用于水铅检测的铋功能化喷墨印刷电化学传感器

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Annatoma Arif;Alexis J. Acevedo-González;Carlos R. Cabrera;Robert C. Roberts
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引用次数: 0

摘要

本文介绍了一种灵敏、选择性和可重复使用的3d铋功能化喷墨打印形状记忆聚合物(SMP),该聚合物结合了电化学传感器(ECS),用于检测水溶液中的铅(II),即去离子水、自来水和河水。所报道的ECS包括一个铋沉积工作电极(WE),一个金(Au)对电极和一个氯化银(AgCl)参比电极。在喷墨印刷的镀金(厚度- $8.24~\mu $ m±1.42)电极上通过两种方法进行铋的功能化:非原位,涉及循环伏安法(CV)的整个周期沉积铋- $400~\mu $ g/L铋;原位,CV或方波阳极溶出伏安法在铅(II)污染的水溶液中稀释$200~\mu $ g/L铋。铋功能化喷墨印刷ECSs的检出限(LOD)为$0.64~\mu $ g/dL(非原位),铋沉积WE ($13.92\Omega $)。$\Box ~\pm ~1.96$)和$1.09~\mu $ g/dL(原位)的Au WE ($4.4\Omega $。$\Box ~\pm ~0.3$)。该传感器专为小型化设计,由于其增加的有效电极表面积(EESA) (7.25 mm ${}^{{2}}~\pm ~0.15$),尽管减少了侧表面积(4.19 mm2),但在电化学传感方面表现出增强的性能。本文提出了一种优化的清洗工艺,并建立了表征喷墨打印电极的印刷区域和电区之间关系的数学模型,为设计不同的电化学和生物传感器提供了灵活性和确定性。最后,本文详细描述了用于水铅(II)检测的铋功能化喷墨打印ECS的设计、制造、铋功能化、表征和优化,包括灵敏度、选择性、可重复性、可重用性和成本评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bismuth Functionalized Inkjet-Printed Electrochemical Sensor for Aqueous Lead (II) Detection
This article introduces a sensitive, selective, and reusable 3-D bismuth functionalized inkjet-printed shape memory polymer (SMP) incorporated electrochemical sensor (ECS) for detecting lead (II) in aqueous solution, that is, deionized water, tap water, and river water. The reported ECS comprises a bismuth deposited working electrode (WE), a gold (Au) counter electrode, and a silver chloride (AgCl) reference electrode. Bismuth functionalization was performed through two approaches on the inkjet-printed Au-plated (thickness – $8.24~\mu $ m ±1.42) electrodes: ex situ, involving a full cycle of cyclic voltammetry (CV) process to deposit bismuth – $400~\mu $ g/L bismuth, and in situ, where CV or square wave anodic stripping voltammetry were performed with a lead (II) contaminated aqueous solution diluted with $200~\mu $ g/L bismuth. The limit of detection (LOD) for the bismuth functionalized inkjet-printed ECSs are $0.64~\mu $ g/dL (ex situ) for the bismuth deposited WE ( $13.92\Omega $ . $\Box ~\pm ~1.96$ ) and $1.09~\mu $ g/dL (in situ) for the Au WE ( $4.4\Omega $ . $\Box ~\pm ~0.3$ ). The sensor, designed for miniaturization, exhibits enhanced performance in electrochemical sensing due to its increased effective electrode surface area (EESA) (7.25 mm ${}^{{2}}~\pm ~0.15$ ) despite a reduced lateral surface area (4.19 mm2). An optimized cleaning process and a mathematical model representing the relationship between printed and electrical areas of the inkjet-printed electrodes are presented thoroughly to provide flexibility and certainty of designing different electrochemical and biosensors. Conclusively, the design, fabrication, bismuth functionalization, characterization, and optimization encompassing sensitivity, selectivity, reproducibility, reusability, and cost-assessment of the bismuth functionalized inkjet-printed ECS for aqueous lead (II) detection are delineated in this article in detail.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
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
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