Momentum for Catalysis: How Surface Reactions Shape the RuO2 Flat Surface State

A Covalent Organic Framework for Cooperative Water Oxidation

The future of water-derived hydrogen as the “sustainable energy source” straightaway bets on the success of the sluggish oxygen-generating half-reaction. The endeavor to emulate the natural photosystem II for efficient water oxidation has been extended across the spectrum of organic and inorganic combinations. However, the achievement has so far been restricted to homogeneous catalysts rather than their pristine heterogeneous forms. The poor structural understanding and control over the mechanistic pathway often impede the overall development. Herein, we have synthesized a highly crystalline covalent organic framework (COF) for chemical and photochemical water oxidation. The interpenetrated structure assures the catalyst stability, as the catalyst’s performance remains unaltered after several cycles. This COF exhibits the highest ever accomplished catalytic activity for such an organometallic crystalline solid-state material where the rate of oxygen evolution is as high as ∼26,000 μmol L^–1 s^–1 (second-order rate constant k ≈ 1650 μmol L s^–1 g^–2). The catalyst also proves its exceptional activity (k ≈ 1600 μmol L s^–1 g^–2) during light-driven water oxidation under very dilute conditions. The cooperative interaction between metal centers in the crystalline network offers 20–30-fold superior activity during chemical as well as photocatalytic water oxidation as compared to its amorphous polymeric counterpart.

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.