Trends in Analytical Chemistry最新文献_第3页

Integration of covalent organic framework-based materials and sorptive extraction techniques for quantitation of pharmaceuticals, hormones, and personal care products 基于共价有机框架的材料和吸附萃取技术的整合，用于药物，激素和个人护理产品的定量

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Trends in Analytical Chemistry Pub Date : 2025-09-17 DOI: 10.1016/j.trac.2025.118468

Abdelmonaim Azzouz , Lamia Hejji , Abdeslam Assafi , Ki-Hyun Kim

{"title":"Integration of covalent organic framework-based materials and sorptive extraction techniques for quantitation of pharmaceuticals, hormones, and personal care products","authors":"Abdelmonaim Azzouz , Lamia Hejji , Abdeslam Assafi , Ki-Hyun Kim","doi":"10.1016/j.trac.2025.118468","DOIUrl":"10.1016/j.trac.2025.118468","url":null,"abstract":"<div><div>Persistent organic pollutants (POPs) are hazardous, long-lasting chemicals that bioaccumulate and persist in the environment. Because of their serious health risks (e.g., endocrine disruption, cancer, and reproductive disorders), there has been the growing demand for accurate detection tools to quantify dioxins, polychlorinated biphenyls, pesticides, pharmaceuticals, bisphenol A, and hormones in food, environmental, and biological matrices. This review explores the practical application of covalent organic frameworks (COFs) in solid-phase extraction as a pre-concentration tool for POPs, mainly in terms of analytical sensitivity and procedural efficiency. COFs are demonstrated as promising sorption media with an enhanced sorption ability toward POPs aided by their unique structural and surface properties. This review delves into the synthesis, characterization, and applications of COFs in association with their performance and the distinctive features. It also discusses their potential applications and significant hurdles.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118468"},"PeriodicalIF":12.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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3D and 4D printed polymers for solid phase extraction and pollutant removal applications 3D和4D打印聚合物固相萃取和污染物去除应用

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Trends in Analytical Chemistry Pub Date : 2025-09-16 DOI: 10.1016/j.trac.2025.118466

Hassan Elzain Hassan Ahmed , Mustafa Soylak

{"title":"3D and 4D printed polymers for solid phase extraction and pollutant removal applications","authors":"Hassan Elzain Hassan Ahmed , Mustafa Soylak","doi":"10.1016/j.trac.2025.118466","DOIUrl":"10.1016/j.trac.2025.118466","url":null,"abstract":"<div><div>Addressing global environmental challenges calls for advanced materials capable of efficient pollutant removal and solid-phase extraction. This review examines recent developments in polymers fabricated by three-dimensional (3D) and four-dimensional (4D, stimuli-responsive) printing methods. It highlights how 3D-printed structures offer customizable geometries, increased porosity, and enhanced surface area to improve adsorption performance. In addition, using smart, 4D-responsive polymers allows for changes in shape or controlled release when triggered by outside factors (like temperature or pH), which adds a new level of functional control. We assess printing technologies, like fused deposition modelling, stereolithography, digital light processing, and direct ink writing, and compare strategies for polymer functionalization, composite formulation, and performance in removing heavy metals, organic pollutants, and pharmaceutical residues from complex samples. This comprehensive overview emphasizes both the scientific advances and remaining challenges, guiding future innovation in sustainable, high-performance 3D/4D printed polymer materials.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118466"},"PeriodicalIF":12.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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Application of machine learning to improve the accuracy of electrochemical sensors: A review 机器学习在提高电化学传感器精度中的应用综述

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Trends in Analytical Chemistry Pub Date : 2025-09-16 DOI: 10.1016/j.trac.2025.118469

