Pub Date : 2026-04-01Epub Date: 2026-02-09DOI: 10.1016/j.microc.2026.117327
Liying Liu , Jingrui Yin , Yun Zhou, Jiayuan Liu, Xiaoting Li, Hu Ren, Honglin Wang, Liming Fan
The establishment of reliable and non-invasive urinary phenylglyoxylic acid (PGA) detection remains a critical occupational health priority, given its role as the primary exposure biomarker for styrene (SM) in industrial workers. In this work, an anionic 3D lead(II) organic framework of {[Pb(TPTA)0.5]·Me2NH2}n (PbMOF) was fabricated under hydrothermal condition and displayed dual-emission features and emitted bright yellow luminescence under UV excitation, demonstrating exceptional potential as a highly efficient luminescent sensor for PGA. It exhibited remarkable selectivity, superior sensitivity, an ultralow detection limit (LOD) of 100.4 nM, and a limit of quantification (LOQ) of 334 nM. Leveraging the photochromic response of PbMOF toward PGA under UV irradiation, an integrated logic gates based intelligent detection system was established to simultaneously enhance operational convenience and intelligence. These results not only establish PbMOF as a promising intelligent platform for early diagnostic of SM exposure biomarker, but also provides a feasible strategy to construct non-rare earth pristine MOF based ratiometric luminescent sensor.
{"title":"Anionic PbMOF based yellow-emitting ratiometric luminescence sensor for efficient detection of styrene biomarker","authors":"Liying Liu , Jingrui Yin , Yun Zhou, Jiayuan Liu, Xiaoting Li, Hu Ren, Honglin Wang, Liming Fan","doi":"10.1016/j.microc.2026.117327","DOIUrl":"10.1016/j.microc.2026.117327","url":null,"abstract":"<div><div>The establishment of reliable and non-invasive urinary phenylglyoxylic acid (PGA) detection remains a critical occupational health priority, given its role as the primary exposure biomarker for styrene (SM) in industrial workers. In this work, an anionic 3D lead(II) organic framework of {[Pb(TPTA)<sub>0.5</sub>]·Me<sub>2</sub>NH<sub>2</sub>}<sub>n</sub> (PbMOF) was fabricated under hydrothermal condition and displayed dual-emission features and emitted bright yellow luminescence under UV excitation, demonstrating exceptional potential as a highly efficient luminescent sensor for PGA. It exhibited remarkable selectivity, superior sensitivity, an ultralow detection limit (LOD) of 100.4 nM, and a limit of quantification (LOQ) of 334 nM. Leveraging the photochromic response of PbMOF toward PGA under UV irradiation, an integrated logic gates based intelligent detection system was established to simultaneously enhance operational convenience and intelligence. These results not only establish PbMOF as a promising intelligent platform for early diagnostic of SM exposure biomarker, but also provides a feasible strategy to construct non-rare earth pristine MOF based ratiometric luminescent sensor.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117327"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-10DOI: 10.1016/j.microc.2026.117331
Ziyue Chen , Jingwen Zhu , Jihong Deng , Yuhan Ding , Congli Mei , Hui Jiang
A colorimetric sensor array (CSA) constructed from nine natural anthocyanins was developed to enable qualitative analysis of aflatoxin B1 (AFB1) in soybeans. This CSA effectively captures information on volatile organic compounds (VOCs) released from soybeans exhibiting varying levels of AFB1 contamination. After dimensionality reduction of the collected data using t-SNE, classification was performed with support vector machine (SVM) models optimized by grey wolf optimization (GWO), particle swarm optimization (PSO) and rime-ice optimization (RIME). Among these, the RIME-SVM model achieved the best performance, reaching an accuracy of 96.67% in distinguishing whether the AFB1 content exceeded the maximum limit of 5 μg/kg specified by the National Food Safety Standard of China (GB 2761–2017). Overall, this study establishes a new method for differentiating AFB1 contamination levels in soybeans, addressing the challenges of conventional detection techniques—namely their operational complexity, high cost and limited suitability for rapid on-site screening—and demonstrating promising prospects for applications in food safety monitoring.
