To address the issues of high hydrogen storage temperature, slow kinetics, and poor cycling stability of magnesium-nickel alloys, this study employed mechanical alloying combined with vacuum annealing to prepare magnesium-nickel alloys with different Nd doping concentrations (Mg₁₋ₓNdₓNi₂, x = 0, 0.05, 0.10, 0.15). Through XRD, TEM, XPS, PCT tests, and first-principles calculations, the influence of Nd doping on the hydrogen storage reaction pathway and interface interaction mechanism of the alloys was systematically investigated. The results showed that Nd doping could induce the formation of NdMgNi₄ intermetallic compound, resulting in significant lattice distortion (the lattice constant increased by up to 0.8%), and the construction of a Nd-rich interface layer; when x = 0.10, the alloy had the best hydrogen storage performance, with a hydrogen storage capacity of 3.87 wt% at room temperature, an increase of 15.2% compared to pure Mg₂Ni, and an absorption rate constant increased by 2.3 times. After 50 cycles of hydrogen absorption and release, the hydrogen storage capacity decay rate was only 6.3% (pure Mg₂Ni was 18.7%). Mechanism analysis revealed that the electron transfer between Nd, Mg, and Ni could optimize the hydrogen atom adsorption energy, the Nd-rich interface layer could reduce the hydrogen atom diffusion barrier (from 0.82 eV to 0.45 eV), and regulate the hydrogen storage reaction pathway as "physical adsorption → chemical adsorption → surface diffusion → phase diffusion → hydrogenation reaction", significantly enhancing the reaction kinetics. This study clarified the "Nd doping - interface structure - hydrogen storage performance" structure-activity relationship, providing theoretical support for the design of high-performance magnesium-based hydrogen storage materials.

Kai Deng, Jidong Li. The hydrogen storage reaction pathway and interface interaction mechanism of magnesium-nickel alloy doped with light rare earth element Nd. Academic Journal of Materials & Chemistry (2026), Vol. 7, Issue 1: 64-72. https://doi.org/10.25236/AJMC.2026.070110.

[1] Kang, Y., Zhang, K., & Lin, X. (2023). Surface modifications of magnesium-based materials for hydrogen storage and nickel–metal hydride batteries: a review. Coatings, 13(6), 1100.

[2] Huang, Y., Huang, W., Hu, X., Liu, Z., & Huo, J. (2025). UDDGN: Domain-Independent Compact Boundary Learning Method for Universal Diagnosis Domain Generation. IEEE Transactions on Instrumentation and Measurement.

[3] Song, X., Chang, M. H., & Pecht, M. (2013). Rare-earth elements in lighting and optical applications and their recycling. Jom, 65(10), 1276-1282.

[4] Li, L., Cai, M., Wang, T., Tan, Z., Huang, P., Wu, K., & Zeng, G. (2024). On-chip source-device-independent quantum random number generator. Photonics Research, 12(7), 1379-1394.

[5] Omodara, L., Pitkäaho, S., Turpeinen, E. M., Saavalainen, P., Oravisjärvi, K., & Keiski, R. L. (2019). Recycling and substitution of light rare earth elements, cerium, lanthanum, neodymium, and praseodymium from end-of-life applications-A review. Journal of Cleaner Production, 236, 117573.

[6] Tan, L., Hu, X., Tang, T., & Yuan, D. (2023). A lightweight metro tunnel water leakage identification algorithm via machine vision. Engineering Failure Analysis, 150, 107327.

[7] Miller, C. F., & Mittlefehldt, D. W. (1982). Depletion of light rare-earth elements in felsic magmas. Geology, 10(3), 129-133. 

[8] Zuo, H., Wu, J., Lim, K. H., Liao, Y., Song, L., Zhu, Y., ... & Zhang, C. (2025). A time series long-short term codec for compression and representation. Applied Intelligence, 55(17), 1-13.

[9] Wang, H., & Cheng, K. W. E. (2022, October). Conical Coil Design for Domino Wireless Power Transfer. In 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC) (pp. 1-2). IEEE.

[10] Zhang, K., Kleit, A. N., & Nieto, A. (2017). An economics strategy for criticality–Application to rare earth element Yttrium in new lighting technology and its sustainable availability. Renewable and Sustainable Energy Reviews, 77, 899-915.

[11] Chen, B., Zuo, H., Liao, Y., & Li, Z. (2024, December). A Data-Driven Analysis of Mental Health Status and Early Warning System for Vocational College Students. In 2024 5th International Conference on Information Science and Education (ICISE-IE) (pp. 312-315). IEEE.

[12] Tao, J., Qiao, Q., Song, J., Sun, S., Chen, Y., Wu, Q., ... & Zhao, F. (2025). Deep Learning-Driven Automatic Segmentation of Weeds and Crops in UAV Imagery. Sensors, 25(21), 6576.

[13] Phang, J. T. S., Lim, K. H., Lease, B. A., & Zuo, H. (2024, November). Markerless Avatar Animation with Forward Kinematics using Depth Sensor. In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 48-51). IEEE.

[14] Bu, W., Wang, R., Liu, Z., Wang, X., Hao, J., Yong, H., ... & Liang, X. (2025). Thermodynamic performance of vanadium-modified Mg2Ni alloy for hydrogen storage. Advanced Composites and Hybrid Materials, 8(5), 390.