Shahzad Noor, Afzal Adeel
Sensors and Diagnostics Lab, School of Chemistry, University of the Punjab, Quaid-I-Azam Campus, Lahore, 54590, Pakistan.
Mikrochim Acta. 2025 Feb 14;192(3):163. doi: 10.1007/s00604-025-07008-0.
A disposable electrochemical sensor is introduced for the selective recognition of uric acid (UA), a crucial biomarker for gout arthritis. The sensor employs synthetic antibodies composed of molecularly imprinted polythiophene (MIP) laden graphitic carbon nitride (GCN) nanocomposites for the selective recognition of UA. Microscopic analysis demonstrates an increase in surface roughness and kurtosis after removing the template, indicating the fabrication and functionalization of the MIP/GCN sensors. These sensors exhibit excellent electrochemical properties, characterized by electrochemical impedance spectroscopy (EIS) and voltammetric (CV, DPV) methods. The sensor displays a wide linear detection range (1-500 µM), encompassing the normal UA levels in human saliva, high sensitivity (5.47 µA/cm.µM), a low limit of detection (0.21 µM), and limit of quantification (0.64 µM). The sensor also exhibits low cross-sensitivity to common salivary interferences, including urea, creatinine, ascorbic acid, glucose, and glutamine. The MIP/GCN sensor accurately identifies UA in human saliva, resulting in a recovery of 93.25 ± 0.33%. Electrochemical studies, utilizing [Fe(CN)] as a redox probe, also provide insights into the mechanisms of interfacial redox reactions and selective UA recognition. This work demonstrates a significant improvement in POC testing, providing a reliable and non-invasive tool for gout diagnosis.
一种一次性电化学传感器被用于选择性识别尿酸(UA),尿酸是痛风性关节炎的关键生物标志物。该传感器采用由负载分子印迹聚噻吩(MIP)的石墨相氮化碳(GCN)纳米复合材料组成的合成抗体来选择性识别尿酸。微观分析表明,去除模板后表面粗糙度和峰度增加,表明MIP/GCN传感器的制造和功能化。这些传感器表现出优异的电化学性能,通过电化学阻抗谱(EIS)和伏安法(CV、DPV)进行表征。该传感器显示出较宽的线性检测范围(1 - 500 μM),涵盖了人类唾液中的正常尿酸水平,具有高灵敏度(5.47 μA/cm·μM)、低检测限(0.21 μM)和定量限(0.64 μM)。该传感器对常见的唾液干扰物,包括尿素、肌酐、抗坏血酸、葡萄糖和谷氨酰胺,也表现出低交叉敏感性。MIP/GCN传感器能够准确识别人类唾液中的尿酸,回收率为93.25±0.33%。利用[Fe(CN)]作为氧化还原探针的电化学研究,也为界面氧化还原反应和尿酸选择性识别的机制提供了见解。这项工作展示了即时检测(POC)的显著改进,为痛风诊断提供了一种可靠且非侵入性的工具。