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.

Surface charge as activity descriptors for electrochemical CO2 reduction to multi-carbon products on organic-functionalised Cu.

Intensive research in electrochemical CO2 reduction reaction has resulted in the discovery of numerous high-performance catalysts selective to multi-carbon products, with most of these catalysts still being purely transition metal based. Herein, we present high and stable multi-carbon products selectivity of up to 76.6% across a wide potential range of 1 V on histidine-functionalised Cu. In-situ Raman and density functional theory calculations revealed alternative reaction pathways that involve direct interactions between adsorbed histidine and CO2 reduction intermediates at more cathodic potentials. Strikingly, we found that the yield of multi-carbon products is closely correlated to the surface charge on the catalyst surface, quantified by a pulsed voltammetry-based technique which proved reliable even at very cathodic potentials. We ascribe the surface charge to the population density of adsorbed species on the catalyst surface, which may be exploited as a powerful tool to explain CO2 reduction activity and as a proxy for future catalyst discovery, including organic-inorganic hybrids.

Metal-salen molecular cages as efficient and recyclable heterogeneous catalysts for cycloaddition of CO2 with epoxides under ambient conditions.

A salen based molecular cage, salen@cage, was synthesized and complexed with Co and Al to yield metal-salen molecular cages, Co(ii)@cage, Co(iii)@cage and Al(iii)@cage. These cages were demonstrated to be efficient heterogeneous catalysts for the cycloaddition of CO2 with styrene oxide, achieving full conversion at 25 °C and 1 atm CO2. Good to excellent yields of various cyclic carbonates were also achieved under mild conditions. Al(iii)@cage can be reused up to five times without any significant loss of its high catalytic activity. The capability to access a variety of heterogeneous organometallic catalysts with salen@cage offers new prospects for practical CO2 utilization and chemical manufacturing.

Anion-π interactions of highly π-acidic dipyridinium-naphthalene diimide salts.

A highly π-acidic dipyridinium-naphthalene diimide acceptor shows anion-π interactions with halides and PF6-. Lewis basicity and redox potential of the anion affect the chemistry, and photophysical and electrochemical properties, as well as both ionic and electrical conductivities. Our results provide insights into doping, degradation and ion transport mechanisms in organic n-type semiconductors.

Correction: A binary catalyst system of a cationic Ru-CNC pincer complex with an alkali metal salt for selective hydroboration of carbon dioxide.

Correction for 'A binary catalyst system of a cationic Ru-CNC pincer complex with an alkali metal salt for selective hydroboration of carbon dioxide' by Chee Koon Ng et al., Chem. Commun., 2016, 52, 11842-11845.

A binary catalyst system of a cationic Ru-CNC pincer complex with an alkali metal salt for selective hydroboration of carbon dioxide.

Binary catalyst systems comprising a cationic Ru-CNC pincer complex and an alkali metal salt were developed for selective hydroboration of CO2 utilizing pinacolborane at r.t. and 1 atm CO2, with the combination of [Ru(CNCBn)(CO)2(H)][PF6] and KOCO2tBu producing formoxyborane in 76% yield. A bicyclic catalytic mechanism was proposed and discussed.