Controlled synthesis of trimetallic nitrogen-incorporated CoNiFe layered double hydroxide electrocatalysts for boosting the oxygen evolution reaction

A facile N-doped NiFe(B) (Oxy)hydroxide monolithic electrode for enhanced water oxidation

N-doped NiFe(B) (oxy)hydroxide can promote the catalytic activity for an alkaline oxygen evolution reaction (OER) significantly, but fabrication is difficult. Herein, we introduced a B-induction route to the N-NiFe(B) (oxy)hydroxide monolithic electrode under a relatively low temperature. We observed an excellent catalytic performance benefiting from an optimal electronic structure, enlarged surface area and improved hydrophilicity. Moreover, this mild protocol could be extended to fabricate an S-doped NiFe-based catalyst. This research could aid large-scale manufacture.

Amorphous-crystalline heterostructures enable energy-level matching of cobalt sulfide/nickel iron layered double hydroxide for efficient oxygen evolution reaction

Interface engineering of heterostructures has emerged as a promising approach to enhance the catalytic activity of nonprecious electrocatalysts. Herein, a novel amorphous cobalt sulfide-crystalline nickel iron layered double hydroxide (a-CoS@NiFe-LDH) hybrid material is presented for application as an electrocatalyst for oxygen evolution reaction (OER). Benefitting from the well-matched energy level structures, the a-CoS@NiFe-LDH catalyst delivers a low overpotential of 221 ± 14 mV at an OER current density of 20 mA cm-2 and a small Tafel slope of 83.1 mV dec-1, showing good OER properties. First-principle computations reveal that the electronic interaction between amorphous cobalt sulfide (a-CoS) and crystalline nickel iron layered double hydroxide (NiFe-LDH) components within a-CoS@NiFe-LDH promotes the adsorbate evolution mechanism and reduces the adsorption energies for oxygen intermediates, thereby enhancing the activity and stability for OER. This work opens up a new avenue to enhance the OER catalytic efficiency via the construction of amorphous-crystalline heterostructures.

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting

Green hydrogen produced via electrochemical water splitting is a suitable candidate to replace emission-intensive fuels. However, the successful widespread adoption of green hydrogen is contingent on the development of low-cost, earth-abundant catalysts. Herein, machine learning models built on experimental data were used to optimize the precursor ratios of hydroxide-based electrocatalysts, with the objective of improving the product's electrocatalytic performance for overall water splitting. The Neural Network-based models were found to be the most effective in predicting and minimizing the overpotentials of the catalysts, reaching a minimum in two iterations. The relatively mild reaction conditions of the synthesis procedure, coupled with its scalability demonstrated herein, renders the optimized catalyst relevant for industrial implementation in the future. The optimized catalyst, characterized to be a molybdate-intercalated CoFe LDH, demonstrated overpotentials of 266 and 272 mV at 10 mA cm-2 for oxygen and hydrogen evolution reactions respectively in alkaline electrolyte, alongside unwavering stability for overall water splitting over 50 h. Overall, our results reflect the efficacy and advantages of machine learning strategies to alleviate the time and labour-intensive nature of experimental optimizations, which can greatly accelerate electrocatalysts research.