MgH2 (magnesium hydride) has attracted attention as a potential hydrogen storage material owing to its rich availability and high theoretical hydrogen capacity. Nevertheless, its practical utilization is restricted by its high thermodynamic stability and slow hydrogen sorption kinetics. Recent advancements have demonstrated that incorporating various catalytic systems—such as transition metals, metal oxides, metal halides, metal sulfides, and carbon-supported materials—effectively improves hydrogen dissociation, diffusion, and Mg–H bond modulation. Structural transformations, interfacial interactions, and synergistic effects in multicomponent systems substantially enhance MgH2 performance. Furthermore, cutting-edge computational techniques such as DFT (density functional theory) and ML (machine learning) have become indispensable for expediting catalyst development and forecasting their performance. These computational techniques enable high-throughput screening, provide atomic-scale insights into catalytic mechanisms, and substantially reduce experimental workloads. This review systematically summarizes recent progress in catalytic modifications of MgH2, elucidates the underlying enhancement mechanisms, highlights the contributions of DFT and ML methodologies, and discusses future directions such as nanostructuring, multifunctional composite catalysts, and computational-driven rational catalyst design for next-generation hydrogen storage technologies.
| 1. | High-entropy catalysts utilize lattice distortion and multielement synergistic effects to promote interfacial electron transfer and weaken Mg–H bonds. The multielement composition provides abundant active sites and enhances both hydrogen absorption and desorption kinetics. Moreover, the intrinsic compositional disorder helps inhibit particle growth and maintains catalyst stability during cycling. |
| 2. | SACs operate via the “single atom–defect–support” interplay. Isolated metal atoms anchored on defects serve as highly active centers for hydrogen dissociation. The unique coordination environment and electronic interactions facilitate efficient charge transfer, sharply reducing activation energies without compromising the system’s thermodynamic stability. Additionally, these catalysts offer tunable selectivity and enhanced durability by minimizing atom migration and aggregation. |
| 3. | Carbon-based matrices provide high surface area, electrical conductivity, and excellent dispersibility. The robust structural framework can host transition metal nanoparticles or single atoms, stabilizing them against sintering and aggregation. The conductive carbon matrix promotes fast electron transfer and can synergistically regulate hydrogen absorption and desorption kinetics. |
| 4. | Other types of catalysts discussed in this article, such as elemental metals, metal halides, metal sulfides, and metal carbides, have certain advantages in terms of material sources and preparation methods, and may play a role in the initial large-scale hydrogen storage applications. However, their catalytic effects are relatively limited, and we look forward to further improvements in catalytic effects and cycle performance in the future. |
| 1. | Co-ball milling MgH2 with a high-entropy precursor to construct a multiphase, defect-rich contact interface in situ. During cycling, multicomponent species with variable valence states can be partially reduced, serving as both active sites and electron and hydrogen transport channels. |
| 2. | Anchoring transition metal single atoms onto a robust support (e.g., TiO2 or fluoride nanosheets) achieves atomic-level dispersion and strong interfacial coupling, thereby promoting the H2 cracking-escape-diffusion process and inhibiting grain coarsening. |
| 3. | Utilizing the dispersibility and stability of carbon materials to load nanoscale transition metal or solid solution nanoparticles, while simultaneously modulating thermodynamics and improving kinetics, enhances their cycling stability. |
| 4. | Combining in situ characterization, DFT, and ML can reveal dynamic structural evolution processes, predict reaction energies, and identify promising components before synthesis, thereby substantially reducing experimental costs and accelerating catalyst discovery and optimization. |
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |