Reversible Fusion-Fission MXene Fiber-Based Microelectrodes for Target-Specific Gram-Positive and Gram-Negative Bacterium Discrimination.
Limin LiXiaoteng DingShuo ShanShengnan ChenYifan ZhangCai ZhangChao HuangMeilin DuanKaikai XuXue ZhangTianming WuZhen ZhaoYinhua LiuYuanhong XuPublished in: Analytical chemistry (2024)
Inaccurate or cumbersome clinical pathogen diagnosis between Gram-positive bacteria (G + ) and Gram-negative (G - ) bacteria lead to delayed clinical therapeutic interventions. Microelectrode-based electrochemical sensors exhibit the significant advantages of rapid response and minimal sample consumption, but the loading capacity and discrimination precision are weak. Herein, we develop reversible fusion-fission MXene-based fiber microelectrodes for G + /G - bacteria analysis. During the fissuring process, the spatial utilization, loading capacity, sensitivity, and selectivity of microelectrodes were maximized, and polymyxin B and vancomycin were assembled for G + /G - identification. The surface-tension-driven reversible fusion facilitated its reusability. A deep learning model was further applied for the electrochemical impedance spectroscopy (EIS) identification in diverse ratio concentrations of G + and G - of (1:100-100:1) with higher accuracy (>93%) and gave predictable detection results for unknown samples. Meanwhile, the as-proposed sensing platform reached higher sensitivity toward E. coli (24.3 CFU/mL) and S. aureus (37.2 CFU/mL) in 20 min. The as-proposed platform provides valuable insights for bacterium discrimination and quantification.
Keyphrases
- gram negative
- multidrug resistant
- deep learning
- label free
- gold nanoparticles
- loop mediated isothermal amplification
- high throughput
- ionic liquid
- escherichia coli
- high resolution
- machine learning
- molecularly imprinted
- magnetic resonance imaging
- candida albicans
- bioinformatics analysis
- computed tomography
- magnetic resonance
- mass spectrometry
- deep brain stimulation
- low cost
- single cell