Automating scanning electrochemical probe microscopy: decision making in the loop

• Grant number: 26/04362-4
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: July 01, 2026
• End date: October 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Analytical Chemistry
• Principal Investigator: Gabriel Negrão Meloni
• Grantee: Takahara dos Santos
• Supervisor: Joaquin Rodriguez Lopez
• Host Institution: Instituto de Química (IQ). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: University of Illinois at Urbana-Champaign, United States
• Associated to the scholarship: 24/18651-2 - Affordable analytical instrumentation - from teaching to researching, BP.IC

Abstract

This project proposes the development of an autonomous guidance system for Scanning Electrochemical Microscopy (SECM) to improve efficiency in the investigation of structures of interest. Conventional SECM experiments require full-area scanning and post-acquisition data processing, which is time-consuming when the structures are sparsely distributed. The central objective is to integrate real-time electrochemical simulations and control algorithms to enable adaptive probe navigation. In Work Package 1, the open-source Python electrochemistry library SoftPotato will be extended to simulate SECM approach curves on the fly. These simulations will fit experimental data and extract kinetic parameters such as the rate constant (K0) and charge-transfer coefficient (¿), to estimate proximity between the probe and structure. Work Package 2 focuses on adapting algorithms from robotics to guide the SECM probe efficiently. Different exploration strategies, including intercalated scanning, spiral paths, random walks will be implemented and compared to a heuristic search algorithm that uses the kinetics parameters to optimize probe trajectory. Work Package 3 introduces computer vision as an alternative guidance strategy. An external camera system, such as an interference reflection microscope (IRM) for identifying nanoparticles, or a conventional camera for non-transparent substrates will be combined with computer vision libraries (OpenCV and YOLO). The optical information will be processed in real-time to identify structures and guide the probe accordingly. Together, the three work packages establish a closed-loop experimental framework. The project aims to reduce acquisition time. It also promotes open-source dissemination of code and integration tools. Ultimately, the research advances autonomous electrochemical experimentation and intelligent nanoscale characterization. (AU)