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Under the "dual carbon" background, the development of high-performance energy and environmental catalysis materials is of great significance for promoting energy clean transformation and environmental pollution control. The traditional research and development mode of catalysts mainly relies on experimental and trial-and-error methods, which to a large extent cannot meet the research and development needs of efficient catalysts in emerging energy and environmental fields. The rapid development of data science technologies such as machine learning is expected to bring about a paradigm shift in catalyst research and development. By using machine learning methods to quickly screen high-performance energy and environmental catalysis materials using experimental or computational data, the limitations of traditional trial-and-error methods could be overcome, and the problem of low efficiency and high cost in catalyst research and development could be solved. This article reviewed the main processes and research progress of machine learning methods in the development of energy and environmental catalysis materials from the perspective of active-sites prediction,catalysts screening, morphology design and reaction mechanism revelation, and the ML methods construction corresponding to various training data acquisition and their applications in the catalytic research. We also discussed the future direction of this method in the catalysis field, in order to provide perspective and promote its application in the energy and environmental fields.

Alkaline water electrolysis (AWE) holds considerable promise for large-scale hydrogen production. Decreasing energy input and improving efficiency are inevitable in its further commercial development. As one of the critical factors for reducing energy consumption, flow channel design on bipolar plates cannot be ignored. This study utilizes a three-dimensional model for alkaline electrolyzer that couples electrochemical reactions with mass and heat transfer to approximate the real physical process and achieve reliable convergence. Integrating diverse turbulence units tailored to different local flow demands, an advanced Partitioned Point Flow Channel (PPFC) structure is proposed aiming at optimizing electrolyte flow distribution. Results reveal that PPFC's unique combination of turbulence units promotes lateral flow and electrolyte mass transfer rate, increasing electrolyte flow uniformity and hydrogen removal efficiency by 17.5% and 24.4%, respectively. Further comparative analysis with traditional designs shows an average diaphragm temperature drop of 4 K and a current density increase of 10.7% at a cell voltage of 2 V. These results indicate that PPFC offers significant advantages in reducing energy loss in electrolyzer. • 3-D AWE model couples electrochemical reaction with mass and heat transfer is built. • Advanced PPFC integrates different turbulence units for diverse local flow demands. • PPFC improves flow uniformity by 17.5% and hydrogen removal efficiency by 24.4%. • PPFC reduces average temperature by 4 K and increases current density by 10.7%. [ABSTRACT FROM AUTHOR]

Sodium‐based rechargeable batteries are considered one of the strongest contenders for the next generation of power storage devices. Functional electrolytes with additives play a crucial role in influencing the electrochemical performance of sodium‐based batteries. The addition of small doses of additives can greatly enhance the electrolyte, improving energy density, cycling performance, and safety. This paper presents an overview of recent research focused on novel additives for sodium‐ion batteries (SIBs) and sodium‐metal batteries (SMBs). The additives are categorized based on their specific functions, including film‐forming, flame retardant, overcharge protection, high‐voltage, acid and water removal, inhibition of gas production, high and low temperature and protection of sodium metal anode. The working mechanisms for these additives are thoroughly explained. Finally, potential future research directions are proposed. [ABSTRACT FROM AUTHOR]

Establishing a suitable electrode microenvironment is important to achieving high-efficiency electrocatalytic CO2 reduction at industrially relevant current densities. Introducing ionomers provided an effective method for regulating the electrode microenvironment, but the mechanism of interactions between the ionomer and the catalyst remains elusive. In this work, the influence of three types of ionomers on the performance and microenvironment of Ag nanoparticle (NP) and molecularly dispersed cobalt phthalocyanine (CoPc MDE) catalysts were systematically investigated. It was found that the non-covalent interactions between the ionomers with aromatic groups and CoPc MDE resulted in the hydrophilicity of electrode and undesirably promoted the generation of hydrogen during the reaction. The optimal ionomer–catalyst combinations achieved excellent FE(CO)s > 99% at current densities up to −200 mA cm−2 and stable operation for more than 180 h at a current density of −100 mA cm−2. This work underlines the necessity of selecting appropriate ionomer according to the nature of catalyst. [ABSTRACT FROM AUTHOR]

NH 3 emissions from industrial sources and possibly future energy production constitute a threat to human health because of their toxicity and participation in PM 2.5 formation. Ammonia selective catalytic oxidation to N 2 (NH 3 -SCO) is a promising route for NH 3 emission control, but the mechanistic origin of achieving high N 2 selectivity remains elusive. Here we constructed a highly N 2 -selective CuO/TiO 2 catalyst and proposed a CuO x dimer active site based on the observation of a quadratic dependence of NH 3 -SCO reaction rate on CuO x loading, ac-STEM, and ab initio thermodynamic analysis. Combining this with the identification of a critical N 2 H 4 intermediate by in situ DRIFTS characterization, a comprehensive N 2 H 4 -mediated reaction pathway was proposed by DFT calculations. The high N 2 selectivity originated from the preference for NH 2 coupling to generate N 2 H 4 over NH 2 dehydrogenation on the CuO x dimer active site. This work could pave the way for the rational design of efficient NH 3 -SCO catalysts.