Aixue Li , Haoyu Yang , Wenxin Yu , Tianyang Liu , Bin Luo , Chunjiang Zhao

{"title":"Application of machine learning to improve the accuracy of electrochemical sensors: A review","authors":"Aixue Li , Haoyu Yang , Wenxin Yu , Tianyang Liu , Bin Luo , Chunjiang Zhao","doi":"10.1016/j.trac.2025.118469","DOIUrl":"10.1016/j.trac.2025.118469","url":null,"abstract":"<div><div>Electrochemical sensors are devices that convert chemical signals into electrical signals, having been widely applied in various fields. However, traditional electrochemical sensors are prone to interference from various issues, which leads to inaccurate measurement results. The development of artificial intelligence (AI) technologies offers new approaches to address these issues. Among these, machine learning (ML) techniques can analyze large volumes of sensor data, identify complex patterns and relationships, and thereby enhance the accuracy and stability of sensors. This paper provides a review of the latest research over the past five years on ML applications addressing challenges such as nonlinear sensor signal relationships, low-concentration accuracy, signal drift, and interference resistance. It also summarizes the performance of various algorithms in different application scenarios. Finally, the paper discusses the challenges faced by ML technologies in improving the accuracy of electrochemical sensors and outlines future development directions.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118469"},"PeriodicalIF":12.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145216826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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Applicability of X-ray fluorescence spectrometry for the characterization of nuclear materials: Front to back end of nuclear fuel cycle x射线荧光光谱法在核材料表征中的适用性：核燃料循环的前后端

IF 12 1区化学

Trends in Analytical Chemistry Pub Date : 2025-09-15 DOI: 10.1016/j.trac.2025.118464

Kaushik Sanyal , Sangita Dhara

{"title":"Applicability of X-ray fluorescence spectrometry for the characterization of nuclear materials: Front to back end of nuclear fuel cycle","authors":"Kaushik Sanyal , Sangita Dhara","doi":"10.1016/j.trac.2025.118464","DOIUrl":"10.1016/j.trac.2025.118464","url":null,"abstract":"<div><div>The applications of X-Ray Fluorescence (XRF) spectrometry in nuclear industry span from the front-end of the nuclear fuel cycle, where the raw materials are mined and processed, to the various stages of fuel fabrication, till its back-end, which includes the waste management as well as decommissioning of a nuclear reactor. XRF is a very powerful spectroscopic technique having the capability of non-destructive, rapid, multi-elemental analysis and used for reliable qualitative as well as quantitative elemental analysis of materials. This manuscript discusses the role of Energy Dispersive XRF (EDXRF) in each stage of the nuclear fuel cycle, starting from its applications in U/Th exploration, their processing and extraction, followed by its role in the various stages of the fuel fabrication as well as the quality assurance and control of nuclear fuels. Further, in the back end of nuclear fuel cycle the utility of EDXRF for the post irradiated examination of the nuclear fuel as well as in applications for spent fuel characterization, and facility decommissioning are discussed in detail.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118464"},"PeriodicalIF":12.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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Onsite environmental analysis by portable mass spectrometry 便携式质谱法现场环境分析

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Trends in Analytical Chemistry Pub Date : 2025-09-15 DOI: 10.1016/j.trac.2025.118461

Jianfeng Zhang , Zhen Zhou , Bin Hu

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Advances in lateral flow assays for sensitive detection of antibiotic residues: An intelligent tool for food safety monitoring 横向流动法敏感检测抗生素残留的研究进展：食品安全监测的智能工具

IF 12 1区化学

Trends in Analytical Chemistry Pub Date : 2025-09-15 DOI: 10.1016/j.trac.2025.118462

Peifang Chen , Imran Mahmood Khan , Mingwei Qin , Yu Li , Ali Raza , Shuo Qi , Zhouping Wang

{"title":"Advances in lateral flow assays for sensitive detection of antibiotic residues: An intelligent tool for food safety monitoring","authors":"Peifang Chen , Imran Mahmood Khan , Mingwei Qin , Yu Li , Ali Raza , Shuo Qi , Zhouping Wang","doi":"10.1016/j.trac.2025.118462","DOIUrl":"10.1016/j.trac.2025.118462","url":null,"abstract":"<div><div>Antibiotics are widely used in livestock and aquaculture to promote growth and prevent disease. However, antibiotic residues in animal-derived food pose serious risks to human health and the global antibiotic resistance. Thus, there is an urgent need for point-of-care testing strategies to ensure food safety. This review provides a comprehensive overview of recent advancements in lateral flow assay (LFA) platforms for sensitive antibiotic detection. We critically assess the integration of biorecognition elements, advanced nanomaterials, innovative optical systems, and intelligent devices including smartphone-assisted platforms and artificial intelligence (AI)-enhanced analysis. Special focus is given to novel signal amplification strategies and multiplexing techniques that enable simultaneous detection of multiple antibiotics. Furthermore, we discuss current limitations and outline future directions. This review highlights the transformative potential of intelligent LFA tools in establishing digitalized, automated, and scalable solutions for end-to-end monitoring of antibiotics throughout the food supply chain.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118462"},"PeriodicalIF":12.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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Microfluidic surface-enhanced Raman spectroscopy aided by artificial intelligence for biosensing 人工智能辅助微流控表面增强拉曼光谱用于生物传感

