13 Jul 2026

Viewed by 1697

Abstract

The detection of RASSF1A (Ras-associated domain family 1 isoform A) promoter methylation in body fluids can offer a powerful tool for the early diagnosis of bladder cancer. Zinc finger proteins (ZFPs) serve as sequence-specific recognition elements for targeting double-stranded DNA sequences. Here, we [...] Read more.

The detection of RASSF1A (Ras-associated domain family 1 isoform A) promoter methylation in body fluids can offer a powerful tool for the early diagnosis of bladder cancer. Zinc finger proteins (ZFPs) serve as sequence-specific recognition elements for targeting double-stranded DNA sequences. Here, we report a cyclic olefin copolymer (COC) chip-integrated ZFP array-based molecular sensor that bypasses the need for bisulfite conversion and PCR amplification to recognize the specific site of DNA methylation in the RASSF1A promoter. Building upon the SEER-LAC (SEquence-Enabled Reassembly of β-Lactamase) framework, we engineered a dual-recognition split-enzyme system in which a COC chip-immobilized ZFP array confers sequence specificity while a co-recruited methyl-binding domain (MBD) enforces methylation-dependent gating, together driving the proximity-induced reconstitution of functional β-lactamase at methylated target loci. Accordingly, this sensor specifically reassembles and restores enzymatic activity only in the presence of specific methylated DNA in the RASSF1A promoter region. We demonstrate that this dual-component array effectively differentiates methylation status with high specificity. Given its rapid turnaround and non-PCR-based mechanism, this system can be well-suited for developing diagnostic assays for bladder cancer, offering a potential alternative to conventional epigenetic screening methods. Full article

(This article belongs to the Section (Bio)chemical Sensing)

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15 pages, 3811 KB

Open AccessArticle

SDRMixer: A Lightweight Dynamic Response Mixer for Deployable Mixed-Gas Quantification Using Sensor Arrays

by Jiahao Zhang, Zaihua Duan, Yuanming Wu, Zhen Yuan, Yadong Jiang and Huiling Tai

Chemosensors 2026, 14(7), 161; https://doi.org/10.3390/chemosensors14070161 - 13 Jul 2026

Viewed by 354

Abstract

Low-cost gas sensor arrays are attractive for mixed-gas monitoring, but deployment-oriented modeling remains challenging because mixed-gas responses are nonlinear, cross-sensitive, and strongly dependent on sensor dynamic states. Existing electronic-nose models often rely on handcrafted response descriptors or generic sequential networks, which may either [...] Read more.

Low-cost gas sensor arrays are attractive for mixed-gas monitoring, but deployment-oriented modeling remains challenging because mixed-gas responses are nonlinear, cross-sensitive, and strongly dependent on sensor dynamic states. Existing electronic-nose models often rely on handcrafted response descriptors or generic sequential networks, which may either compress transient response information or introduce unnecessary computational cost. This work proposes SDRMixer, a lightweight sensor-specific framework for mixed-gas concentration quantification. SDRMixer uses a parameter-free sparse dynamic response encoding to organize the original sensor response, baseline-referenced excitation, and smoothed response kinetics into a physically meaningful dynamic response field. A compact temporal-feature mixer is then applied over fixed response-stage tokens for simultaneous multi-gas regression. To improve calibration coverage, a response-consistent augmentation strategy is used during model training. The proposed framework is evaluated on a previously reported mixed-gas sensor array dataset containing NO₂, NH₃, CH₄, and CO₂ mixtures. Both augmentation-enriched calibration domain benchmarking and original-measurement-based validation are conducted to assess prediction performance, computational efficiency, and stability on measured calibration samples. The results show that SDRMixer provides a good trade-off between accuracy and efficiency compared with generic deep learning architectures and compact gas-sensing baselines. These findings indicate that explicit dynamic response encoding combined with lightweight temporal-feature mixing is an effective modeling strategy for compact mixed-gas quantification within the investigated calibration domain. Full article

(This article belongs to the Special Issue Functional Nanomaterial-Based Gas Sensors and Humidity Sensors, 2nd Edition)

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19 pages, 11193 KB

Open AccessArticle

Digital Morphology Meets Chemometrics: Multi-Sensor Combination for Rapid Quality Grading and Geographical Origin Discrimination of Atractylodes lancea Rhizome

by Lu Chen, Changyun Dai, Mingjun Wang, Feilong Ren, Zhiming Zeng and Hui Ao

Chemosensors 2026, 14(7), 160; https://doi.org/10.3390/chemosensors14070160 - 12 Jul 2026

Viewed by 338

Abstract

The dried rhizome of Atractylodes lancea (RAL) is a widely used traditional Chinese medicine (TCM). Its quality evaluation and origin authentication have long relied on time-consuming chromatographic methods, which are poorly suited for rapid, on-site decisions in commercial supply chains, and existing studies [...] Read more.

