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Iridium oxide (IrO2) is the predominant electrocatalyst for the oxygen evolution reaction (OER) with high stability in both alkaline and harsh acidic media, but its low efficiency and high cost limit its applications. In this work, we have developed a high-throughput computational strategy based on density functional theory (DFT) and employ the machine-learning (ML) techniques to extensively explore the optimal dopants in IrO2 and enhance its OER activity. Thirty-six kinds of metal dopants are considered to substitute for Ir to form binary and ternary metal oxides with different concentrations, and the most stable surface structures are selected from a total of 4,648 structures for OER activity evaluation. By using the neural network language model (NNLM), we are able to associate the atomic environment with the formation energies of crystals and adsorption free energies of OER intermediates, which is efficient for complex compounds and accurately evaluates the structural stability and electrochemical activity. A series of potential candidates have been identified as superior OER catalysts, and some of them are novel with the lowest calculated overpotential. Our strategy could efficiently explore the promising electrocatalysts, especially for evaluating complex multi-metallic compounds.

The pursuit of selective two-electron oxygen reduction reaction to H

Almost all surfaces sensitive to the ambient environment are covered by water, whereas the impacts of water on surface-dominated colloidal quantum dot (CQD) semiconductor electronics have rarely been explored. Here, strongly hydrogen-bonded water on hydroxylated lead sulfide (PbS) CQD is identified. The water could pilot the thermally induced evolution of surface chemical environment, which significantly influences the nanostructures, carrier dynamics, and trap behaviors in CQD solar cells. The aggravation of surface hydroxylation and water adsorption triggers epitaxial CQD fusion during device fabrication under humid ambient, giving rise to the inter-band traps and deficiency in solar cells. To address this problem, meniscus-guided-coating technique is introduced to achieve dense-packed CQD solids and extrude ambient water, improving device performance and thermal stability. Our works not only elucidate the water involved PbS CQD surface chemistry, but may also achieve a comprehensive understanding of the impact of ambient water on CQD based electronics., Surface of colloidal quantum dot is sensitive to water, and the interaction could potentially alter its chemical environments. Here, Shi et al. investigate how the interaction effects the nanostructures and carrier dynamic in CQDs, and subsequently introduce meniscus-guided coating technique to mitigate CQD fusion triggered by water adsorption.

In hydrogen production, the anodic oxygen evolution reaction (OER) limits the energy conversion efficiency and also impacts stability in proton-exchange membrane water electrolyzers. Widely used Ir-based catalysts suffer from insufficient activity, while more active Ru-based catalysts tend to dissolve under OER conditions. This has been associated with the participation of lattice oxygen (lattice oxygen oxidation mechanism (LOM)), which may lead to the collapse of the crystal structure and accelerate the leaching of active Ru species, leading to low operating stability. Here we develop Sr-Ru-Ir ternary oxide electrocatalysts that achieve high OER activity and stability in acidic electrolyte. The catalysts achieve an overpotential of 190 mV at 10 mA cm-2 and the overpotential remains below 225 mV following 1,500 h of operation. X-ray absorption spectroscopy and 18O isotope-labeled online mass spectroscopy studies reveal that the participation of lattice oxygen during OER was suppressed by interactions in the Ru-O-Ir local structure, offering a picture of how stability was improved. The electronic structure of active Ru sites was modulated by Sr and Ir, optimizing the binding energetics of OER oxo-intermediates.

Here, we report a density functional theory (DFT)-based high-throughput screening method to successfully identify a type of alloy nanoclusters as the electrocatalyst for hydrogen evolution reaction (HER). Totally 7924 candidates of Cu-based alloy clusters of Cu55-nMn (M = Co, Ni, Ru, and Rh) are optimized and evaluated to screening for the promising catalysts. By comparing different structural patterns, Cu-based alloy clusters prefer the core–shell structures with the dopant metal in the core and Cu as the shell atoms. Generally speaking, the HER performance of the Cu-based nanoclusters can be significantly improved by doping transition metals, and the active sites are the bridge sites and three-fold sites on the outer-shell Cu atoms. Considering the structural stability and the electrochemical activity, core–shell CuNi alloy clusters are suggested to be the superior electrocatalyst for hydrogen evolution. A descriptor composing of surface charge is proposed to efficiently evaluate the HER activity of the alloy clusters supported by the DFT calculations and machine-learning techniques. Our screening strategy could accelerate the pace of discovery for promising HER electrocatalysts using metal alloy nanoclusters.