IF 12 1区化学

Trends in Analytical Chemistry Pub Date : 2025-09-13 DOI: 10.1016/j.trac.2025.118426

Muhammad Sohail Ibrahim , Myeong-Seok Lee , Sejin Park , Abdul Naman , Dongho Lee , Yunsang Kwak , Minseok Kim

{"title":"Microfluidic surface-enhanced Raman spectroscopy aided by artificial intelligence for biosensing","authors":"Muhammad Sohail Ibrahim , Myeong-Seok Lee , Sejin Park , Abdul Naman , Dongho Lee , Yunsang Kwak , Minseok Kim","doi":"10.1016/j.trac.2025.118426","DOIUrl":"10.1016/j.trac.2025.118426","url":null,"abstract":"<div><div>The integration of microfluidics, surface-enhanced Raman spectroscopy (SERS), and artificial intelligence (AI) is revolutionizing chemical and biomedical sensing. Microfluidic systems enable precise fluid control at the microscale, while SERS offers ultrasensitive, label-free molecular detection. Combining AI with microfluidic SERS enhances data processing, feature extraction, and automated decision-making, enabling efficient and intelligent diagnostics and analysis. This review highlights recent advances in AI-driven microfluidic SERS for biomedical detection and analysis, environmental monitoring, and chemical analysis. Key developments include improved detection accuracy, real-time classification, and high-throughput analysis. However, challenges such as data interpretability, computational complexity, and seamless integration must be addressed. Future research directions call for explainable AI, lightweight machine learning models, and privacy-preserving techniques to support broader adoption and safeguard sensitive information. By leveraging these technologies, researchers can develop innovative platforms for real-time sensing and analysis, ultimately advancing applications across healthcare, environmental science, and other interdisciplinary domains.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118426"},"PeriodicalIF":12.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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Methods for pre-concentration and clean-up of glyphosate and aminomethylphosphonic acid from water samples: A review 水样中草甘膦和氨基甲基膦酸的预富集和净化方法综述

IF 12 1区化学

Trends in Analytical Chemistry Pub Date : 2025-09-12 DOI: 10.1016/j.trac.2025.118459

Zahra Shamsi, Daniel Buchner, Stefan B. Haderlein

{"title":"Methods for pre-concentration and clean-up of glyphosate and aminomethylphosphonic acid from water samples: A review","authors":"Zahra Shamsi, Daniel Buchner, Stefan B. Haderlein","doi":"10.1016/j.trac.2025.118459","DOIUrl":"10.1016/j.trac.2025.118459","url":null,"abstract":"<div><div>Glyphosate, the world's most widely applied herbicide, is frequently detected in both surface and groundwater systems along with its metabolite aminomethylphosphonic acid (AMPA). Due to their high polarity, ionic character, and water solubility, detection of trace levels in environmental samples requires effective enrichment prior to instrumental analysis. This review critically examines the current landscape of pre-concentration and clean-up methods developed for glyphosate and AMPA quantification, including solid-phase extraction (SPE), liquid-liquid extraction (LLE), microextraction, and advanced techniques. The effects of sample matrix, pH, and ionic composition on recovery rates and reproducibility are critically discussed, along with method performance metrics such as detection limits and matrix suppression effects. While derivatization remains essential for enhancing determination via GC- or LC-based techniques, it is treated here as a secondary complement to enrichment steps. Advances such as magnetic sorbents, online SPE systems, and environmentally friendly formats are highlighted for their potential to simplify workflows and enhance throughput. Drawing from peer-reviewed studies, this review presents comparative insights into performance metrics such as recovery, reproducibility, and matrix effects across different methods. Rather than prescribing universal best practices, the synthesis allows researchers to make informed choices based on their specific analytical context, instrumentation, and regulatory requirements. Ultimately, the work provides a foundation for selecting appropriate enrichment strategies that align with diverse environmental monitoring objectives and laboratory capabilities.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118459"},"PeriodicalIF":12.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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The high-throughput and high-sensitivity strategies of multiplex lateral flow immunoassays for agricultural product contaminants 农产品污染物多重侧流免疫分析的高通量和高灵敏度策略

