Bryan E. Alvarez-Serna;Daniel A. Arcos-Santiago;Jorge A. Uc-Martín;Roberto G. Ramírez-Chavarría
{"title":"Electrochemical Sensor With Dynamic Self-Calibration for Acetaminophen Detection in Water","authors":"Bryan E. Alvarez-Serna;Daniel A. Arcos-Santiago;Jorge A. Uc-Martín;Roberto G. Ramírez-Chavarría","doi":"10.1109/LSENS.2025.3528342","DOIUrl":null,"url":null,"abstract":"In this letter, we introduce a self-calibrating electrochemical sensor for water acetaminophen (ACT) detection. The sensor is built upon a graphite pencil lead (GPL) electrode modified with a molecularly imprinted polymer (MIP) to ensure selectivity. Moreover, using a sparse identification scheme, the sensor is equipped with a dynamic calibration algorithm to increase the sensor accuracy in time-dependent measurements. The sensor performance was evaluated under static and dynamic conditions using ACT solutions prepared in tap water as the matrix. As a result, the sensor achieved a detection limit of 9.3 mg/L, proving to be a viable alternative for quantifying emerging concerns in water. Finally, we show how simple but robust sensor models could enhance the performance of online measurements.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 2","pages":"1-4"},"PeriodicalIF":2.2000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10836916/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this letter, we introduce a self-calibrating electrochemical sensor for water acetaminophen (ACT) detection. The sensor is built upon a graphite pencil lead (GPL) electrode modified with a molecularly imprinted polymer (MIP) to ensure selectivity. Moreover, using a sparse identification scheme, the sensor is equipped with a dynamic calibration algorithm to increase the sensor accuracy in time-dependent measurements. The sensor performance was evaluated under static and dynamic conditions using ACT solutions prepared in tap water as the matrix. As a result, the sensor achieved a detection limit of 9.3 mg/L, proving to be a viable alternative for quantifying emerging concerns in water. Finally, we show how simple but robust sensor models could enhance the performance of online measurements.