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@ARTICLE{Agafonov:641779,
author = {Agafonov, Andrei and Pineda-Romero, Nayely and Witman,
Matthew D. and Enblom, Veronica and Sahlberg, Martin and
Nassif, Vivian and Lei, Lei and Grant, David M. and
Dornheim, Martin and Ling, Sanliang and Stavila, Vitalie and
Zlotea, Claudia},
title = {{P}romising {A}lloys for {H}ydrogen {S}torage in the
{C}ompositional {S}pace of
({T}i{VN}b)$_{100–x}$({C}r,{M}o)$_x$ {H}igh {E}ntropy
{A}lloys},
journal = {ACS applied materials $\&$ interfaces},
volume = {17},
number = {29},
issn = {1944-8244},
address = {Washington, DC},
publisher = {Soc.},
reportid = {PUBDB-2025-05189},
pages = {41991 - 42003},
year = {2025},
note = {Waiting for fulltext},
abstract = {This study reports on the search for the most promising
alloys in the compositional space of (TiVNb)80Cr20–xMox (x
= 5, 10, and 15) and (TiVNb)75Cr25–xMox (x = 5, 10, 15,
and 20) high-entropy alloys. First, data-driven machine
learning applied to these systems predicts that increasing
the Mo content destabilizes the enthalpy of the hydride
phases. Second, experimental and density functional theory
(DFT) validations were performed. The as-prepared alloys
have single-phase bcc lattices and rapidly absorb hydrogen
to form fcc-type hydrides with a high capacity between 1.6
and 2.0 H/M. Despite a positive effect on the thermodynamics
of the hydride phases, increasing the Mo content in these
alloys has a negative effect on the maximum capacity. The
cycling experiments highlight the need to balance the
reversible capacity, cycle life, and crystalline stabilities
of these phases. Therefore, considering all these results,
the most promising alloy with trade-off properties within
the targeted compositional space has been identified to be
(TiVNb)75Cr5Mo20 that shows a maximum capacity of 2.6 wt
$\%$ (1.8 H/M), a reasonable enthalpy of hydride formation
(−38.6 kJ/mol H2), and a notable gravimetric reversible
capacity of 1.42 wt $\%$ at room temperature. To identify
the most promising high-entropy alloys for this application,
integrated machine learning predictions followed by
experimental and DFT validations proved to be an effective
strategy.},
cin = {DOOR ; HAS-User},
ddc = {600},
cid = {I:(DE-H253)HAS-User-20120731},
pnm = {6G3 - PETRA III (DESY) (POF4-6G3) / SWEDEN-DESY -
SWEDEN-DESY Collaboration $(2020_Join2-SWEDEN-DESY)$},
pid = {G:(DE-HGF)POF4-6G3 / $G:(DE-HGF)2020_Join2-SWEDEN-DESY$},
experiment = {EXP:(DE-H253)P-P02.1-20150101},
typ = {PUB:(DE-HGF)16},
doi = {10.1021/acsami.5c08574},
url = {https://bib-pubdb1.desy.de/record/641779},
}