Misfit-strain-guided phase separation for programmable patterned catalysts with spatiotemporal adaptability

Abstract

Rational patterning of catalyst morphologies offers a powerful avenue to tailor surface chemistry and spatiotemporal reactivity, yet existing paradigms—such as Turing patterns—lack mechanical considerations essential for quantitatively predicting structure in abiotic systems. Here, a misfit-strain-guided phase separation model rooted in Cahn–Hilliard–Cook and Ginzburg–Landau frameworks, capturing the interplay between elastic heterogeneity and morphological evolution in alloy films is developed. This model enables the programmable design of patterned nanostructures by modulating local Young's modulus and applied stress fields. Guided by this principle, a spotty amorphous cobalt phosphide (Co-P) nanoglass with spatially segregated phases for electrocatalytic nitrate reduction to ammonia (eNRA) is synthesized. Operando spectroscopies and density functional theoretical calculations reveal that this strain-programmed architecture exhibits robust adaptability and record-high activity. The misfit-strain strategy presented here offers a broadly applicable, mechanically informed framework for the predictive design of dynamic, phase-engineered catalysts across diverse chemistries and materials platforms.