{"title":"Qualitative analysis of aflatoxin B1 in soybeans using an anthocyanin-based colorimetric sensor array","authors":"Ziyue Chen , Jingwen Zhu , Jihong Deng , Yuhan Ding , Congli Mei , Hui Jiang","doi":"10.1016/j.microc.2026.117331","DOIUrl":"10.1016/j.microc.2026.117331","url":null,"abstract":"<div><div>A colorimetric sensor array (CSA) constructed from nine natural anthocyanins was developed to enable qualitative analysis of aflatoxin B1 (AFB1) in soybeans. This CSA effectively captures information on volatile organic compounds (VOCs) released from soybeans exhibiting varying levels of AFB1 contamination. After dimensionality reduction of the collected data using t-SNE, classification was performed with support vector machine (SVM) models optimized by grey wolf optimization (GWO), particle swarm optimization (PSO) and rime-ice optimization (RIME). Among these, the RIME-SVM model achieved the best performance, reaching an accuracy of 96.67% in distinguishing whether the AFB1 content exceeded the maximum limit of 5 μg/kg specified by the National Food Safety Standard of China (GB 2761–2017). Overall, this study establishes a new method for differentiating AFB1 contamination levels in soybeans, addressing the challenges of conventional detection techniques—namely their operational complexity, high cost and limited suitability for rapid on-site screening—and demonstrating promising prospects for applications in food safety monitoring.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117331"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-06DOI: 10.1016/j.microc.2026.117297
Dennis S. Ferreira , Raffaele Vitale , Edenir R. Pereira-Filho
The efficient management of electronic waste (e-waste) is a global challenge, limited by the high heterogeneity of the material, which hinders the recovery of valuable metals. This study presents an alternative approach to automated e-waste sorting, based on the hypothesis that different categories of devices have a distinctive elemental fingerprint, even after being ground and homogenized. Samples of printed circuit boards (PCBs) from three categories (computers/laptops, cellphones, and tablets) were processed and analyzed by Laser-Induced Breakdown Spectroscopy (LIBS). A total of 58 samples were systematically analyzed, with 130 individual spectra acquired per sample, yielding 7540 individual LIBS spectra for comprehensive model development. A machine learning pipeline was developed, comparing feature selection/compression strategies (ANOVA, Partial Least Squares Discriminant Analysis (PLS-DA), and Principal Component Analysis (PCA), oversampling strategies (Random OverSampler, SMOTE, ADASYN), and four well-established classification algorithms (Logistic Regression, Random Forest, KNN, and RBF-SVM). Systematic adjustment revealed distinct advantages for two approaches: PCA-RF combined with SMOTE achieved the highest classification performance (F1-score 0.7611), while ANOVA-LR (F1-score 0.7323) demonstrated superior stability and direct chemical interpretability. In external validation (conducted on an independent test set), the model obtained an F1-score of 0.7611, demonstrating high robustness and generalization ability. In addition, a Leave-One-Device-Out (LODO) validation scheme consistently yielded an average F1-score of 0.7568 across 16 different devices, confirming the robustness of the model against brand and model variability. Chemical interpretation, supported by ANOVA variable selection, permitted to identify Al, Si, Sn, Ba, Ca, Cu, K, and Na as the most relevant discriminating markers, linking statistical separation to specific characteristics such as substrate composition, miniaturized capacitors, and soldering materials. It is concluded that the proposed method is a viable and promising pre-sorting strategy for the recycling industry, enabling rapid and chemically interpretable classification.
