Oxygen redox catalysis, including the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), is crucial in determining the electrochemical performance of energy conversion and storage devices such as fuel cells, metal–air batteries,and electrolyzers. The rational design of electrochemical catalysts replaces the traditional trial‐and‐error methods and thus promotes the R&D process. Identifying descriptors that link structure and activity as well as selectivity of catalysts is the key for rational design. In the past few decades, two types of descriptors including bulk‐ and surface‐based have been developed to probe the structure–property relationships. Correlating the current descriptors to one another will promote the understanding of the underlying physics and chemistry, triggering further development of more universal descriptors for the future design of electrocatalysts. Herein, the current benchmark activity descriptors for oxygen electrocatalysis as well as their applications are reviewed. Particular attention is paid to circumventing the scaling relationship of oxygen‐containing intermediates. For hybrid materials, multiple descriptors will show stronger predictive power by considering more factors such as interface reconstruction, confinement effect, multisite adsorption, etc. Machine learning and high‐throughput simulations can thus be crucial in assisting the discovery of new multiple descriptors and reaction mechanisms.

No abstract

Electrocatalytic nitrogen reduction reaction (NRR) enabled by introducing Ti 3+ defect sites into TiO 2 through a doping strategy has recently attracted widespread attention. However, the amount of Ti 3+ ions is limited due to the low concentration of dopants. Herein, we propose Ti 2 O 3 nanoparticles as a pure Ti 3+ system that performs efficiently toward NH 3 electrosynthesis under ambient conditions. This work has suggested that Ti 3+ ions, as the main catalytically active sites, significantly increase the NRR activity. In an acidic electrolyte, Ti 2 O 3 achieves extraordinary performance with a high NH 3 yield and a Faradaic efficiency of 26.01 μg h −1 mg −1 cat. and 9.16%, respectively, which are superior to most titanium-based NRR catalysts recently reported. Significantly, it also demonstrates a stable NH 3 yield in five consecutive cycles. Theoretical calculations uncovered that the enhanced electrocatalytic activity of Ti 2 O 3 originated from Ti 3+ active sites and significantly lowered the overpotential of the potential-determining step.

A concise and efficient synthetic approach to producing a novel non-cyclic nucleotide EPAC antagonist ESI-09 and its new analogs is reported. Key features of the synthesis include a mild and reliable one-pot procedure for an isoxazole synthon, as well as a modified one-pot protocol for the cyanomethyl ketone key intermediate. The synthesis requires inexpensive starting materials and only three linear steps for the completion in a total yield of 53%.

Accurate prediction of the future energy needs is crucial for energy management. This work presents a novel grey forecasting model that integrates the principle of new information priority into accumulated generation. This grey model can better reflect the priority of the new information theoretically. The results of two practical examples demonstrate that this grey model provides very remarkable short-term predication performance compared with traditional grey forecasting model for limited data set forecasting. It is applied to Chinese gas consumption forecasting to show its superiority and applicability.