Ziwen Zhao: Advancing Single-Entity Electrochemistry from Methods to Applications

Abstract

This thesis addresses key challenges in Nano-Impact Electrochemistry (NIE), an advanced technique within Single-Entity Electrochemistry (SEE) that enables the detection and analysis of individual nanoscale objects. By measuring discrete collision events between nanoscale entities and electrodes, NIE provides unique insights into electrochemical processes that remain hidden in conventional ensemble measurements. The research focuses on four significant challenges: improving signal-to-noise ratios through advances in instrumentation, developing robust computational methods for signal analysis, conducting measurements based on these advances, and validating experimental signals through theoretical modeling for accurate mechanistic interpretation.

Methodological developments first focused on noise reduction through optimized experimental setups. High-impedance sourcemeter configurations with specialized pre-amplifiers were implemented alongside refined nanoelectrode fabrication protocols, reducing noise levels from picoamperes to femtoamperes. Novel data analysis algorithms were then developed using sliding window transformations with shape-based signal detection rather than conventional threshold methods. These approaches incorporated data-driven template matching for spike detection and convolutional neural networks for step identification, enabling more reliable signal processing across diverse experimental conditions.

With these methodological foundations, NIE was applied to previously unexplored catalytic systems. Studies of plasmon-enhanced electrocatalysis on gold nanoparticles revealed wavelength-specific enhancement effects only observable at the single-entity level, providing evidence for the contribution of hot carriers in catalysis. Investigations of diffusion-limited enzymes (catalase and superoxide dismutase) demonstrated how NIE can be used to study enzymatic activity.

Computational modeling provided further theoretical frameworks for interpretation. A geometric model for catalytic NIE accurately reproduced experimental data for catalase, while sequential blocking simulations revealed how collision location and history affect observed signal characteristics.

This integrated approach combining experimental advancements, sophisticated data analysis, and computational modeling significantly expands NIE applications. The findings demonstrate NIE's unique capability to reveal entity-specific behaviors obscured in ensemble measurements, opening new avenues for understanding nanoscale electrochemical phenomena.