Research

Operando evidence, catalyst stability, and reproducible electrochemical interpretation.

Research map

We study what electrochemical interfaces do while they are working.

The group connects time-resolved measurements with catalyst design and rigorous data analysis. Each project asks a practical question: what changes at the interface, why does performance drift, and how can the measurement be interpreted without overclaiming?

See the interface Track morphology, spectra, and products with DEMS, UV-vis, Raman, and EC-AFM.
Explain degradation Resolve time-dependent loss of activity, selectivity, conductivity, and active area.
Standardize analysis Build reproducible workflows for impedance, iR correction, and Python-based analysis.
Area 01

Operando Spectroelectrochemistry and EC-AFM

We observe electrochemical interfaces as they evolve, then connect the observed changes to activity, selectivity, and stability.

DEMS UV-vis spectroelectrochemistry EC-AFM

Core Questions

  • Which surface changes occur during operation rather than before or after testing?
  • How do structural and morphological dynamics affect catalytic output?
  • Which signals are robust enough to guide catalyst design?

Selected Papers

More publications
Area 02

Electrocatalyst Degradation and Stability Analysis

We investigate why catalyst performance changes over time and how active sites, supports, and electrolytes contribute to that instability.

Stability testing Interface reconstruction Selectivity tracking

Core Questions

  • Which degradation pathway dominates under realistic operating histories?
  • How can transition-metal catalysts balance activity with durability?
  • What measurements distinguish true catalyst change from measurement artifacts?

Selected Papers

More publications
Area 03

Quantitative Electrochemical Methods and Data Workflows

We develop practical guidance and open computational workflows for electrochemical measurements that are easy to misinterpret.

Python tools Protocol design Data standardization

Core Questions

  • Which assumptions in common electrochemical workflows are fragile?
  • How should data be processed so others can reproduce the interpretation?
  • How can computational tools reduce avoidable analysis errors?

Selected Papers

More publications

Guiding Principle

The first principle is that you must not fool yourself. If it doesn't agree with experiment, it's wrong.

Prof. Richard Feynman