The dried rhizome of Atractylodes lancea (RAL) is a widely used traditional Chinese medicine (TCM). Its quality evaluation and origin authentication have long relied on time-consuming chromatographic methods, which are poorly suited for rapid, on-site decisions in commercial supply chains, and existing studies generally focus on isolated morphological indicators without systematic digital characterization and practical on-site grading tools. Guided by the traditional empirical knowledge of “Bianzhuang Lunzhi”, which holds that external morphological traits can reflect the internal quality of TCM, this study presents the first systematic multi-dimensional digital characterization of RAL morphological traits using an integrated multi-sensor approach and quantitatively explores the underlying correlations between digital traits and key bioactive constituent contents. Nighty samples from three major producing regions were analyzed. Significant correlations were observed between odor indices, color parameters, density, oil cavity area ratio and bioactive component contents in the authentic Maoshan-sourced RAL (p < 0.01 or p < 0.05). Such associations were absent in the emerging regions (Dabie and Qin−Ba Mountains). A three-grade quality classification system based on density thresholds (Grade A: ≥0.73 g/cm³; B: 0.58–0.73 g/cm³; C: <0.58 g/cm³) was established specifically for Maoshan RAL. Additionally, an electronic nose-based classification model was constructed for geographical origin discrimination, which delivered reliable and robust classification performance in external validation with independent blind test samples. This work provides practical, low-cost tools for rapid quality grading and origin identification of RAL. The proposed trait-driven analytical strategy offers a generalizable framework for the quality control of other complex herbal medicines. Full article

(This article belongs to the Section Applied Chemical Sensors)

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34 pages, 5970 KB

Open AccessReview

Functional 2D Nanomaterials Gas Sensor for Exhaled Breath Analysis: A Review

by Yuqing Zhang, Yanjie Wang, Kun Zhu, Zhiqiang Lan, Jie Wang, Jian He, Xiujian Chou and Yong Zhou

Chemosensors 2026, 14(7), 159; https://doi.org/10.3390/chemosensors14070159 - 12 Jul 2026

Viewed by 810

Abstract

Exhaled breath analysis has emerged as a promising non-invasive approach for disease diagnosis, leveraging gas sensors for their high sensitivity, portability, and real-time monitoring capabilities. Two-dimensional nanomaterials, such as graphene, transition metal dichalcogenides (TMDs), MXenes, black phosphorus, and metal–organic frameworks (MOFs), exhibit exceptional [...] Read more.

Exhaled breath analysis has emerged as a promising non-invasive approach for disease diagnosis, leveraging gas sensors for their high sensitivity, portability, and real-time monitoring capabilities. Two-dimensional nanomaterials, such as graphene, transition metal dichalcogenides (TMDs), MXenes, black phosphorus, and metal–organic frameworks (MOFs), exhibit exceptional gas-sensing properties due to their atomic-scale thickness, ultra-large specific surface area, and tunable electronic structures. These characteristics enable enhanced gas adsorption and room-temperature operation, making them ideal for detecting ppb-level biomarkers like acetone, ammonia, and nitric oxide in breath. However, sensors based on pristine 2D materials face challenges including slow response/recovery kinetics, poor stability, weak humidity resistance, and limited selectivity in complex breath environments. To address these limitations, functionalization strategies have been developed to engineer material properties. Key approaches include heteroatom doping to modulate electronic band structures, heterojunction construction to facilitate charge transfer and improve selectivity, and noble metal decoration for catalytic enhancement of gas adsorption. Additionally, light irradiation has been employed to regulate the carrier concentration on the surface of sensitive materials. These strategies significantly boost sensor performance, achieving ppb-level detection limits, robust humidity resistance, and rapid response. Future directions involve integrating functionalized 2D materials into wearable, multiplexed sensor arrays for simultaneous biomarker detection, coupled with machine learning for real-time diagnostic platforms. Full article

(This article belongs to the Special Issue Functional Nanomaterial-Based Gas Sensors and Humidity Sensors, 2nd Edition)

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23 pages, 4528 KB

Open AccessArticle

E-Nose Classification of Muscles in Dry-Cured Bísaro Ham Using Piercing-Assisted Volatile Extraction

by Lia Vasconcelos, Javier Mateo, Nuno A. S. Dias, Ana Leite, Alfredo Teixeira, Sandra S. Q. Rodrigues and Luís G. Dias

Chemosensors 2026, 14(7), 158; https://doi.org/10.3390/chemosensors14070158 - 10 Jul 2026

Viewed by 1139

Abstract

This study evaluated the use of an E-nose using a piercing-assisted volatile extraction as a practical and non-destructive tool for distinguishing between three muscle types (biceps femoris—BF; semitendinosus—ST; and semimebranosus—SM) in 30-month ripened dry-cured Bísaro hams (n = [...] Read more.