{"title":"E-waste device category classification combining LIBS and machine learning","authors":"Dennis S. Ferreira , Raffaele Vitale , Edenir R. Pereira-Filho","doi":"10.1016/j.microc.2026.117297","DOIUrl":"10.1016/j.microc.2026.117297","url":null,"abstract":"<div><div>The efficient management of electronic waste (e-waste) is a global challenge, limited by the high heterogeneity of the material, which hinders the recovery of valuable metals. This study presents an alternative approach to automated e-waste sorting, based on the hypothesis that different categories of devices have a distinctive elemental fingerprint, even after being ground and homogenized. Samples of printed circuit boards (PCBs) from three categories (computers/laptops, cellphones, and tablets) were processed and analyzed by Laser-Induced Breakdown Spectroscopy (LIBS). A total of 58 samples were systematically analyzed, with 130 individual spectra acquired per sample, yielding 7540 individual LIBS spectra for comprehensive model development. A machine learning pipeline was developed, comparing feature selection/compression strategies (ANOVA, Partial Least Squares Discriminant Analysis (PLS-DA), and Principal Component Analysis (PCA), oversampling strategies (Random OverSampler, SMOTE, ADASYN), and four well-established classification algorithms (Logistic Regression, Random Forest, KNN, and RBF-SVM). Systematic adjustment revealed distinct advantages for two approaches: PCA-RF combined with SMOTE achieved the highest classification performance (F1-score 0.7611), while ANOVA-LR (F1-score 0.7323) demonstrated superior stability and direct chemical interpretability. In external validation (conducted on an independent test set), the model obtained an F1-score of 0.7611, demonstrating high robustness and generalization ability. In addition, a Leave-One-Device-Out (LODO) validation scheme consistently yielded an average F1-score of 0.7568 across 16 different devices, confirming the robustness of the model against brand and model variability. Chemical interpretation, supported by ANOVA variable selection, permitted to identify Al, Si, Sn, Ba, Ca, Cu, K, and Na as the most relevant discriminating markers, linking statistical separation to specific characteristics such as substrate composition, miniaturized capacitors, and soldering materials. It is concluded that the proposed method is a viable and promising pre-sorting strategy for the recycling industry, enabling rapid and chemically interpretable classification.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117297"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-14DOI: 10.1016/j.microc.2026.117295
Mei Wang , Hui Yuan , Xinmin He , Lingling Yang , Zhigang Yang , Tao Gao , Weibiao Wang , Fen Ma , Weiman Zhang , Gidion Wilson Mening' oo , Yuping Sa , Xiaofei Chen , Guoning Chen , Xueqin Ma
Hyaluronidase (HAase), an enzyme responsible for hyaluronic acid (HA) degradation, compromises the structural integrity of HA by cleaving anti-inflammatory high-molecular-weight HA into pro-inflammatory lower molecular weight forms. Consequently, developing simple and efficient strategies for screening HAase inhibitors is of critical importance. Herein, a novel biosensing-based targeted affinity screening method was established for the identification of HAase inhibitors from complex herbal medicines. In the biosensing system, carbon dots served as fluorescent probes, while HA-functionalized gold nanoparticles acted as quenchers, and the addition of HAase induced a marked fluorescence recovery. Under optimized conditions, HAase exhibited a good linear response over the concentration range of 5.3 to 343.8 U mL−1. This method enables rapid and efficient detection of HAase inhibitory activity in complex herbal extracts. Subsequently, active components were selectively captured, separated, and identified using immobilized HAase affinity chromatography. Following validation with both negative and positive control drugs, the screening model successfully identified Rhodiola rosea L. among 43 herbal medicines as a potent HAase. Gallic acid (GA) was identified as the active constituent, exhibiting an IC50 value of 111.7 μM. Results from molecular docking and molecular dynamics simulations demonstrated a strong binding affinity between GA and HAase. Additionally, cellular experiments confirmed the anti-inflammatory activity of GA, as evidenced by its significant inhibition of LPS-induced inflammatory markers IL-4, IL-5, IL-6, and TNF-α. Collectively, the integrated biosensing and affinity-based screening strategy established in this study provides a precise and efficient platform for the discovery of bioactive compounds from complex natural products.
