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Crystal Facet Engineering Modulated Electron Transfer Mechanisms: A Self-Powered Photoelectrochemical Sensing Platform for Noninvasive Detection of Uric Acid

化学 面(心理学) 电子转移 尿酸 Crystal(编程语言) 纳米技术 光化学 光电子学 生物化学 物理 程序设计语言 材料科学 人格 五大性格特征 社会心理学 计算机科学 心理学
作者
Shuo Tian,Zhichao Yu,Yunsen Wang,Shuyun Chen,Mei‐Jin Li,Dianping Tang
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:97 (17): 9518-9526 被引量:76
标识
DOI:10.1021/acs.analchem.5c01345
摘要

Crystal facet engineering is a pivotal strategy to design high-performance photoelectrodes and suppress electron and hole complexation, thus enhancing photoelectrochemical (PEC) activity through carrier enrichment at specific crystal facets. However, there is still a lack of systematic resolution on the intrinsic principles of crystal facet tuning energy band structure and the specific adsorption of signaling molecules. In this work, a multidimensional synergistic optimization strategy was proposed to achieve precise prediction and targeted crystal facet design of photoelectrodes by establishing a quantitative structure-activity relationship (QSAR) model of "crystal configuration-molecular recognition-carrier transport". A three-dimensional hierarchical TiO2 nanoflower (3D HTNF) photoelectrode dominated by the {110} facet exhibited a significant positive photocurrent toward uric acid (UA). Integrated with a microelectromechanical system (MEMS), a miniaturized self-powered PEC biosensor provided an innovative solution for high-throughput, noninvasive UA monitoring in saliva and displayed a linear range of 0.01-50 μM with a detection limit of 8.76 nM. In addition, the advantages of photoelectrodes in light harvesting, charge separation and migration, molecular adsorption, and surface reactions were verified by density functional theory (DFT) calculations to reveal the path selectivity and carrier transport mechanisms of the photo-oxidation reactions on specific crystal surfaces. This study elucidates the interplay mechanism of the crystal surface tuning energy band structure and the interfacial kinetics of response. The program can be extended to precisely detect biomarkers in complex biological matrices, promoting the leapfrog development of noninvasive health monitoring technology.
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