Preparation of a Nonenzymatic Potentiometric Lactic Acid Biosensor Modified by g-C₃N₄/ZnS Composite Materials on a SnO₂-Coated Flexible Printed Circuit Board
{"title":"Preparation of a Nonenzymatic Potentiometric Lactic Acid Biosensor Modified by g-C₃N₄/ZnS Composite Materials on a SnO₂-Coated Flexible Printed Circuit Board","authors":"Yu-Hsun Nien;Xin-Han Chen;Jung-Chuan Chou;Chih-Hsien Lai;Po-Yu Kuo;Po-Hui Yang;Jyun-Ming Huang;Wei-Shun Chen;Yu-Wei Chen;Yi-Wen Huang","doi":"10.1109/JSEN.2024.3525078","DOIUrl":null,"url":null,"abstract":"In this study, to enhance sensor performance a nonenzymatic lactic acid (LA) sensor based on SnO2 was developed and modified via graphitic carbon nitride (g-C3N4)/ZnS composite material to enhance its sensing performance. The modified sensor generates a voltage through the reaction between LA and the material, and its performance is characterized using a voltage-time measurement system. Specific evaluation criteria include average sensitivity, linearity, repeatability, response time, selectivity, drift effect, and limit of detection (LOD). Experimental evidence shows that compared to the unmodified SnO2 variant, the g-C3N4/ZnS-modified sensor exhibits superior performance in LA concentrations ranging from 1 to 9 mM, with an average sensitivity of 8.02 ± 0.12 mV/mM and with a linearity of 0.999. This modification significantly enhances sensor performance.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6007-6016"},"PeriodicalIF":4.3000,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10836146/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, to enhance sensor performance a nonenzymatic lactic acid (LA) sensor based on SnO2 was developed and modified via graphitic carbon nitride (g-C3N4)/ZnS composite material to enhance its sensing performance. The modified sensor generates a voltage through the reaction between LA and the material, and its performance is characterized using a voltage-time measurement system. Specific evaluation criteria include average sensitivity, linearity, repeatability, response time, selectivity, drift effect, and limit of detection (LOD). Experimental evidence shows that compared to the unmodified SnO2 variant, the g-C3N4/ZnS-modified sensor exhibits superior performance in LA concentrations ranging from 1 to 9 mM, with an average sensitivity of 8.02 ± 0.12 mV/mM and with a linearity of 0.999. This modification significantly enhances sensor performance.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice