Machine learning guided development of high-performance nano-structured nickel electrodes for alkaline water electrolysis

Veronica Humlebæk Jensen, Enzo Raffaele Moretti*, Jonas Busk, Emil Howaldt Christiansen, Sofie Marie Skov, Emilie Jacobsen, Mikkel Rykær Kraglund, Arghya Bhowmik, Ragnar Kiebach*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Utilizing a human in the loop Bayesian optimisation paradigm based on Gaussian process regression, we optimized an Ni electrodeposition method to synthesize nano-structured, high-performance hydrogen evolution reaction electrodes. Via exploration-exploitation stages, the synthesis process variables current density, temperature, ligand concentration and deposition time were optimized influencing the deposition layer morphology and, consequently, hydrogen evolution reaction activity. The resulting structures range from micrometre-sized, star-shaped features to nano-sized sandpaper-like structures with very high specific surface areas and good hydrogen evolution reaction activity. Using the overpotential at 10 mA cm−2 as the figure of merit, hydrogen evolution reaction overpotentials as low as -117 mV were reached, approaching the best known technical high surface area electrodes (e.g. Raney Ni). This is achieved with considerably fewer experiments than what would have been necessary with a linear grid search, as the machine learning model could capture the unintuitive interdependencies of the synthesis variables.
Original languageEnglish
Article number102005
JournalApplied Materials Today
Volume35
Number of pages10
ISSN2352-9407
DOIs
Publication statusPublished - 2023

Keywords

  • Water electrolysis
  • Nano catalyst
  • Hydrogen evolution reaction
  • Bayesian optimization
  • Technical electrodes
  • Human in the loop

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