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

Abstract

A trimetallic nitrogen-incorporated CoNiFe exhibited good catalytic properties toward the oxygen evolution reaction, e.g., high stability and low overpotential (318 mV at 10 mA cm−2).

Facile synthesis of a medium-entropy CoNiFe subacetate nanoprism for high efficiency oxygen evolution reaction

“…4b). In addition, the catalytic activity of the CoNiFe NP is also comparable with many reported similar catalysts, such as CoNiP/CoNi-G/CMF-2 (341 mV at 10 mA cm −2 ), 35 FeCuNiCu medium entropy (439 mV at 50 mA cm −2 ), 36 N-CoNiFe (318 mV at 10 mA cm −2 ), 37 Mo-NiFe LDH (317 mV at 20 mA cm −2 ) 38 and CoNiFe/MnO@CNTs (275 mV at 10 mA cm −2 mV). 39 The results show that the trimetallic CoNiFe NP exhibited relatively good OER activity due to the synergistic effect of Co, Ni and Fe elements.…”

Hollow Nickel Nanochains Coated with Nickel-Iron Hydroxide for Oxygen Evolution

“…Consequently, extensive research efforts have been focused on developing alternative water electrolysis catalysts based on transition metal compounds, such as perovskite oxides, , carbides, nitrides, phosphides, and sulfides . Additionally, layered double hydroxides (LDHs), which are two-dimensional materials with layered structures, have been recognized as excellent catalysts owing to their relatively large surface area and anion exchange ability. , The introduction of highly oxidized transition metal ions into LDHs has a synergistic effect on the catalytic activity of OER. − However, LDHs suffer from low electrical conductivity and weak structural stability, owing to the presence of anions and water molecules between their layers. − To overcome these drawbacks, transition metals have been combined with carbon nanostructures to produce systems such as NiFe-graphene, , NiCo-graphene, and NiFe-carbon nanotubes . The electrical conductivity limit has been improved through the hierarchical growth of an LDH structure on a precious metal framework with excellent conductivity .…”

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

“…Ergo, the objective of this work is to implement a straightforward ML-driven optimization of LDH synthesis for overall water splitting based solely on the elemental composition of the electrocatalyst, with a small initial training dataset generated in-house and efficient convergence towards optimum performance via optimum tuning of the Bayesian Optimization process. 33 Given the rich literature on NiFe, 6,13,34 cobalt-iron (CoFe), [35][36][37] and NiCoFe 7,38 LDH-based catalysts and the abovementioned molybdate-and tungstate-intercalated LDHs, we seek an optimized LDH synthesis for overall water splitting centred around these five metal components. Given the five-dimensional material space selected, a typical full-scale optimization approach would be extremely time-and resource-intensive.…”

“…Given the rich literature on NiFe, 6,13,34 cobalt–iron (CoFe), 35–37 and NiCoFe 7,38 LDH-based catalysts and the abovementioned molybdate- and tungstate-intercalated LDHs, we seek an optimized LDH synthesis for overall water splitting centred around these five metal components. Given the five-dimensional material space selected, a typical full-scale optimization approach would be extremely time- and resource-intensive.…”