透明质酸酶（HAase）是一种负责透明质酸（HA）降解的酶，它通过将抗炎的高分子量HA切割成促炎的低分子量形式，从而破坏透明质酸的结构完整性。因此，开发简单有效的筛选HAase抑制剂的策略至关重要。本研究建立了一种基于生物传感的靶向亲和力筛选方法，用于鉴定复方中草药中HAase抑制剂。在生物传感系统中，碳点作为荧光探针，ha功能化金纳米粒子作为猝灭剂，加入HAase后荧光恢复明显。在优化条件下，在5.3 ~ 343.8 U mL−1的浓度范围内，HAase表现出良好的线性响应。该方法能够快速有效地检测复方草药提取物中HAase抑制活性。随后，使用固定化HAase亲和层析选择性地捕获、分离和鉴定活性成分。通过阴性对照药和阳性对照药的验证，筛选模型成功地在43种草药中鉴定出红景天为强效HAase。未食子酸（GA）为活性成分，IC50值为111.7 μM。分子对接和分子动力学模拟结果表明，GA和HAase具有很强的结合亲和力。此外，细胞实验证实了GA的抗炎活性，其显著抑制lps诱导的炎症标志物IL-4、IL-5、IL-6和TNF-α。总之，本研究建立的基于生物传感和亲和的综合筛选策略为从复杂的天然产物中发现生物活性化合物提供了一个精确而高效的平台。
{"title":"A precision inhibitor screening strategy from complex herbal medicine for hyaluronidase based on carbon quantum dot sensing and targeted capture technology","authors":"Mei Wang , Hui Yuan , Xinmin He , Lingling Yang , Zhigang Yang , Tao Gao , Weibiao Wang , Fen Ma , Weiman Zhang , Gidion Wilson Mening' oo , Yuping Sa , Xiaofei Chen , Guoning Chen , Xueqin Ma","doi":"10.1016/j.microc.2026.117295","DOIUrl":"10.1016/j.microc.2026.117295","url":null,"abstract":"<div><div>Hyaluronidase (HAase), an enzyme responsible for hyaluronic acid (HA) degradation, compromises the structural integrity of HA by cleaving anti-inflammatory high-molecular-weight HA into pro-inflammatory lower molecular weight forms. Consequently, developing simple and efficient strategies for screening HAase inhibitors is of critical importance. Herein, a novel biosensing-based targeted affinity screening method was established for the identification of HAase inhibitors from complex herbal medicines. In the biosensing system, carbon dots served as fluorescent probes, while HA-functionalized gold nanoparticles acted as quenchers, and the addition of HAase induced a marked fluorescence recovery. Under optimized conditions, HAase exhibited a good linear response over the concentration range of 5.3 to 343.8 U mL<sup>−1</sup>. This method enables rapid and efficient detection of HAase inhibitory activity in complex herbal extracts. Subsequently, active components were selectively captured, separated, and identified using immobilized HAase affinity chromatography. Following validation with both negative and positive control drugs, the screening model successfully identified <em>Rhodiola rosea</em> L. among 43 herbal medicines as a potent HAase. Gallic acid (GA) was identified as the active constituent, exhibiting an IC<sub>50</sub> value of 111.7 μM. Results from molecular docking and molecular dynamics simulations demonstrated a strong binding affinity between GA and HAase. Additionally, cellular experiments confirmed the anti-inflammatory activity of GA, as evidenced by its significant inhibition of LPS-induced inflammatory markers IL-4, IL-5, IL-6, and TNF-α. Collectively, the integrated biosensing and affinity-based screening strategy established in this study provides a precise and efficient platform for the discovery of bioactive compounds from complex natural products.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117295"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study intends to accurately predict the absorption capacity of sulfur dioxide (SO₂) in deep eutectic solvents (DESs) by developing robust machine learning (ML) models. A comprehensive dataset comprising 1382 experimental and 924 calculated data points was constructed, covering 156 unique DESs derived from 22 hydrogen bond acceptors (HBAs) and 42 hydrogen bond donors (HBDs). Input variables included sigma profile descriptors (s1–s10), water content, pressure, and temperature. Fifteen ML algorithms were evaluated, including Support Vector Regression (SVR), Convolutional Neural Networks (CNN), Random Forest, XGBoost, and LightGBM. Dataset reliability was confirmed using a Monte Carlo outlier detection algorithm. Among all models, SVR and CNN attained the highest level of performance on the testing dataset, with R2 values of 0.9872 and 0.9904, mean squared errors (MSE) of 0.0015 and 0.0011, and Mean relative deviation (MRD) of 9.11% and 8.95%, respectively. The analysis identified pressure and structural descriptors (particularly s8) as key variables influencing SO₂ absorption. These results highlight the effectiveness of ML techniques in modeling gas absorption behavior in complex DES systems and support their application in the design of high-efficiency solvent systems.
