Regulating electronic structure and adsorptivity in molybdenum selenide for boosting electrocatalytic water splitting

Correlation between d Electrons and the Sweet Spot for the Hydrogen Evolution Reaction: Is Platinum Always the Best Electrocatalyst?

Herein, two Laves intermetallic series, ZrCo1.75M0.25 and NbCo1.75M0.25 (M = Fe, Co, Ni, Ru, Rh, Pd, Os, Ir, and Pt), were synthesized, and their hydrogen evolution reaction (HER) activities were examined to reveal the influence of d electrons to the corresponding HER activities. Owing to the different electronegativity between Zr and Nb (χZr = 1.33; χNb = 1.60), Co and/or M elements receive more electrons in ZrCo1.75M0.25 than that of the Nb one. This leads to the overall weak H adsorption energy (ΔGHad) of ZrCo1.75M0.25 series compared to that of NbCo1.75M0.25 and rationalizes well the superior HER activity of the Rh member compared to that of the Pt one in the ZrCo1.75M0.25 series. Under industrial conditions (333 K, 6.0 M KOH), ZrCo1.75Rh0.25 only requires an overpotential of 110 mV to reach the current density of 500 mA/cm2 and can be operated at high current density over 400 h. This work demonstrates that with a proper combination between elements in intermetallic phases, one can manipulate d electrons of the active metal to be closer to the sweet spot (ΔGHad = 0). The Pt member may no longer exhibit the best HER activity in series, and all elements exhibit the potential to outperform the Pt member in the HER with careful control of the d electron population.

Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects

Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.