This study evaluated the use of an E-nose using a piercing-assisted volatile extraction as a practical and non-destructive tool for distinguishing between three muscle types (biceps femoris—BF; semitendinosus—ST; and semimebranosus—SM) in 30-month ripened dry-cured Bísaro hams (n = 23). The muscles were analyzed for volatile organic compounds (VOC) using gas chromatography-mass spectrometry (GC-MS) and for signal profiles obtained from an E-nose system composed of metal oxide (SnO₂) sensors. Sensor signals were standardized using Z-score normalization prior to chemometric modeling. Linear discriminant analysis (LDA) was used to evaluate the capability of the MOS-based E-nose to differentiate the VOC profiles of Bísaro ham across its main muscle types. The model trained on Z-score-standardized sensor signals achieved classification accuracies of 94.3% and 80.0% for the training and external test sets, respectively, demonstrating good predictive performance and robustness. When compared with the VOC-based LDA model (94.4% and 78.6% for the training and test sets, respectively), the E-nose showed comparable classification performance and slightly higher predictive capability in the external validation set. The first two discriminant functions explained 88.01% and 11.99% of the discriminant variance, respectively, indicating that most of the discrimination occurred along a single dominant axis. To chemically interpret the sensor-based discrimination, multiple linear regression models were established between the LDA scores and VOC concentrations. The first discriminant function was significantly associated with compounds related to lipid oxidation and aroma development, particularly 2-pentylfuran, butanoic acid, hexanoic acid, hexanal, and benzaldehyde (R² = 0.617; p < 0.001), whereas the second discriminant function showed a weaker but significant relationship with hexanal, 3-methylbutanal, butanoic acid, and hexanoic acid (R² = 0.202; p = 0.006). These findings demonstrate that the E-nose is capable of capturing meaningful chemical information associated with muscle-specific volatile profiles and can provide a rapid, non-destructive, and cost-effective alternative for the characterization and classification of dry-cured Bísaro ham. Full article

(This article belongs to the Topic Advances in Analysis of Food and Beverages, 2nd Edition)

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20 pages, 15694 KB

Open AccessReview

Sodium Alginate-Based Hydrogels: Sensing and Indicating for Intelligent Food Packaging

by Fengchao Zhou, Liyan Xie, Guorong Lin, Yilin Lin, Jiandong Shen, Shibin Deng and Gaowa Xing

Chemosensors 2026, 14(7), 157; https://doi.org/10.3390/chemosensors14070157 - 9 Jul 2026

Viewed by 794

Abstract

Intelligent food packaging (IFP) is among the key technologies for overcoming global challenges of food safety and food resource waste. Its core lies in monitoring the quality of food in real-time without damage. Sodium alginate (SA), a natural polysaccharide characterized by biodegradability and [...] Read more.

Intelligent food packaging (IFP) is among the key technologies for overcoming global challenges of food safety and food resource waste. Its core lies in monitoring the quality of food in real-time without damage. Sodium alginate (SA), a natural polysaccharide characterized by biodegradability and excellent biocompatibility, can form hydrogels with a 3D network structure, high water content, and functional modification capability, making it an ideal matrix for developing IFP sensing and indicator platforms. Based on the gel chemistry fundamentals of SA, this paper deeply analyzes the structure-activity relationship between sensing mechanism and material structure, and summarizes the existing modification strategies and functional integration paths. The paper also provides a detailed discussion on the application principles and latest advancements of SA-based hydrogels in colorimetric/visual sensing, gas sensing, time-temperature indicator (TTI), and controlled-release carriers for active substances. The current research results show that the detection limit of SA hydrogel beads loaded with anthocyanins for volatile amines can reach 15–25 ppm, and the color difference ΔE can reach 34.2 after 7 days of storage at 4 °C, which is strongly correlated with microbial indicators, total volatile basic nitrogen (TVB-N), pH, etc. The color difference value (ΔE) response of Co-Imd microcrystalline functionalized SA film to ammonia gas reached 23.7 within 60 min, and it had antibacterial activity. The activation energy of Immobilization of laccase on sodium alginate/soluble starch microcapsules to develop a TTI (27.32–61.13 kJ/mol) was highly matched with the activation energy of Agaricus bisporus. The hydrogel microspheres loaded with Cur@Se reduced the total oxidation value of the oils by 53%. The G/SA/nZnOs cryogel pad extended the shelf life of shrimp from 4 days to 6 days at 4 °C. In addition, this paper also discusses the challenges faced by SA-based hydrogels in large-scale production and long-term stability evaluation, and looks forward to future development trends such as integration with artificial intelligence (AI), Internet of Things (IoT), and multi-functional integration, in order to provide theoretical support for in-depth research and industrial application in this field. Full article

(This article belongs to the Section Materials for Chemical Sensing)

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15 pages, 2878 KB

Open AccessArticle

A Novel Ratiometric Fluorescent Nanosensor Based on N-CDs@UiO-66-NH₂ for Sensitive and Selective Detection of Nitrite

by Tong Xiang, Chongyang Zhang, Qiongqiong Ren, Jinlong Chang, Linyan Xie and Xuming Sun

Chemosensors 2026, 14(7), 156; https://doi.org/10.3390/chemosensors14070156 - 8 Jul 2026

Viewed by 450

Abstract

Nitrite (NO₂⁻) is a crucial environmental and food safety indicator, and excessive intake poses severe threats to human health; thus, highly sensitive and selective detection methods are urgently needed. Herein, a ratiometric fluorescent nanosensor based on N-CDs@UiO-66-NH₂ was constructed [...] Read more.