{"title":"New insights into sulfur dioxide absorption in deep eutectic solvents","authors":"Chaofan Hu , Farag M.A. Altalbawy , Krunal Vaghela , V. Vivek , Sarbeswara Hota , Devendra Singh , Mahesh Manchanda , Prakhar Tomar , Raed Alfilh , Aseel Smerat , Mehrdad Mottaghi","doi":"10.1016/j.microc.2026.117312","DOIUrl":"10.1016/j.microc.2026.117312","url":null,"abstract":"<div><div>This study intends to accurately predict the absorption capacity of sulfur dioxide (SO₂) in deep eutectic solvents (DESs) by developing robust machine learning (ML) models. A comprehensive dataset comprising 1382 experimental and 924 calculated data points was constructed, covering 156 unique DESs derived from 22 hydrogen bond acceptors (HBAs) and 42 hydrogen bond donors (HBDs). Input variables included sigma profile descriptors (s1–s10), water content, pressure, and temperature. Fifteen ML algorithms were evaluated, including Support Vector Regression (SVR), Convolutional Neural Networks (CNN), Random Forest, XGBoost, and LightGBM. Dataset reliability was confirmed using a Monte Carlo outlier detection algorithm. Among all models, SVR and CNN attained the highest level of performance on the testing dataset, with R<sup>2</sup> values of 0.9872 and 0.9904, mean squared errors (MSE) of 0.0015 and 0.0011, and Mean relative deviation (MRD) of 9.11% and 8.95%, respectively. The analysis identified pressure and structural descriptors (particularly s8) as key variables influencing SO₂ absorption. These results highlight the effectiveness of ML techniques in modeling gas absorption behavior in complex DES systems and support their application in the design of high-efficiency solvent systems.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117312"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-05DOI: 10.1016/j.microc.2026.117272
Mariia V. Samodelova , Nikita R. Yarenkov , Igor K. Solontsov , Irina A. Lemesh , Olesya O. Kapitanova , Irina A. Veselova
Pyridine and its derivatives are highly toxic and strictly regulated class of compounds, making their sensitive monitoring essential for human health and environmental safety. Despite the high sensitivity of surface-enhanced Raman scattering (SERS) for detecting various analytes, analysing volatile compounds remains difficult due to the inability to retain the analyte on noble metal nanoparticle surfaces. Herein, we demonstrate for the first time that SERS enables the sensitive, rapid and simple detection of the toxicant pyridine in gaseous form. We introduce a novel strategy that provides SERS analysis of volatile analytes, thereby expanding the scope of molecules accessible to this method. Trapping pyridine in stable transition metal–pyridine complexes enables shifting the absorption maximum from ultraviolet to the visible spectral range (500–660 nm), which is closer in energy to the plasmon absorption band of the silver nanostructured substrate and the laser energy of the Raman spectrometer. Thus, we observed sensitivity down to 5.7 mg/m3 using benchtop Raman spectrometer with a 638 nm laser wavelength, achieving detection limits below the permissible exposure limit for gaseous pyridine, due to the additional resonant enhancement. The immobilisation of transition metal ions in a porous chitosan layer on the plasmonic surface efficiently captures and retains pyridine vapours near the surface. This approach demonstrates broad potential for the trace-level monitoring of a wide class of volatile analytes by SERS.
{"title":"Metal–complex assisted resonant SERS for trace-level detection of hazardous gaseous pyridine","authors":"Mariia V. Samodelova , Nikita R. Yarenkov , Igor K. Solontsov , Irina A. Lemesh , Olesya O. Kapitanova , Irina A. Veselova","doi":"10.1016/j.microc.2026.117272","DOIUrl":"10.1016/j.microc.2026.117272","url":null,"abstract":"<div><div>Pyridine and its derivatives are highly toxic and strictly regulated class of compounds, making their sensitive monitoring essential for human health and environmental safety. Despite the high sensitivity of surface-enhanced Raman scattering (SERS) for detecting various analytes, analysing volatile compounds remains difficult due to the inability to retain the analyte on noble metal nanoparticle surfaces. Herein, we demonstrate for the first time that SERS enables the sensitive, rapid and simple detection of the toxicant pyridine in gaseous form. We introduce a novel strategy that provides SERS analysis of volatile analytes, thereby expanding the scope of molecules accessible to this method. Trapping pyridine in stable transition metal–pyridine complexes enables shifting the absorption maximum from ultraviolet to the visible spectral range (500–660 nm), which is closer in energy to the plasmon absorption band of the silver nanostructured substrate and the laser energy of the Raman spectrometer. Thus, we observed sensitivity down to 5.7 mg/m<sup>3</sup> using benchtop Raman spectrometer with a 638 nm laser wavelength, achieving detection limits below the permissible exposure limit for gaseous pyridine, due to the additional resonant enhancement. The immobilisation of transition metal ions in a porous chitosan layer on the plasmonic surface efficiently captures and retains pyridine vapours near the surface. This approach demonstrates broad potential for the trace-level monitoring of a wide class of volatile analytes by SERS.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117272"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heavy metals (HMs) are known as toxic and non-biodegradable pollutants. It is essential to develop a quick, and cost-effective sensing platform for the detection of HMs. Electrochemical sensor based on screen-printed electrodes (SPEs) have obtained continuous consideration in recent years by offering sensitivity, selectivity, disposability, cost-effectiveness, portability, simplicity in pretreatment steps, eco-friendly methods, and improving signal-to-noise ratio due to using a small sample volume. Various researches are being conducted to develop green sensing platforms to minimize toxic effects of reagents, materials, and solvents utilized in the structure of electroanalytical sensors to monitor HMs. Therefore, this review represents the efforts on the scope of detection of HMs based on green SPEs as eco-friendly environmental sensing systems. Moreover, it examines green electrochemical sensor design, and sensor performance in important features to present insights about practical challenges and successful approaches regarding to determination of HMs in the real samples of food and beverages. Lastly, future trends focusing on green portable electrochemical sensor development combined with artificial intelligence (AI) are highlighted. It was found that applying green modifiers for preparation of sensors based- SPEs such as non-hazardous materials, reagents, substrates as well as green synthesis methodologies can decrease or prevent environmental impact for detection of HMs. Additionally, an efficient pretreatment process can improve sensitivity and selectivity of assessment of HMs through eliminating interfering compounds. Notably, the combination of portable devices, AI and deep learning algorithms can enable to produce devices with capability of multi-analytes detection and delivering accurate and reliable results toward safety assurance and commercialization as future developments.
