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The pH, or the acidity or alkalinity of an environment, has long been known to affect how efficiently catalysts drive key electrochemical reactions. Yet despite decades of research, the atomic-scale mechanisms behind these pH effects have eluded scientists.

A new study sheds light on this mystery by decoding how electric fields, surface properties, and charge dynamics intertwine to govern catalytic performance. The findings mark a significant step toward rationally designing catalysts that perform efficiently in a range of environments, paving the way for next-generation clean energy technologies.

The paper is published in the .

Traditional models have explained pH-dependent activity mainly through the computational hydrogen electrode (CHE) model and the Nernst equation. These frameworks linked shifts in activity to changes in potential and proton concentration.

However, the new research shows that the reality is far more complex, involving a web of interfacial electric fields and molecular interactions that standard models cannot fully capture.

Recent advances in both experimental and computational methods have revealed that properties such as dipole moments, polarizability, and the potential of zero charge (PZC) play a critical role. These factors determine how molecules and ions interact with catalyst surfaces, directly influencing reaction rates and selectivity.

By bringing together insights from electrochemistry, physics, and computational modeling, the research highlights how these interfacial effects manifest across a wide array of reactions, including hydrogen evolution (HER), oxygen reduction (ORR), carbon dioxide reduction (CO₂RR), and nitrate reduction (NO₃RR). These are among the most important reactions for renewable energy conversion, fuel generation, and environmental remediation.

"Our work shows that pH effects are not just surface-level phenomena; they are governed by the electric field environment at the interface," said Hao Li, a professor from Tohoku University's Advanced Institute for Materials Research (WPI-AIMR) who led the study.

"By understanding and modeling these fields, we can predict how catalysts behave under different pH conditions and ultimately design materials that are more efficient and sustainable."

The study also introduces advanced theoretical frameworks that go beyond traditional thermodynamic descriptions. Notably, the reversible hydrogen electrode (RHE)-referenced Pourbaix diagram and the pH-dependent microkinetic volcano model provide a more accurate depiction of catalytic activity and stability across varying electrochemical conditions.

These new models offer scientists a powerful toolkit for predicting and optimizing catalyst behavior at the atomic scale. By integrating experimental data with computational simulations, researchers are now able to map how subtle changes in pH shift reaction pathways and determine overall efficiency.

Looking ahead, the research team plans to combine molecular dynamics with machine learning potentials to simulate reaction conditions in real time. This approach could unlock even deeper insights into how catalysts evolve during operation, further accelerating the design of high-performance materials for a sustainable energy future.