Nitrite (NO₂⁻) is a crucial environmental and food safety indicator, and excessive intake poses severe threats to human health; thus, highly sensitive and selective detection methods are urgently needed. Herein, a ratiometric fluorescent nanosensor based on N-CDs@UiO-66-NH₂ was constructed via a facile assembly strategy for sensitive and selective nitrite detection. The composite demonstrated dual-emission fluorescence at 456 nm and 730 nm under 365 nm excitation, originating from UiO-66-NH₂ and N-CDs, respectively, enabling an intrinsic self-referencing signal. The fluorescence intensity ratio exhibited a good linear response toward nitrite in the range of 10–100 μM and 100–450 μM with a limit of detection (LOD) 1.76 μM. The nanosensor showed high selectivity and anti-interference ability against various ions and molecules. Furthermore, it was successfully applied to nitrite detection in lake water samples with satisfactory recoveries (97–101%) and low relative standard deviations (<1.8%). This work provides a simple and effective approach for nitrite monitoring in environmental samples. Full article

(This article belongs to the Special Issue Advancements of Chemosensors and Biosensors in China—3rd Edition)

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34 pages, 4697 KB

Open AccessReview

Chemoresistive Metal Oxide-Based Sensors Synthesized Through Physical Vapor Deposition Techniques for Gas Detection

by Andrei-Silviu Zancu, Mihai Robert Zamfir, Nicolae Cristian Mihailescu, Constantin Pintilie and Nicu Doinel Scărișoreanu

Chemosensors 2026, 14(7), 155; https://doi.org/10.3390/chemosensors14070155 - 7 Jul 2026

Cited by 1 | Viewed by 737

Abstract

In our day-to-day lives, we are regularly exposed to a wide spectrum of dangerous gases. Their origins vary, ranging from industrial activities to objects found within our very homes. Naturally, there is an interest in developing cost-efficient and durable devices that can successfully [...] Read more.

In our day-to-day lives, we are regularly exposed to a wide spectrum of dangerous gases. Their origins vary, ranging from industrial activities to objects found within our very homes. Naturally, there is an interest in developing cost-efficient and durable devices that can successfully track these gases within our environment. One such candidate is represented by chemoresistive gas sensors based on metal oxides. This is due to their simple architecture and the possibility of scaling down their size, making them valid contenders for future advancements in portable gas sensors. This review focuses on chemoresistive gas sensors that have been obtained through different Physical Vapor Deposition (PVD) methods, which are easily scalable for potential technological transfer towards commercialization or are already exploited at the industrial level, and how varying different deposition parameters impacts the structure of the active material, thus modifying the gas sensing properties of the device. In this review, we report results obtained for different metal oxides: WO₃, ZnO, CeO₂, TiO₂, NiO, and SnO₂. The main findings of these studies revealed that the sensor’s response was highly impacted by oxygen deficiencies within the deposited material, the specific surface area, and the thickness of the film. Moreover, this study also delves into different strategies of functionalization that result in improved gas sensing properties. Thus, we herein report how tailoring functional properties modifies the gas sensing performance of different metal oxides. Full article

(This article belongs to the Section Materials for Chemical Sensing)

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21 pages, 4197 KB

Open AccessArticle

Machine Learning-Based Calibration of Low-Cost PM_2.5 Sensors Using Location-Specific Environmental Covariates and Feature Engineering Strategies

by Hayrettin Gökozan

Chemosensors 2026, 14(7), 154; https://doi.org/10.3390/chemosensors14070154 - 4 Jul 2026

Viewed by 551

Abstract

In this study, an integrated machine learning (ML)-based calibration approach was employed for the data-driven calibration of a low-cost PM_2.5 sensor. The primary objective was to systematically evaluate the relative contributions of location-specific environmental covariates, auxiliary gaseous pollutants, and Feature Engineering (FE) [...] Read more.

In this study, an integrated machine learning (ML)-based calibration approach was employed for the data-driven calibration of a low-cost PM_2.5 sensor. The primary objective was to systematically evaluate the relative contributions of location-specific environmental covariates, auxiliary gaseous pollutants, and Feature Engineering (FE) strategies in the calibration of a low-cost PM_2.5 sensor deployed in an outdoor residential environment. For this purpose, a multi-stage experimental framework based on an ablation study design incorporating different environmental information groups was implemented, and the models were evaluated using a leakage-safe time-based validation approach. The results demonstrated that FE strategies significantly improved model performance across all experimental configurations. The highest performance was obtained under the configuration using raw PM_2.5 sensor data together with FE-derived features, where the RF model increased the Test R² value from 0.7237 to 0.8467 and reduced the RMSE from 6.96 µg/m³ to 5.19 µg/m³. Feature importance analyses indicated that humidity-based interaction features and cyclic temporal encoding structures provided substantial contributions to model performance. The findings suggest that meaningful PM_2.5 calibration performance can be achieved using location-specific environmental covariates and data-driven FE strategies in scenarios where continuous co-location is not operationally feasible. Full article

(This article belongs to the Section Analytical Methods, Instrumentation and Miniaturization)

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47 pages, 22409 KB

Open AccessReview

Single-Entity Electrochemistry for Analytical Chemistry: Moving Towards the Limits of Detecting Single Molecules and Single Cells

by Li Fu, Fei Chen, Yanfei Lv, Shichao Zhao and Cheng-Te Lin

Chemosensors 2026, 14(7), 153; https://doi.org/10.3390/chemosensors14070153 - 3 Jul 2026

Viewed by 841

Abstract

Single-entity electrochemistry (SEE) expands the scope of analytical electrochemical measurement by shifting attention from ensemble-averaged currents to individually resolved stochastic events. This review evaluates progress toward two analytical endpoints, trustworthy detection of single molecules and context-preserving interrogation of single cells, with emphasis on [...] Read more.