{"title":"Recent advances in design, and applications of electrochemical sensors focused on green screen-printed electrodes to monitor heavy metals in food and beverage","authors":"Zohre Eskandari Alughare , Alireza Sanati , Zahra Esfandiari , Parham Joolaei Ahranjani","doi":"10.1016/j.microc.2026.117192","DOIUrl":"10.1016/j.microc.2026.117192","url":null,"abstract":"<div><div>Heavy metals (HMs) are known as toxic and non-biodegradable pollutants. It is essential to develop a quick, and cost-effective sensing platform for the detection of HMs. Electrochemical sensor based on screen-printed electrodes (SPEs) have obtained continuous consideration in recent years by offering sensitivity, selectivity, disposability, cost-effectiveness, portability, simplicity in pretreatment steps, eco-friendly methods, and improving signal-to-noise ratio due to using a small sample volume. Various researches are being conducted to develop green sensing platforms to minimize toxic effects of reagents, materials, and solvents utilized in the structure of electroanalytical sensors to monitor HMs. Therefore, this review represents the efforts on the scope of detection of HMs based on green SPEs as eco-friendly environmental sensing systems. Moreover, it examines green electrochemical sensor design, and sensor performance in important features to present insights about practical challenges and successful approaches regarding to determination of HMs in the real samples of food and beverages. Lastly, future trends focusing on green portable electrochemical sensor development combined with artificial intelligence (AI) are highlighted. It was found that applying green modifiers for preparation of sensors based- SPEs such as non-hazardous materials, reagents, substrates as well as green synthesis methodologies can decrease or prevent environmental impact for detection of HMs. Additionally, an efficient pretreatment process can improve sensitivity and selectivity of assessment of HMs through eliminating interfering compounds. Notably, the combination of portable devices, AI and deep learning algorithms can enable to produce devices with capability of multi-analytes detection and delivering accurate and reliable results toward safety assurance and commercialization as future developments.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117192"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-11DOI: 10.1016/j.microc.2026.117349
Eugenia Geddes da Filicaia , Richard P. Evershed , Ian D. Bull , David A. Peggie
Differentiation between the main types of cochineal dyestuffs, used as a principal red colourant throughout history, remains challenging. Nonetheless, accurate cochineal source identification would provide corroborating evidence to historical accounts, unveiling ancient practices and historical trade routes. Herein, stable carbon isotope analysis is proposed, for the first time, as a tool for source investigation of colourants on cultural heritage objects. Through bulk analysis, the isotopic signatures of Mexican, Armenian, and Polish cochineal insects were confirmed to reflect those of their host plants (CAM, C4, and C3, respectively). Compound-specific isotope analysis (CSIA), widely used for provenancing archaeological lipids, was then utilised to investigate the insect lipid extracts, revealing that Mexican, Armenian, and Polish cochineals have distinct fatty acid profiles and isotopic signatures. A new protocol, based on direct inlet pyrolysis-gas chromatography-combustion-isotope ratio mass spectrometry (DIP-GC-C-IRMS), was developed to target a known biomarker of the main colourant carminic acid (CA), thereby obtaining the isotopic signature of the colourant itself. Although Armenian cochineal harvested from C3 plants has not yet been investigated, results so far reveal distinct CA δ13C values (δ13CCA) for Armenian and Mexican cochineal lakes and paint replicas, suggesting that this method may be able to differentiate between the three main cochineal sources. The presence of the known marker in similar red insect-derived lakes (kermes and lac) was also investigated. In addition to providing the first example of CSIA of lake pigments, this is the first study that utilises DIP-GC-QTOF-MS/MS as a complementary technique. By offering additional structural information for CA derivatives, a structure and mechanism of formation for a newly identified biomarker is proposed.
