Machine Learning Unveils Ce–O–Co Motifs in Perovskite with Lewis Acidic–Basic Microenvironment for Dual-Site Intramolecular Oxygen Evolution in Saline Water

Perovskite oxides are promising anodic electrocatalysts
for alkaline
saline water electrolysis, yet their performance is often limited
by sluggish oxygen evolution reaction kinetics and competing chloride
corrosion. Building on Pr0.1Sr0.9Co0.5Fe0.5O3 (PSCF), a candidate previously identified
via our transfer learning paradigm, we further leveraged data-driven
compositional searching of the A-site configuration, which led to
an optimized catalyst with a proper Ce/Sr/Pr ratio (Ce-PSCF). The
asymmetric Ce–O–Co motifs invoked a negative charge-transfer
regime and activated the evolution of nonbonding oxygen (ONB) states, which reconciled the activity-stability trade-off and promoted
a dual-site intramolecular lattice oxygen mechanism, as evidenced
by promoted 18O–18O evolution. The electrolyzer
with Ce-PSCF lowered the overpotential of the PSCF-based one by 100
mV at 300 mA cm–2 and exhibited 15-fold greater
durability. Electrochemical analysis revealed a higher reaction order,
promoted *OH coverage, and faster surface reconstruction on the Ce-incorporated
electrocatalyst, indicating *OH-controlled chemical-step kinetics.
Theoretical insight inferred a variation of the rate-determining step
from O–O coupling to secondary OH– refilling
with a reduced energy barrier from 0.68 to 0.54 eV. Moreover, ab initio
molecular dynamics simulations visualized a dynamically strengthened
affinity for OH– and significant repulsion of Cl–, arising from its intrinsic Lewis acidic and basic
microenvironment.