Development of a Nonenzymatic Potentiometric or Amperometric Lactate Biosensor Enhanced by Silver Phosphate/Titanium Dioxide on Flexible Printed Circuit Board

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yu-Hsun Nien;Xin-Han Chen;Jung-Chuan Chou;Chih-Hsien Lai;Po-Yu Kuo;Po-Hui Yang;Yu-Wei Chen;Lian-Sheng Jiang
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引用次数: 0

Abstract

In this study, a nonenzymatic lactate biosensor based on a novel silver phosphate and titanium dioxide (Ag3PO4/TiO2)-modified SnO2 sensing platform was developed to improve the detection performance. Different from traditional enzyme or single metal oxide sensors, this work introduces a catalytic composite Ag3PO4/TiO2 combined with SnO2 to enhance charge transfer and surface reactivity under ambient conditions. This improvement significantly increased the selectivity and average sensitivity of lactate detection. The interaction of lactic acid with the composite material induces measurable voltage or current changes, which can be characterized through voltage-time (V– ${T}\text {)}$ and current-time (I– ${T}\text {)}$ measurements. The proposed lactate biosensor demonstrated a sensitivity of $43.38~\mu $ A $\cdot $ mM ${}^{-{1}}~\cdot $ cm ${}^{-{2}}$ with a linearity of 0.997 in amperometric measurements, and a sensitivity of 8.09 mV/mM with a linearity of 0.992 in potentiometric measurements. The limit of detection (LOD) was as low as 0.01 mM, the response time was only $6~{s}$ , and excellent selectivity was observed against common interfering species such as glucose, uric acid (UA), as well as ascorbic acid (AA). The experimental results show that the Ag3PO4/TiO2 modified sensor exhibits higher sensitivity, improved linearity over a wider lactic acid concentration range, and superior stability compared to the unmodified SnO2 sensor. Structural and morphological analyses (X-Ray diffractometer (XRD), SEM, Raman) confirmed the homogeneous dispersion and successful integration of the composites. In addition, this sensor also has the characteristics of fast response speed, high selectivity, low hysteresis, and good long-term operation stability. These findings suggest that the proposed Ag3PO4/TiO2SnO2 platform provides a promising alternative to conventional lactate sensing strategies, especially in nonenzymatic detection systems.
柔性印刷电路板上磷酸银/二氧化钛增强非酶电位或电流乳酸生物传感器的研制
为了提高检测性能,本研究开发了一种基于新型磷酸银和二氧化钛(Ag3PO4/TiO2)修饰的SnO2传感平台的无酶乳酸生物传感器。与传统的酶或单金属氧化物传感器不同,本研究引入了一种催化复合材料Ag3PO4/TiO2与SnO2结合,以增强环境条件下的电荷转移和表面反应性。这一改进显著提高了乳酸检测的选择性和平均灵敏度。乳酸与复合材料的相互作用引起可测量的电压或电流变化,可以通过电压时间(V - ${T}\text{)}$和电流时间(I - ${T}\text{)}$测量来表征。该乳酸生物传感器的灵敏度为43.38~\mu $ a $\cdot $ mM ${}^{-{1}}~\cdot $ cm ${}^{-{2}}$,在安培测量中线性度为0.997;在电位测量中灵敏度为8.09 mV/mM,线性度为0.992。检出限(LOD)低至0.01 mM,响应时间仅为6~{s}$,对葡萄糖、尿酸(UA)、抗坏血酸(AA)等常见干扰物具有良好的选择性。实验结果表明,与未改性的SnO2传感器相比,改性后的Ag3PO4/TiO2传感器具有更高的灵敏度,在更宽的乳酸浓度范围内改善了线性度,并且具有更好的稳定性。结构和形态分析(x射线衍射仪(XRD)、扫描电镜(SEM)、拉曼光谱(Raman))证实了复合材料的均匀分散和成功集成。此外,该传感器还具有响应速度快、选择性高、迟滞低、长期工作稳定性好等特点。这些发现表明,所提出的Ag3PO4/TiO2SnO2平台为传统的乳酸传感策略提供了一个有希望的替代方案,特别是在非酶检测系统中。
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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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