{"title":"Stable carbon isotope and accurate mass studies for source determination and identification of cochineal insects and lake pigments","authors":"Eugenia Geddes da Filicaia , Richard P. Evershed , Ian D. Bull , David A. Peggie","doi":"10.1016/j.microc.2026.117349","DOIUrl":"10.1016/j.microc.2026.117349","url":null,"abstract":"<div><div>Differentiation between the main types of cochineal dyestuffs, used as a principal red colourant throughout history, remains challenging. Nonetheless, accurate cochineal source identification would provide corroborating evidence to historical accounts, unveiling ancient practices and historical trade routes. Herein, stable carbon isotope analysis is proposed, for the first time, as a tool for source investigation of colourants on cultural heritage objects. Through bulk analysis, the isotopic signatures of Mexican, Armenian, and Polish cochineal insects were confirmed to reflect those of their host plants (CAM, C<sub>4</sub>, and C<sub>3</sub>, respectively). Compound-specific isotope analysis (CSIA), widely used for provenancing archaeological lipids, was then utilised to investigate the insect lipid extracts, revealing that Mexican, Armenian, and Polish cochineals have distinct fatty acid profiles and isotopic signatures. A new protocol, based on direct inlet pyrolysis-gas chromatography-combustion-isotope ratio mass spectrometry (DIP-GC-C-IRMS), was developed to target a known biomarker of the main colourant carminic acid (CA), thereby obtaining the isotopic signature of the colourant itself. Although Armenian cochineal harvested from C<sub>3</sub> plants has not yet been investigated, results so far reveal distinct CA δ<sup>13</sup>C values (δ<sup>13</sup>C<sub>CA</sub>) for Armenian and Mexican cochineal lakes and paint replicas, suggesting that this method may be able to differentiate between the three main cochineal sources. The presence of the known marker in similar red insect-derived lakes (kermes and lac) was also investigated. In addition to providing the first example of CSIA of lake pigments, this is the first study that utilises DIP-GC-QTOF-MS/MS as a complementary technique. By offering additional structural information for CA derivatives, a structure and mechanism of formation for a newly identified biomarker is proposed.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117349"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-14DOI: 10.1016/j.microc.2026.117381
Yanfen Wang , Xupeng Gao , Zhenkang Li , Hongwei Zhang , Liuhao Wang , Qiang Zhang , Feifei Sun , Feng Zhou , Hao Yu
Fusarium graminearum is a destructive fungal pathogen that causes major diseases in cereal crops like maize, wheat, and barley. In maize, it is a primary causal agent of stalk rot, leading to significant yield losses and contaminated grain with mycotoxin that threaten human and animal health. This study presents and evaluates a novel nucleic acid detection platform that combines recombinase polymerase amplification (RPA) with a CRISPR/Cas12a system for the rapid identification of F. graminearum in maize. By targeting the translation elongation factor 1α (EF-1α) gene, the assay discriminates F. graminearum from related species with high conservation. Following systematic optimization, the proposed method exhibited high sensitivity and specificity for the detection of F. graminearum using both lateral flow strips (LFS) and green fluorescence visualization. The method enabled the detection of F. graminearum DNA at concentrations as low as 0.63 pg (13 copies) within 20 min, while it reliably identified infections in maize coleoptiles and field samples as early as 4 days post-inoculation. Notably, this approach provides a novel alternative for the rapid, sensitive, and specific visualization, detection, and identification of F. graminearum without requiring specialized technical expertise or costly instrumentation. By integrating CRISPR/Cas12a specificity with the rapid amplification capability of RPA, this assay represents a powerful tool for early and accurate pathogen detection in maize production systems.