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Trends in Analytical Chemistry Pub Date : 2025-09-12 DOI: 10.1016/j.trac.2025.118455

Qinghuan Wu , Beibei Liu , A.M. Abd El-Aty , Xing Zhang , Linglong Chen , Guangyang Liu , Xiaomin Xu , Jing Wang , Maojun Jin , Qijun Wang , Xiaodong Huang , Ge Chen , Donghui Xu

{"title":"The high-throughput and high-sensitivity strategies of multiplex lateral flow immunoassays for agricultural product contaminants","authors":"Qinghuan Wu , Beibei Liu , A.M. Abd El-Aty , Xing Zhang , Linglong Chen , Guangyang Liu , Xiaomin Xu , Jing Wang , Maojun Jin , Qijun Wang , Xiaodong Huang , Ge Chen , Donghui Xu","doi":"10.1016/j.trac.2025.118455","DOIUrl":"10.1016/j.trac.2025.118455","url":null,"abstract":"<div><div>Effective detection of agricultural pollutants in complex matrices is crucial for food safety. Lateral flow immunoassay (LFIA) is a vital onsite tool due to its simplicity, portability, low cost, and rapid detection. However, the efficiency and sensitivity of the conventional single-target detection mode limits its utility for detecting multiple contaminants in complex agricultural matrices. High-throughput and sensitive multiplex lateral flow immunoassay (MLFIA) technologies have become mainstream in agricultural product safety inspection. This review highlights four high-throughput formats of MLFIA—single-line multicolor, multiline, multiplex, and microarray methods—and their suitability for multianalyte detection. Furthermore, a range of high-sensitivity signal transduction methods, colorimetric, fluorescent, surface-enhanced Raman spectroscopy (SERS), and magnetic nanoparticle-based systems, have been evaluated for adaptability to complex scenarios. The limitations, challenges and future development trends of MLFIA are proposed, with the goal of providing useful references for researchers to promote the rapid development and practical application of MLFIA in multiple agricultural fields.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118455"},"PeriodicalIF":12.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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Biosensors for transaminase detection: Their diversity and trends – A comprehensive review 用于转氨酶检测的生物传感器：多样性和发展趋势综述

IF 12 1区化学

Trends in Analytical Chemistry Pub Date : 2025-09-12 DOI: 10.1016/j.trac.2025.118460

Daryna Mruga , Kseniia Berketa , Sergei Dzyadevych , Oleksandr Soldatkin

{"title":"Biosensors for transaminase detection: Their diversity and trends – A comprehensive review","authors":"Daryna Mruga , Kseniia Berketa , Sergei Dzyadevych , Oleksandr Soldatkin","doi":"10.1016/j.trac.2025.118460","DOIUrl":"10.1016/j.trac.2025.118460","url":null,"abstract":"<div><div>Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are organ-specific biomarkers for liver (cirrhosis, hepatitis) and heart (failure, infarction) diseases, as well as other conditions (preeclampsia, myopathy). Numerous biosensors have been developed to detect their activity or concentration, using various principles and materials. This review systematizes existing approaches to identify the most effective ones. Most single-analyte biosensors are electrochemical (mainly chronoamperometric), while dual-analyte systems are typically optical. Some use less common techniques like quartz crystal microbalance, photoreflectometry, or photocurrent spectroscopy. Bioselective materials are usually oxidase or dehydrogenase enzymes; rare examples involve autocatalysis or non-enzymatic elements. Immobilization is typically via covalent linking or adsorption. Enhancements include membranes, mediators, and nanomaterials. Despite active development, no AST or ALT biosensor has reached commercialization, likely due to technical and economic barriers such as limited analytical performance, insufficient selectivity in real samples, lack of portability, challenges with multi-analyte detection, non-invasive sampling, and absence of AI integration.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118460"},"PeriodicalIF":12.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0