Single-entity electrochemistry (SEE) expands the scope of analytical electrochemical measurement by shifting attention from ensemble-averaged currents to individually resolved stochastic events. This review evaluates progress toward two analytical endpoints, trustworthy detection of single molecules and context-preserving interrogation of single cells, with emphasis on quantitative rigor rather than platform novelty alone. Across nanoparticle collisions, nanopores, confined nanoelectrodes, vesicle electrochemical cytometry, intracellular nanopipettes, and array-enabled single-cell devices, the decisive analytical issue is no longer simply whether one entity can be detected, but whether event assignment, calibration, throughput, and reproducibility are sufficient to support credible inference. Representative primary studies are compared through shared metrics including event frequency, temporal resolution, bandwidth, molecular counts, detection limit, affinity, and effective yield of analyzable events. Particular attention is given to three recurring bottlenecks: interfacial variability, model-dependent event interpretation, and incomplete reporting of denominators such as rejected events, insertion success, and pore-to-pore or cell-to-cell reproducibility. The current evidence base is strongest in secretion and vesicle studies, whereas confinement-enabled and multimodal routes define the leading edge of single-molecule analysis. Overall, SEE is developing not as a single universal platform, but as a family of interface-controlled, data-rich analytical strategies whose future analytical value will depend on standardized reporting, multimodal validation, and benchmarking practices that preserve both sensitivity and confidence of assignment. Full article

(This article belongs to the Special Issue Electrochemical Sensors and Biosensors: Recent Progress, Challenges, and Future Perspectives)

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17 pages, 8407 KB

Open AccessArticle

Rapid Geographical Origin Discrimination of Tremella fusiform Based on Temporal Response Features of Electronic Nose

by Ying Li, Meng Liu, Zhaomin Sun, Lei Yu, Feifei Gong and Guangyu Yan

Chemosensors 2026, 14(7), 152; https://doi.org/10.3390/chemosensors14070152 - 1 Jul 2026

Cited by 1 | Viewed by 335

Abstract

Rapid geographical origin discrimination of Tremella fuciformis is important for quality control and authenticity assessment; however, conventional analytical methods are often time-consuming and require complex sample preparation. In this study, a rapid discrimination approach was established by integrating electronic nose (E-nose) response fingerprints [...] Read more.

Rapid geographical origin discrimination of Tremella fuciformis is important for quality control and authenticity assessment; however, conventional analytical methods are often time-consuming and require complex sample preparation. In this study, a rapid discrimination approach was established by integrating electronic nose (E-nose) response fingerprints with machine learning. To capture temporal variation in the E-nose signals, fingerprint features were extracted from three response windows: the selected overall response window (0–69 s), the early response window (0–29 s), and the relatively stable response window (56–65 s). Random forest, partial least squares discriminant analysis (PLS-DA), Gaussian naive Bayes, nearest centroid, and decision tree were then constructed and evaluated. Classification performance varied among the temporal-window feature sets. Based on 100 repeated stratified random splits, PLS-DA model using the 56–65 s feature window achieved the best overall classification performance, with accuracy, balanced accuracy, F1-score (the harmonic mean of precision and recall), and ROC-AUC (the area under the receiver operating characteristic curve) values of 0.9933 ± 0.0255, 0.9928 ± 0.0256, 0.9919 ± 0.0293, 0.9991 ± 0.0085, respectively. These findings indicate that E-nose fingerprinting combined with PLS-DA may provide a rapid and effective method for geographical origin discrimination of T. fuciformis. Full article

(This article belongs to the Section Applied Chemical Sensors)

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15 pages, 1522 KB

Open AccessArticle

Formulation-Aware SW-NIR Spectroscopic Sensing of Bread Staling Using Stratified Chemometric Modeling and Wavelength Selection

by Shuai Lu, Jiakang Sheng, Yibo Xu, Fan Zhang and Xingyu Song

Chemosensors 2026, 14(7), 151; https://doi.org/10.3390/chemosensors14070151 - 1 Jul 2026

Viewed by 358

Abstract

Short-wave near-infrared (SW-NIR) spectroscopy provides a rapid and nondestructive sensing route for monitoring bread staling, but formulation-dependent moisture redistribution and starch retrogradation can make pooled spectral regression unstable. This study investigated a stratified SW-NIR modeling strategy for bread staling prediction using 324 spectra [...] Read more.

