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Record W4413332356 · doi:10.1016/j.jmrt.2025.08.125

A new strategy for designing Nb-based H2-selective alloys based on physical-chemical parameter matching rules

2025· article· en· W4413332356 on OpenAlexaff
Erhu Yan, Lizhen Tao, Kexiang Zhang, Hongfei Chen, Guanzhong Huang, Jinwang Bai, Yinghao Li, Kangxing Liao, Yongjin Zou, Huanzhi Zhang, Lixian Sun

Bibliographic record

VenueJournal of Materials Research and Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Natural Science Foundation of ChinaGuangxi Key Laboratory of Information Materials
KeywordsMaterials scienceMatching (statistics)StatisticsMathematics

Abstract

fetched live from OpenAlex

: The current research on Nb-based H 2 -selective alloys encompasses a wide range of compositions and a variety of systems. Designing alloys with more desirable properties, either individually or in series, remains a great challenge. To this end, a simple physical-chemical parameter matching (PCPM) rule has been established for the first time in this study to accelerate the design of Nb-based hydrogen permeable alloys. Specifically, four parameters – valence electron concentration, mixed entropy, atomic radius difference, and electronegativity difference – were calculated to determine an effective parameter selection window with outstanding permeability. Based on this, six alloys with the formula Nb 30 Hf 35 Co 30 M 5 (M = Fe, Cu, Mo, W, Al, Cr,) were arc melted to verify the newly established PCPM rules. The H 2 permeability of produced samples was further evaluated using a hydrogen permeable test instrument. It was shown that a good match between the four parameters, rather than a single valence electron concentration value, is critical for controlling performance in terms of hydrogen permeation. Additionally, the presence of an impurity phase was confirmed to further deteriorate permeability, even if the PCPM rules were met. The significance of this study lies in introducing a new strategy for screening and exploring H 2 -selective alloys to find the optimal combination of elements that provide better predictions for performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.330
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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