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Elucidating Primary Degradation Mechanisms in High-Cycling-Capacity, Compositionally Tunable High-Entropy Hydrides.

Renato Belli StroziMatthew D WitmanVitalie StavilaJakub CizekKouji SakakiHyunjeong KimOksana MelikhovaLoïc PerrièreAkihiko MachidaYuki NakahiraGuilherme ZeponWalter José BottaClaudia Zlotea
Published in: ACS applied materials & interfaces (2023)
The hydrogen sorption properties of single-phase bcc (TiVNb) 100- x Cr x alloys ( x = 0-35) are reported. All alloys absorb hydrogen quickly at 25 °C, forming fcc hydrides with storage capacity depending on the Cr content. A thermodynamic destabilization of the fcc hydride is observed with increasing Cr concentration, which agrees well with previous compositional machine learning models for metal hydride thermodynamics. The steric effect or repulsive interactions between Cr-H might be responsible for this behavior. The cycling performances of the TiVNbCr alloy show an initial decrease in capacity, which cannot be explained by a structural change. Pair distribution function analysis of the total X-ray scattering on the first and last cycled hydrides demonstrated an average random fcc structure without lattice distortion at short-range order. If the as-cast alloy contains a very low density of defects, the first hydrogen absorption introduces dislocations and vacancies that cumulate into small vacancy clusters, as revealed by positron annihilation spectroscopy. Finally, the main reason for the capacity drop seems to be due to dislocations formed during cycling, while the presence of vacancy clusters might be related to the lattice relaxation. Having identified the major contribution to the capacity loss, compositional modifications to the TiVNbCr system can now be explored that minimize defect formation and maximize material cycling performance.
Keyphrases
  • machine learning
  • high intensity
  • high resolution
  • single molecule
  • magnetic resonance imaging
  • mass spectrometry
  • deep learning
  • solid state
  • energy transfer