2022
DOI: 10.1016/j.intermet.2022.107590
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The effect of 10 at.% Al addition on the hydrogen storage properties of the Ti0.33V0.33Nb0.33 multi-principal element alloy

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Cited by 28 publications
(53 citation statements)
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“…The Mg 0.10 Ti 0.30 V 0.25 Zr 0.10 Nb 0.25 and Al 0.10 Ti 0.30 V 0.25 Zr 0.10 Nb 0.25 alloys occurred as bcc single phases with a maximum capacity of 1.7 H/M (2.7 wt.%) [15] and 1.6 H/M (2.6 wt.%), respectively [16]. Furthermore, in our previous investigations, we demonstrated that the addition of 10 at.% of Al into the ternary bcc TiVNb alloy decreased the storage capacity from 2.0 H/M (3.2 wt.% for TiVNb) to 1.59 H/M (2.6 wt.% for Al 0.10 (TiVNb) 0.90 ) [17]. Despite the observed decrease in capacity, other aspects of performance were positively affected by the presence of Al.…”
Section: Introductionmentioning
confidence: 69%
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“…The Mg 0.10 Ti 0.30 V 0.25 Zr 0.10 Nb 0.25 and Al 0.10 Ti 0.30 V 0.25 Zr 0.10 Nb 0.25 alloys occurred as bcc single phases with a maximum capacity of 1.7 H/M (2.7 wt.%) [15] and 1.6 H/M (2.6 wt.%), respectively [16]. Furthermore, in our previous investigations, we demonstrated that the addition of 10 at.% of Al into the ternary bcc TiVNb alloy decreased the storage capacity from 2.0 H/M (3.2 wt.% for TiVNb) to 1.59 H/M (2.6 wt.% for Al 0.10 (TiVNb) 0.90 ) [17]. Despite the observed decrease in capacity, other aspects of performance were positively affected by the presence of Al.…”
Section: Introductionmentioning
confidence: 69%
“…For this purpose, laboratory and large-scale experimental facilities were combined to clarify the effect of the amount of Al introduced into TiVNb. Well-known laboratory techniques, as well as ex situ and in situ synchrotron X-ray powder diffraction, were carried out and the results compared to our previous results for ternary TiVNb and quaternary Al 0.10 (TiVNb) 0.90 alloys [17]. The thermodynamic experimental results obtained were compared with data-driven predictions facilitated by a previously published machine/statistical learning (ML) model [17].…”
Section: Introductionmentioning
confidence: 99%
“…24 Machine learning. Our models are trained on the thermodynamic data contained in v0.0.4 of the ML-HydPARK database, 38 which includes recent literature data on high entropy alloy hydrides 24,28,30,33,35,39 and metal hydrides for compression. 15,50 The Magpie strategy, as implemented in Matminer, 51 was employed for materials featurization.…”
Section: Density Functional Theory Calculationsmentioning
confidence: 99%
“…28 The combinatorial explosion of compositional design space in equimolar HEAs alone is simply too large for brute-force experimental search 29 or modeling with compute-intensive methods like density functional theory (DFT). While experimental validation of ML-predicted hydride thermodynamics has indeed been demonstrated, 24,30 and the space of experimentally investigated HEA hydrides is ever growing, [31][32][33][34][35][36][37] only a small fraction of possible compositional space has been explored. Furthermore, a perspective on the possible upper bounds of HEA hydride performance remains obtuse.…”
Section: Introductionmentioning
confidence: 99%
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