{"title":"Rapid and sensitive detection of Fusarium graminearum in maize using an RPA–CRISPR/Cas12a system","authors":"Yanfen Wang , Xupeng Gao , Zhenkang Li , Hongwei Zhang , Liuhao Wang , Qiang Zhang , Feifei Sun , Feng Zhou , Hao Yu","doi":"10.1016/j.microc.2026.117381","DOIUrl":"10.1016/j.microc.2026.117381","url":null,"abstract":"<div><div><em>Fusarium graminearum</em> is a destructive fungal pathogen that causes major diseases in cereal crops like maize, wheat, and barley. In maize, it is a primary causal agent of stalk rot, leading to significant yield losses and contaminated grain with mycotoxin that threaten human and animal health. This study presents and evaluates a novel nucleic acid detection platform that combines recombinase polymerase amplification (RPA) with a CRISPR/Cas12a system for the rapid identification of <em>F. graminearum</em> in maize. By targeting the translation elongation factor 1α (<em>EF-1α</em>) gene, the assay discriminates <em>F. graminearum</em> from related species with high conservation. Following systematic optimization, the proposed method exhibited high sensitivity and specificity for the detection of <em>F. graminearum</em> using both lateral flow strips (LFS) and green fluorescence visualization. The method enabled the detection of <em>F. gra</em>min<em>earum</em> DNA at concentrations as low as 0.63 pg (13 copies) within 20 min, while it reliably identified infections in maize coleoptiles and field samples as early as 4 days post-inoculation. Notably, this approach provides a novel alternative for the rapid, sensitive, and specific visualization, detection, and identification of <em>F. graminearum</em> without requiring specialized technical expertise or costly instrumentation. By integrating CRISPR/Cas12a specificity with the rapid amplification capability of RPA, this assay represents a powerful tool for early and accurate pathogen detection in maize production systems.</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117381"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-15DOI: 10.1016/j.microc.2026.117419
Jiaxin Xu , Yeqiu Li , Qin Dai , Rina Wu , Qian Li , Yinguo Xie , Yizhe Zhang , Zhiwei Men
To improve the detection performance of laser-induced breakdown spectroscopy (LIBS) for organic elements, this study investigates the enhancement effect of cu, Zn, Al metal substrates combined with double-pulse LIBS (DP-LIBS) on organic element spectral lines, using azithromycin as the target analyte. The results show that different metal substrates have selectivity for element spectral line enhancement. The cu substrate significantly enhanced the C I 247.8 nm and CN 388.3 nm (, 0–0) signals due to its high thermal conductivity. The spectral line intensities of C, H, N, O, CN, C2, were enhanced up to 36 times, and the coefficient of quartile deviation (CQD) was reduced under the synergistic effect of the metal substrate and double pulses compared with a single pulse. The intensity ratio of CN/C and CN/C2 confirmed that CN was mainly derived from the reaction of C with background nitrogen. This study reveals the synergistic enhancement mechanism of metal substrate and DP-LIBS, and provides a theoretical basis for high sensitivity analysis of organic matter
{"title":"Metal substrate and DP-LIBS enhanced the spectral signal of organic elements and analyzed the evolution of CN molecules","authors":"Jiaxin Xu , Yeqiu Li , Qin Dai , Rina Wu , Qian Li , Yinguo Xie , Yizhe Zhang , Zhiwei Men","doi":"10.1016/j.microc.2026.117419","DOIUrl":"10.1016/j.microc.2026.117419","url":null,"abstract":"<div><div>To improve the detection performance of laser-induced breakdown spectroscopy (LIBS) for organic elements, this study investigates the enhancement effect of cu, Zn, Al metal substrates combined with double-pulse LIBS (DP-LIBS) on organic element spectral lines, using azithromycin as the target analyte. The results show that different metal substrates have selectivity for element spectral line enhancement. The cu substrate significantly enhanced the C I 247.8 nm and CN 388.3 nm (<span><math><mrow><msup><mi>B</mi><mn>2</mn></msup><msup><mi>Σ</mi><mo>+</mo></msup><mo>→</mo><msup><mi>X</mi><mn>2</mn></msup><msup><mi>Σ</mi><mo>+</mo></msup></mrow></math></span>, 0–0) signals due to its high thermal conductivity. The spectral line intensities of C, H, N, O, CN, C<sub>2</sub>, were enhanced up to 36 times, and the coefficient of quartile deviation (CQD) was reduced under the synergistic effect of the metal substrate and double pulses compared with a single pulse. The intensity ratio of CN/C and CN/C<sub>2</sub> confirmed that CN was mainly derived from the reaction of C with background nitrogen. This study reveals the synergistic enhancement mechanism of metal substrate and DP-LIBS, and provides a theoretical basis for high sensitivity analysis of organic matter</div></div>","PeriodicalId":391,"journal":{"name":"Microchemical Journal","volume":"223 ","pages":"Article 117419"},"PeriodicalIF":4.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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