文献类型：期刊文献

中文题名：多维特征强化的芳香族多氟羧酸异构体单分子分辨

英文题名：Multidimensional feature reinforced single-molecule identification of aromatic polyfluorinated carboxylic acid isomers

作者：李洪双[1];蒋佳乐[1];李浦风[1];谢雪迎[1];丁冉[1];刘科贤[1];周睿祺[1];左嘉琦[2];邱恺培[1,3,4,5]

机构：[1]生态环境部化工过程环境风险评价与控制重点实验室,华东理工大学资源与环境工程学院,上海200237;[2]河口海岸学全国重点实验室,华东师范大学,上海200241;[3]煤液化气化及高效低碳利用全国重点实验室,上海200237;[4]上海市环境保护化学污染物环境标准与风险管理重点实验室,上海200237;[5]上海污染控制与生态安全研究院,上海200092

年份：2026

卷号：45

期号：3

起止页码：925

中文期刊名：环境化学

外文期刊名：Environmental Chemistry

收录：;北大核心:【北大核心2023】；

基金：“十四五”国家重点研发计划课题(2023YFC3008803);国家自然科学基金(22006037,21972041);上海市自然科学基金(23ZR1416300)资助。

语种：中文

中文关键词：单体环境分析化学;全氟化合物;位置异构体;特征工程;芳香族多氟羧酸

外文关键词：single-entity environmental analytical chemistry;perfluoroalkyl and polyfluoroalkylsubstances;positional isomers;feature engineering;aromatic polyfluorinated carboxylic acids.

摘要：全氟和多氟羧酸(Per-and Polyfluorinated Carboxylic Acids,PFCAs)种类繁多,具备环境持久性与生物累积性,对生态系统和人体健康构成威胁已成为共识.目前,基于纳米孔道电化学的单体电化学传感方法通过构建全氟直链羧酸分子体积与阻塞电流强度的构效关系,向PFCAs的无标准品检测迈出了重要的一步.然而,该方法在区分结构相似、体积差异微小的PFCAs同分异构体方面仍面临空间分辨率不足导致分辨性能欠佳的问题.本研究采用单分子电化学传感结合机器学习的策略,对三类、17种芳香族氢取代多氟羧酸异构体的识别进行了研究.通过从原始电信号中提取多维特征并结合低通滤波降噪处理,充分挖掘了信号中存储的分子性质信息,最终实现了高达88.92%的综合分类准确率.此外,通过抗干扰检测验证该方法在实际环境样品中的应用潜力,为实现全覆盖的PFCAs分子无标准品检测奠定坚实基础.
Per-and polyfluorinated carboxylic acids(PFCAs)are a large class of persistent,bioaccumulative substances that pose well-established threats to ecosystems and human health.Recent advances in the single-molecule electrochemical sensing technology based on nanoporeelectrochemistry has made significant progress towards standard-free detection of PFCAs byestablishing a strict linear correlation between the current blockade of PFCAs and their volume.However,the spatial resolution of current approaches remains insufficient to identify PFCAs isomerswith subtle structural and volumetric differences.In this study,we introduced an integrated strategycombining single-molecule electrochemical sensing with machine learning to identify 17 aromaticPFCAs isomers across three categories.By extracting multidimensional features from raw single-molecule signals and applying low-pass filtering for noise reduction,we effectively captured intrinsicmolecular information embedded within the signals,achieving an overall classification accuracy of88.92%.Furthermore,anti-interference test confirmed the potential for practical application incomplex environmental samples.This work lays a solid foundation for achieving comprehensive,label-free detection of PFCAs.