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AI Atomic Tomography Maps 3D Catalyst Degradation, Reveals Gallium Boosts PtNi Durability

The KAIST-led team reports the first direct 3D view of atomic-scale degradation in PtNi fuel-cell catalysts in a Nature Communications study.

Overview

  • Using neural network–assisted atomic electron tomography with AI-based corrections, researchers mapped the positions and identities of individual atoms in catalyst nanoparticles across thousands of cycles.
  • Gallium-doped PtNi retained roughly 96% of oxygen-reduction activity after 12,000 cycles compared with about 83% for undoped PtNi, indicating approximately 4% versus 17% loss.
  • Atomic reconstructions showed Ga helps preserve octahedral shape and {111} facets, limits nickel leaching from surface and subsurface regions, and maintains beneficial compressive strain.
  • The work was led by KAIST with collaborators at Stanford University and wrence BeBerkeley National Laboratory, and it was published online on August 28, 2025, in Nature Communications.
  • Authors present the method as a broadly applicable tool for designing longer-lived energy catalysts, noting the findings are based on laboratory-scale cycling tests rather than commercial deployments.