Short-wave near-infrared (SW-NIR) spectroscopy provides a rapid and nondestructive sensing route for monitoring bread staling, but formulation-dependent moisture redistribution and starch retrogradation can make pooled spectral regression unstable. This study investigated a stratified SW-NIR modeling strategy for bread staling prediction using 324 spectra from control bread (CR) and two maltogenic

α

-amylase treatments (EZ1 and EZ2). A global full-spectrum partial least squares (PLS) model was compared with bread-type-specific PLS models; competitive adaptive reweighted sampling (CARS), support vector machine recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble LASSO (MFE-LASSO) were then each coupled with PLS and evaluated within each bread type. The pooled benchmark achieved a root mean square error of prediction (RMSEP) of 2.28 days, whereas stratified full-spectrum PLS reduced this to 1.86, 2.14, and 2.15 days for CR, EZ1, and EZ2, respectively. In repeated wavelength-selection runs, MFE-LASSO was the most consistently competitive method across bread types. In the representative best-model comparison, MFE-LASSO-PLS yielded the strongest performance for CR (RMSEP = 1.71 days) and EZ1 (RMSEP = 1.43 days), while CARS-PLS gave the lowest RMSEP for EZ2 (2.00 days). An exploratory position-specific analysis within the CR subset further suggested that the middle crumb region carried stronger staling-related spectral information than the top and bottom regions. These results indicate that formulation-aware SW-NIR spectroscopic sensing is a practical strategy for nondestructive bread-staling assessment and that the optimal wavelength-selection method is bread-type-dependent. Full article

(This article belongs to the Special Issue Spectroscopy, Imaging and Chemometric Modelling for Quality Analysis in Food and Drug Research)

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21 pages, 7086 KB

Open AccessArticle

Rational Design of a Hydrophobic Ion-Pair Sensor for Potentiometric Determination of Cationic Surfactants in Disinfectants: Combined Experimental and DFT Study

by Marija Kraševac Sakač, Maksym Fizer, Hanna Zhukouskaya, Martin Hrubý, Jiří Pánek, Jasmin Suljagić, Dean Marković, Domagoj Drenjančević, Nikola Sakač, Martina Šrajer Gajdošik and Marija Jozanović

Chemosensors 2026, 14(7), 150; https://doi.org/10.3390/chemosensors14070150 - 1 Jul 2026

Viewed by 595

Abstract

Cationic surfactants are widely used in disinfectants, creating a need for rapid and reliable analytical methods for their determination in complex formulations. In this study, a new hydrophobic ion-pair, 1,3-didecyl-2-methylimidazolium tetrakis(perfluorophenyl)borate (DDMIm–TPFPhB), was developed and applied as an ionophore in a potentiometric sensor. [...] Read more.

Cationic surfactants are widely used in disinfectants, creating a need for rapid and reliable analytical methods for their determination in complex formulations. In this study, a new hydrophobic ion-pair, 1,3-didecyl-2-methylimidazolium tetrakis(perfluorophenyl)borate (DDMIm–TPFPhB), was developed and applied as an ionophore in a potentiometric sensor. The ion-pair was incorporated into a PVC membrane and evaluated by direct potentiometric measurements and titrations. The sensor exhibited near-Nernstian responses toward selected cationic surfactants (56.8–59.1 mV per decade), low detection limits (1.4–2.2 × 10⁻⁶ M), and stable signal behavior, along with good selectivity and stability over a pH range of 3–9. Application on commercial disinfectant samples showed good agreement with a commercial ion-selective electrode. According to the charge decomposition analysis performed using density functional theory calculations, the number of electrons donated from perfluorotetraphenyl borate to 1,3-didecyl-2-methylimidazolium is 0.25 e. In contrast, the back-donation from the cation to the anion is only 0.05 e, indicating a relatively substantial overall charge transfer of 0.20 e. This pronounced charge transfer, together with dominant dispersion interactions, contributes to enhanced ion-pair stability within the membrane phase, which is reflected in reduced signal drift and improved analytical performance. These findings establish a direct link between molecular-level interactions and sensor behavior, providing a rational basis for the design of potentiometric sensors for real-sample analysis. Full article

(This article belongs to the Special Issue Potentiometric Sensors in Analytical Chemistry)

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13 pages, 7089 KB

Open AccessArticle

Ultrasensitive and Selective Immuno-Magnetic Ratiometric Fluorescent Sensor for Aflatoxin B1 in Food Matrices

by Ming Li and Xi Zhang

Chemosensors 2026, 14(7), 149; https://doi.org/10.3390/chemosensors14070149 - 1 Jul 2026

Viewed by 353

Abstract

Aflatoxin B1 (AFB1), a highly carcinogenic mycotoxin, has been the focus of research for the development of efficient detection methods. In this study, a novel magnetic immuno-ratiometric fluorescent sensing system was constructed for the quantitative detection of AFB1. Green-emitting carbon quantum dots were [...] Read more.

