A Machine Learning-Assisted Liquid Crystal Droplet Array Platform for the Sensitive and Selective Detection of Per- and Polyfluoroalkyl Substances (PFAS) in Water
Fengrui Wang, Shiyi Qin, Zhao Yang, Leena M. Edwards-Medina, Benjamin L. Chiu, Claribel Acevedo-Vélez, Christina K. Remucal, Reid C. Van Lehn, Victor M. Zavala, David M. Lynn
{"title":"A Machine Learning-Assisted Liquid Crystal Droplet Array Platform for the Sensitive and Selective Detection of Per- and Polyfluoroalkyl Substances (PFAS) in Water","authors":"Fengrui Wang, Shiyi Qin, Zhao Yang, Leena M. Edwards-Medina, Benjamin L. Chiu, Claribel Acevedo-Vélez, Christina K. Remucal, Reid C. Van Lehn, Victor M. Zavala, David M. Lynn","doi":"10.1021/acssensors.5c00907","DOIUrl":null,"url":null,"abstract":"We report a machine learning (ML)-assisted liquid crystal (LC) droplet array platform for the detection of per- and polyfluoroalkyl substances (PFAS) in water. Our approach uses an autoencoder network to process thousands of images obtained from arrays of microscale droplets of thermotropic LCs. The latent space obtained using the autoencoder contains significant information that enables sensitive and selective detection of two amphiphilic PFAS [perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS)] at concentrations as low as parts-per-trillion (ppt) in ultrapure water, municipal tap water, and simulated river water containing dissolved organic matter. Despite the absence of visually discernible changes in the optical outputs of LC arrays at low PFAS concentrations, this approach accurately predicts their presence, even in water containing interfering molecules. We also demonstrate the use of transfer learning to differentiate between PFOA, PFOS, and PFOA/PFOS mixtures, showcasing the potential for practical environmental monitoring. This platform permits identification of PFOA and PFOS below the maximum contaminant levels (4 ppt) established by the U.S. Environmental Protection Agency. Our approach is compatible with automated printing, treatment, and high-throughput optical and ML analysis and could provide a basis for the development of low-cost sensors to monitor PFAS and other amphiphilic contaminants in real-world water samples.","PeriodicalId":24,"journal":{"name":"ACS Sensors","volume":"22 1","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Sensors","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acssensors.5c00907","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
We report a machine learning (ML)-assisted liquid crystal (LC) droplet array platform for the detection of per- and polyfluoroalkyl substances (PFAS) in water. Our approach uses an autoencoder network to process thousands of images obtained from arrays of microscale droplets of thermotropic LCs. The latent space obtained using the autoencoder contains significant information that enables sensitive and selective detection of two amphiphilic PFAS [perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS)] at concentrations as low as parts-per-trillion (ppt) in ultrapure water, municipal tap water, and simulated river water containing dissolved organic matter. Despite the absence of visually discernible changes in the optical outputs of LC arrays at low PFAS concentrations, this approach accurately predicts their presence, even in water containing interfering molecules. We also demonstrate the use of transfer learning to differentiate between PFOA, PFOS, and PFOA/PFOS mixtures, showcasing the potential for practical environmental monitoring. This platform permits identification of PFOA and PFOS below the maximum contaminant levels (4 ppt) established by the U.S. Environmental Protection Agency. Our approach is compatible with automated printing, treatment, and high-throughput optical and ML analysis and could provide a basis for the development of low-cost sensors to monitor PFAS and other amphiphilic contaminants in real-world water samples.
期刊介绍:
ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.