HIU Seminar | Dr. Janine Richter
Dr. Janine Richter from the McGill University Montreal, Canada will be guest during the upcoming HIU Seminar taking place on Tuesday, 9th July 2026 at 11:00 am: The talk will be held in person in the large seminar room (230) at the HIU and streamed under the usual Zoom link: The title of the talk will be "Engineering Stable Aqueous Zn Metal Anodes: From Deposition Behavior to Interfacial Chemistry”.
Abstract
In developing new advanced battery materials, the acceleration of the design process is proving essential to explore very complex composition spaces. Introducing automation into high-throughput experiments allows fast screening of the numerous key metrics needed to evaluate battery performance.
One such parameter for battery electrode kinetics is the lithium-ion diffusion coefficient, D, that is directly linked to rate capability and fast-charging performance. However, common electrochemical techniques typically used to determine D – potentiostatic intermittent titration technique (PITT), galvanostatic intermittent titration technique (GITT), and electrochemical impedance spectroscopy (EIS) – often yield highly variable results due to sensitivity to experimental conditions and analysis assumptions. This variability limits the generation of reliable datasets for machine-learning-assisted battery research.
This lecture presents three semi-automated high-throughput workflows for the rapid extraction of diffusion coefficients using PITT, GITT, and EIS. By combining combinatorial electrochemical cells with automated data analysis, several hundred measurements can be acquired and evaluated within days. The workflows enable simultaneous characterization of up to 64 samples, while automated analysis is performed using in-house Python routines and openly available software packages. Systematic reproducibility studies across hundreds of measurements reveal sources of uncertainty that are often overlooked in conventional experiments. The presented approach provides a framework for generating robust electrochemical datasets suitable for data-driven battery discovery and next-generation self-driving laboratories.