Atomic Layer Deposition of PdRux Alloy Nanoparticles for Hydrogen Evolution Reaction Electrocatalysis in Acidic Media
Source abstract
Abstract
Palladium (Pd) metal is considered a promising alternative to platinum (Pt) due to its similar hydrogen adsorption Gibbs free energy (Δ G H ). Alloying is a viable strategy to mitigate the suboptimal hydrogen evolution reaction (HER) electrochemical performance associated with the excessively strong Pd–H bonding interaction. This study focuses on synthesizing PdRu x alloy nanoparticles on carbon cloth using a combination of atomic layer deposition (ALD) and annealing. By adjusting the number of Ru cycles from 100 to 500 on 200-cycle Pd on carbon cloth, a series of PdRu bimetallic nanoparticle samples were obtained by ALD. The subsequent annealing process was conducted at 600 °C in an argon atmosphere to facilitate the formation of PdRu x alloy phases. The density functional theory (DFT) calculation results indicate that some surface configurations of the alloyed PdRu x electrocatalysts are better than those of the single metal Ru or Pd, conducive to HER performance. Among them, the annealed 200-cycle Pd-400-cycle Ru alloy electrocatalyst exhibits the best electrocatalytic performance, with an overpotential of 35.6 mV at 10 mA cm –2 . Simultaneously, these PdRu x alloy nanoparticles show excellent long-term stability with a 40 h chronopotentiometry test at 10 mA cm –2 in acidic media. The enhanced catalytic performance is attributed to the nanoscale size effect of the PdRu x alloy nanoparticles and the optimized electronic interaction between alloy components. These synergistic effects lead to a significant reduction in hydrogen binding energy (HBE), thereby substantially improving the HER activity. This work provides enormous potential for the development of highly efficient alloy electrocatalysts by the ALD method.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Electrocatalysts for Energy Conversion · Catalytic Processes in Materials Science · Machine Learning in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Laboratory of Solid State Microstructure, Materials Science & Engineering Department, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Key Laboratory of Artificial Functional Materials
Nanjing University
Jiangsu Leadmicro Nano Technology Co., Ltd.
Funding
Transformation Program of Scientific and Technological Achievements of Jiangsu Province · Natural Science Foundation of Jiangsu Province · National Natural Science Foundation of China
References (53)
Show source-provided references
1. 10.1016/j.jclepro.2019.01.30910.1016/j.jclepro.2019.01.309 ↗
2. 10.1007/s10311-021-01322-810.1007/s10311-021-01322-8 ↗
3. 10.1016/j.apenergy.2021.11814510.1016/j.apenergy.2021.118145 ↗
4. 10.1021/acsomega.2c0399610.1021/acsomega.2c03996 ↗
5. 10.1016/j.ijhydene.2024.11.11010.1016/j.ijhydene.2024.11.110 ↗
6. 10.1039/D0CS00013B10.1039/D0CS00013B ↗
7. 10.1016/j.ijhydene.2019.05.18310.1016/j.ijhydene.2019.05.183 ↗
8. 10.1149/1.185698810.1149/1.1856988 ↗
9. 10.1016/S0013-4686(00)00523-510.1016/S0013-4686(00)00523-5 ↗
10. 10.1039/C3CS60468C10.1039/C3CS60468C ↗
11. 10.1002/ece2.1810.1002/ece2.18 ↗
12. 10.1021/acscatal.3c0505310.1021/acscatal.3c05053 ↗
13. 10.1002/adma.20231101810.1002/adma.202311018 ↗
14. 10.1002/smll.20230146510.1002/smll.202301465 ↗
15. 10.1016/j.apcatb.2022.12214510.1016/j.apcatb.2022.122145 ↗
16. 10.1016/j.nanoen.2022.10746710.1016/j.nanoen.2022.107467 ↗
17. 10.1126/sciadv.160054210.1126/sciadv.1600542 ↗
18. 10.1002/ece2.1910.1002/ece2.19 ↗
19. 10.1039/D3TA02238B10.1039/D3TA02238B ↗
20. 10.1039/c2cs35160a10.1039/c2cs35160a ↗
21. 10.1039/C7TA05314B10.1039/C7TA05314B ↗
22. 10.1016/j.ijhydene.2021.06.07510.1016/j.ijhydene.2021.06.075 ↗
23. 10.1016/j.ccr.2021.21395310.1016/j.ccr.2021.213953 ↗
24. 10.1016/j.jelechem.2015.07.04410.1016/j.jelechem.2015.07.044 ↗
25. 10.1002/smll.20230303110.1002/smll.202303031 ↗
26. 10.1021/jacs.1c0057810.1021/jacs.1c00578 ↗
27. 10.1016/j.apcatb.2021.12067010.1016/j.apcatb.2021.120670 ↗
28. 10.1016/j.catcom.2019.10590010.1016/j.catcom.2019.105900 ↗
29. 10.1007/s10562-019-02754-210.1007/s10562-019-02754-2 ↗
30. 10.1016/j.cej.2016.01.09310.1016/j.cej.2016.01.093 ↗
31. 10.1016/j.scitotenv.2018.09.20010.1016/j.scitotenv.2018.09.200 ↗
32. 10.1016/S1872-2067(19)63440-610.1016/S1872-2067(19)63440-6 ↗
33. 10.1038/s41929-019-0283-x10.1038/s41929-019-0283-x ↗
34. 10.1002/aenm.20190088910.1002/aenm.201900889 ↗
35. 10.1088/2631-7990/acd88e10.1088/2631-7990/acd88e ↗
36. 10.1021/accountsmr.1c0025010.1021/accountsmr.1c00250 ↗
37. 10.1016/j.apcatb.2017.04.02210.1016/j.apcatb.2017.04.022 ↗
38. 10.1016/j.apcatb.2010.04.00310.1016/j.apcatb.2010.04.003 ↗
39. 10.1021/acscentsci.9b0111010.1021/acscentsci.9b01110 ↗
40. 10.1021/acscatal.5b0003710.1021/acscatal.5b00037 ↗
41. 10.1038/ncomms426410.1038/ncomms4264 ↗
42. 10.1093/nsr/nwy05410.1093/nsr/nwy054 ↗
43. 10.1524/zkri.220.5.567.6507510.1524/zkri.220.5.567.65075 ↗
44. 10.1088/0953-8984/14/11/30110.1088/0953-8984/14/11/301 ↗
45. 10.1103/PhysRevB.74.03510110.1103/PhysRevB.74.035101 ↗
46. 10.1016/0021-9517(85)90360-410.1016/0021-9517(85)90360-4 ↗
47. 10.1021/acssuschemeng.9b0132710.1021/acssuschemeng.9b01327 ↗
48. 10.1002/smll.20240578410.1002/smll.202405784 ↗
49. 10.1016/j.apcatb.2023.12367710.1016/j.apcatb.2023.123677 ↗
50. 10.1016/j.jallcom.2024.17393210.1016/j.jallcom.2024.173932 ↗
51. 10.1021/acs.inorgchem.4c0259110.1021/acs.inorgchem.4c02591 ↗
52. 10.1088/0957-4484/19/04/04530210.1088/0957-4484/19/04/045302 ↗
53. 10.1016/j.surfrep.2016.03.00310.1016/j.surfrep.2016.03.003 ↗