Aflatoxin B1 (AFB1), a highly carcinogenic mycotoxin, has been the focus of research for the development of efficient detection methods. In this study, a novel magnetic immuno-ratiometric fluorescent sensing system was constructed for the quantitative detection of AFB1. Green-emitting carbon quantum dots were conjugated with AFB1 monoclonal antibody to obtain GCDs@AFB1 mAb, and AFB1 oxime was immobilized on Fe₃O₄ magnetic microspheres to prepare AFB1-Ox@Fe₃O₄ NPs. After the immune-competitive adsorption of GCDs@AFB1 mAb by AFB1-Ox@Fe₃O₄ NPs and free AFB1, magnetic separation was performed. Red fluorescent silver nanoclusters were introduced as an internal reference to construct a GCDs-AgNCs ratiometric fluorescent system. The sensor exhibited a good linear response in the range of 0~240 pg/mL with a low limit of detection of 18 pg/mL and excellent selectivity. The spiked recoveries in real samples ranged from 92.14% to 110.02%, with a relative standard deviation of 0.57% to 4.58%. Combining the specific antigen–antibody recognition with magnetic separation technology, this method addresses the issues of poor stability and high environmental interference of traditional fluorescent sensors, and provides a new strategy for the sensitive and stable detection of AFB1. Full article

(This article belongs to the Section Optical Chemical Sensors)

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16 pages, 5740 KB

Open AccessArticle

Assessment of Cooked Meatballs’ Edibility Using Calibrated MOS Sensors and Microbiological Validation

by Luigi Masi, Revathy Gurusamy, Daniel Garcia-Romeo, Andreas Schütze, Rafael Pagán and Christian Bur

Chemosensors 2026, 14(7), 148; https://doi.org/10.3390/chemosensors14070148 - 30 Jun 2026

Cited by 1 | Viewed by 603

Abstract

Food waste is often driven by consumer uncertainty about the spoilage of stored food, especially for cooked meal leftovers where microbial growth is the main concern. We analyzed whether metal oxide semiconductor (MOS) gas sensors placed inside ordinary food containers can monitor the [...] Read more.

Food waste is often driven by consumer uncertainty about the spoilage of stored food, especially for cooked meal leftovers where microbial growth is the main concern. We analyzed whether metal oxide semiconductor (MOS) gas sensors placed inside ordinary food containers can monitor the edibility of leftovers, specifically cooked meatballs. Sensors were operated using temperature cycling to enhance selectivity, and cycle-aligned features were extracted. A prior calibration campaign produced information used to map cycle-aligned features into estimated gas concentrations for relevant VOCs. Total viable counts, which represent the growth of total number of spoilage microorganisms, were analyzed on days 0, 5 and 7 to determine the food’s freshness. Both the raw sensor features and the calibration-derived gas concentration estimates were analyzed with principal component analysis (PCA) and evaluated with a leave-one-sensor-out (LOSO) binary classifier for multiple food containers. PCA on the calibrated gas estimates revealed a dominant axis that consistently tracks food degradation over time across various containers. LOSO classification accuracy improved from 81.7% using raw sensor features to 87.8% using calibrated gas concentration estimates. These findings represent a proof of principle that calibrated MOS sensor systems can robustly support in situ edibility assessment for cooked food. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality: 2nd Edition)

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21 pages, 2990 KB

Open AccessArticle

A Hybrid Probabilistic Framework for Temporal Drift Compensation in Conductimetric Biosensors: Combining Machine Learning Predictions with Bayesian Latent Process Modeling

by Sid-Ali Kouras, Ramdane Mahamdi and Fouad Kerrour

Chemosensors 2026, 14(7), 147; https://doi.org/10.3390/chemosensors14070147 - 29 Jun 2026

Viewed by 342

Abstract

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive [...] Read more.

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive degradation of the sensing layer. The biosensor targets the urea concentration range 0.01–30 mM, validated against experimental data and covering the clinically relevant range for blood urea detection (2.5–7.5 mM), urine (20–40 mM), and environmental monitoring applications. Conventional calibration techniques, such as the conventional calibration method (based on reference measurements), and purely deterministic correction methods, such as deterministic methods (based on known fixed equations), often prove insufficient because they struggle to capture the non-stationary and inherently stochastic nature of these drifts. In this work, we propose an original hybrid probabilistic framework that synergistically combines machine learning and Bayesian inference for robust adaptive drift compensation. A Random Forest model is first implemented to model the deterministic nonlinear relationships between environmental parameters (temperature, pH, CO₂ concentration) and the sensor response. The residual temporal drift is then explicitly modeled as a non-stationary latent stochastic process using Bayesian inference based on a Gaussian process. This approach allows continuous online model updating, real-time uncertainty quantification, and automatic detection of anomalies. The models were trained and validated on a large dataset obtained from multiphysics simulations carried out in COMSOL Multiphysics 5.6. These simulations incorporated enzymatic reactions, thermal effects, and chemical dynamics taking place inside the sensor. Experimental results show that the hybrid approach substantially enhances sensor performance, lowering the root mean square error (RMSE) to below 0.8 μS/cm (corresponding to less than 0.5% of the full-scale response) over a wide temperature range (15–45 °C) and across extended operating periods. This represents a clear improvement over conventional compensation method. By merging the predictive power of ensemble learning with a probabilistic Bayesian model of dynamic drift, this study introduces a fresh perspective on the design of intelligent, self-adaptive, and drift-resistant conductimetric biosensors. The proposed framework holds strong potential for reliable, long-term autonomous operation in urea reliable, long-term autonomous operation in urea monitoring across biomedical diagnostics (kidney/liver function assessment) and environmental surveillance (water eutrophication prevention). Full article

(This article belongs to the Topic Recent Advances in Chemical Artificial Intelligence)

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31 pages, 24757 KB

Open AccessReview

Transformative Impacts of Laser-Induced Breakdown Spectroscopy on Environmental and Biological Research at Oak Ridge National Laboratory

by Madhavi Martin

Chemosensors 2026, 14(7), 146; https://doi.org/10.3390/chemosensors14070146 - 26 Jun 2026

Viewed by 463

Abstract

This manuscript will present an advancement of transformative research that has been conducted at Oak Ridge National Laboratory (ORNL) over a 25-year period (2000–2025) on a variety of environmental and biological matrices. These investigations derived a fundamental understanding of how elemental detection and [...] Read more.

