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Published on: May 3, 2024
Self-Assembled Peptide Hydrogel-Engineered Au/SCN/PCN-222 Photocathode for Antifouling Photoelectrochemical
Feng Xu1, Yao Xiao1, Gao-Chao Fan1
1Key Laboratory of Optic-Electric Sensing and Analytical Chemistry for Life Science, MOE, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao266042, China.
Abstract:
S100B is a clinically important biomarker for traumatic brain injury and neurodegenerative diseases, necessitating the development of sensitive and reliable analytical methods for its determination in complex biological fluids susceptible to potential biofouling and electroactive interference. Herein, we report a robust antifouling photoelectrochemical (PEC) immunoassay based on a self-assembled peptide hydrogel-engineered Au/SCN/PCN-222 photocathode for sensitive S100B detection. The Au/SCN/PCN-222 photocathode integrates sulfur-doped carbon nitride (SCN) quantum dots and Au nanoparticles with the porphyrinic metal-organic framework PCN-222, serving as the signal transduction element with enhanced light absorption and improved charge transfer, thereby increasing the cathodic photocurrent response. To address nonspecific adsorption and biofouling, a self-assembled Fmoc-FF-GG-EK-EK-EK peptide hydrogel was engineered on the photocathode surface, forming a highly hydrated, charge-balanced three-dimensional interface that effectively suppresses biofouling while enabling efficient antibody immobilization. Upon specific recognition of S100B, immune complex formation increases interfacial steric hindrance and suppresses charge transfer, resulting in a concentration-dependent decrease in photocurrent. The proposed PEC immunoassay exhibited high sensitivity, excellent antifouling performance, and good selectivity, enabling reliable analysis in complex biological matrices. This work establishes a general peptide hydrogel-based interfacial engineering strategy for antifouling PEC bioanalysis, providing a versatile and robust platform for biomarker detection.

