Molybdenum disulfide holds promise as a low cost and abundant catalyst for the hydrogen evolution reaction in an alkaline environment. However, its hydrogen evolution reaction activity is not sufficient for practical application because of its semiconducting properties in the 2H phase, presence of an electrochemically inert basal plane, and suboptimal hydrogen adsorption energy for hydrogen evolution reaction. In this article, we present a facile synthesis method for fabricating a Ni-doped molybdenum disulfide hydrogen evolution reaction electrode with a 1T structure through co-sputtering of molybdenum disulfide and Ni. Our results demonstrate that Ni doping not only promotes the 1T-phase yield in molybdenum disulfide structure but also activates the basal plane and improves the hydrogen adsorption energy of the edge plane. Also, the surface morphologies and 1T-phase yield, which are influenced by sputtering power and deposition time, are critical factors for the variation of hydrogen evolution reaction performance. Our Ni-doped molybdenum disulfide electrode, which exhibits high 1T yield and increased electrochemical surface area by tuning the morphology, shows an overpotential of ~91 mV at 10 mA cm−2, nearly 2.5 times lower than that of ~227 mV observed for molybdenum disulfide. Also, the single-cell test exhibits enhanced cell performance with improved durability in the repetitive on/off evaluation for the potential application of renewable energy integration.

Graphene oxide (GO) has emerged as a prominent membrane material due to its potential for precise gas and liquid separations enabled by surface modification and channel optimisation. Here, we present tunable graphitic nanofluidic channels-containing GO (gGO) membranes, characterised by a continuous sp2 hybridised carbon lattice distributed across 35 %–72 % of their structure. This architecture enables exceptional water vapour permeability through a Fickian diffusion mechanism, contrasting the non-Fickian transport typically observed in conventional GO membranes. Our findings demonstrate ultrafast water vapour transport through well-defined hydrophobic nanofluidic channels, achieving enhanced water vapour/N2 selectivity. Utilising scalable, top-down multi-scale simulations based on the inverse Ising method, we elucidate the critical role of graphitic sp2 domains in optimising selective diffusion pathways for water molecules. The resulting gGO-based thin-film composite membranes deliver state-of-the-art water vapour/gas separation applications, showcasing their potential for advanced humidification or dehumidification applications.

In response to growing demand for cost-effective, highly active, and stable catalysts for the electrocatalytic oxygen evolution reaction (OER), we have developed a comprehensive database for AA’BB’O6-type cubic double perovskites (DP) and implemented a machine learning high-throughput screening (ML-HTS) strategy to discover promising DP for OER. Our approach covers a vast chemical space of approximately 6,500 DP by considering all valid compositional combinations. Additionally, we have developed two machine learning (ML) strategies to predict thermodynamic stability (energy above the hull and Pourbaix decomposition energy) and binding Gibbs free energy, enabling an efficient exploration of the chemical space. Both models, trained with minimal calculations, achieve high accuracies even with unrelaxed structures (mean absolute error (MAE) 0.028 and 0.031 eV/atom for Pourbaix stability and energy above hull, respectively, and MAE 0.124 and 0.129 eV for O* and OH* binding Gibbs free energy, respectively). By utilizing the descriptor-based ML models and graph-based pretrained universal ML potential, we evaluated the stability of approximately 3,500 bulk structures and the activity of around 14,000 surfaces, respectively. This ML-HTS approach led to the successful and efficient identification of 15 novel DPs that are predicted to be more active and stable than established DPs such as LaSrCoFeO6 (LSCF).