This manuscript will present an advancement of transformative research that has been conducted at Oak Ridge National Laboratory (ORNL) over a 25-year period (2000–2025) on a variety of environmental and biological matrices. These investigations derived a fundamental understanding of how elemental detection and analysis of these matrices led to the knowledge and discovery of natural processes in plants and the environment. Each project led to the initiation of a new research area which unearthed awesome and novel breakthroughs. Highlights are listed below: 1. The preliminary research at ORNL centered on the detection of aerosols utilizing Laser-induced Breakdown Spectroscopy (LIBS) technology. The Clean Air Act Amendment (CAAA) of 1990 highlighted the importance of identifying hazardous air pollutants (HAPs) due to their impact on environmental and human health, thereby underscoring the need to detect various toxic elements. Research in aerosol chemistry aimed to identify these harmful elements released by factories during periods of increased emissions in their manufacturing processes. LIBS emerged as the most effective method for real-time, in situ measurements of metal species in both gaseous and aerosol phases. 2. An understanding of the presence of total carbon in soils gives perspective on how to develop carbon sequestration strategies. The recognition that carbon sinks can evolve back to carbon sources to emit back to the atmosphere was an important consideration. Also, the concentration of carbon in soil indicates the health of land areas for growing crops successfully. 3. The direct detection of most of the elements in a wood sample in a single emission spectrum, without sample preparation, encouraged the research to use the LIBS technique for preservative treated wood coupled with use of multivariate statistical methodology. Additionally, it encouraged the researchers to try to differentiate natural woods from different parts of the country, and it was successfully demonstrated that LIBS coupled with MVA analysis could differentiate wood of different species from each other and of similar species grown in different environments based on their elemental spectra. This was a breakthrough since it revealed a systematic approach to connect elemental scarcity and abundance to either drought or typical rainfall conditions for the hardwood trees grown in specific areas. 4. Furthermore, the research progressed to reveal physiological and developmental processes contributing to biomass production such that the variation in leaf elemental composition increases our understanding of terrestrial nutrient cycles, as well as tracking the transfer of toxic elements from soils to living organisms. 5. Recently another breakthrough viz., ionomics initiated the correlation of elements to specific genes, uncovering the function that the element performed in the plant. More recently, this has been extended from plants to fungi as well as fungi growing in symbiotic relations with plants. Full article

(This article belongs to the Special Issue Application of Laser-Induced Breakdown Spectroscopy, 3rd Edition)

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19 pages, 11374 KB

Open AccessArticle

Portable Multi-Spectral Sensing Platform and Self-Metering Microfluidic Strips for Quantitative Monitoring of o-Phthalaldehyde Disinfectants

by Hsien-Yi Hsiao, Tzong-Jih Cheng, Hung-Yu Chen and Richie L. C. Chen

Chemosensors 2026, 14(7), 145; https://doi.org/10.3390/chemosensors14070145 - 24 Jun 2026

Viewed by 426

Abstract

Routine monitoring of ortho-phthalaldehyde (OPA) disinfectants is critical for endoscope reprocessing, yet commercial test strips suffer from subjective visual ambiguity, strict manual timing, and susceptibility to sample matrix dilution. This study proposes a portable multi-spectral colorimetric sensing platform paired with structurally engineered [...] Read more.

Routine monitoring of ortho-phthalaldehyde (OPA) disinfectants is critical for endoscope reprocessing, yet commercial test strips suffer from subjective visual ambiguity, strict manual timing, and susceptibility to sample matrix dilution. This study proposes a portable multi-spectral colorimetric sensing platform paired with structurally engineered microfluidic plastic strips for quantitative OPA monitoring. The strips utilize a confined microfluidic geometry to achieve capillary-driven volumetric self-metering (5.4 μL), while cross-hatched micro-structures eliminate edge pooling, yielding uniform colorimetric responses. Analytically, the system integrates a matrix-matched reagent formulation, an interference-free indicator, and an automated steady-state ratiometric readout algorithm to counteract physical dilution and spectral interference. Cross-validation against a capillary electrophoresis benchmark confirmed quantitative accuracy (R² = 0.9684) under physical dilution of real-world CIDEX OPA solutions. This correlation facilitated a matrix-compensated 0.32% diagnostic threshold for unambiguous, automated “[PASS]” or “[FAIL]” alerts. Ultimately, this scalable, cost-effective microfluidic architecture provides an objective point-of-care diagnostic solution, demonstrating translational potential for broad dry chemistry optical detection. Full article

(This article belongs to the Section Analytical Methods, Instrumentation and Miniaturization)

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