Biocompatible WSe 2 @BSA Dots with Merged Catalyst and Coreactant for Efficient Electrochemiluminescence.
Fei YinXiaohe ZhouMingming ZhangQian SunJinjin ZhaoGuoqiu WuYuanjian ZhangYanfei ShenPublished in: Small (Weinheim an der Bergstrasse, Germany) (2024)
Electrochemiluminescence (ECL) is a powerful tool for clinical diagnosis due to its exceptional sensitivity. However, the standard tripropylamine (TPrA) coreactant for Ru(bpy) 3 Cl 2 , the most widely studied and used ECL system, is highly toxic. Despite extensive research on alternative coreactants, they often fall short in poor efficiency. From a reaction kinetics perspective, accelerating electrooxidation rate of Ru(bpy) 3 Cl 2 is an essential way to compensate the efficiency limitation of coreactants, but is rarely reported. Here, a hybrid electrocatalyst@coreactant dots for the ECL of Ru(bpy) 3 Cl 2 is reported. The as-prepared WSe 2 @bovine serum albumin (WSe 2 @BSA) dots is biocompatible, and demonstrate dual functions, i.e., the BSA shell works as a coreactant, meanwhile, the WSe 2 core effectively catalyzes Ru(bpy) 3 Cl 2 oxidation. As a result, WSe 2 @BSA dots exhibit an exceptionally high efficiency comparable to TPrA for the ECL of Ru(bpy) 3 Cl 2 . In addition, the procedure for synthesizing WSe 2 @BSA dots is facile (room temperature, atmospheric conditions), rapid (5 min), and scalable (for millions of bioassays). A biosensor utilizing WSe 2 @BSA dots shows promise for highly sensitive detecting glypican-3 in clinical liver cancer serum samples, especially for alpha-fetoprotein-negative patients. This work opens a new avenue for developing a highly efficient ECL system for biosensing and clinical diagnosis.
Keyphrases
- fluorescent probe
- highly efficient
- room temperature
- energy transfer
- molecularly imprinted
- ionic liquid
- high efficiency
- living cells
- sensitive detection
- quantum dots
- newly diagnosed
- ejection fraction
- gold nanoparticles
- hydrogen peroxide
- big data
- artificial intelligence
- label free
- metal organic framework
- particulate matter
- deep learning