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Record W4221073609 · doi:10.1088/2752-5724/ac5e0c

Properties and processing technologies of high-entropy alloys

2022· article· en· W4221073609 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Futures · 2022
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Toronto
FundersBasic and Applied Basic Research Foundation of Guangdong Province
KeywordsHigh entropy alloysSalientMaterials scienceFabricationDuctility (Earth science)NanotechnologyComputer scienceMetallurgyMicrostructureArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract High-entropy alloys (HEAs) are emerging materials that are developed based on entropy, and draw significant attention for the potential to design their chemical disorder to bring out different structural and physical characteristics. Over the past two decades, significant salient efforts have been conducted to explore many unique and useful properties of HEAs, such as overcoming the strength–ductility trade-off, outstanding thermal stability, and excellent low temperature plasticity. Here, we review the key research topic of HEAs in the following three aspects: (a) performance advantages and composition design, (b) performance-driven HEAs and (c) fabrication process-driven HEAs. Towards their industrial applications, our article reviews a large range of methods to synthesise, fabricate and process HEAs. We also discuss the current challenges and future opportunities, mainly focusing on performance breakthroughs in HEAs.

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.

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)0.000
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 score0.006
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.182
Teacher spread0.172 · 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