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Record W4308567760 · doi:10.3390/met12111910

Achieving a Combination of Higher Strength and Higher Ductility for Enhanced Wear Resistance of AlCrFeNiTi0.5 High-Entropy Alloy by Mo Addition

2022· article· en· W4308567760 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMetals · 2022
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesMitacs
KeywordsMaterials scienceDuctility (Earth science)AlloyMicrostructureHigh entropy alloysGrain boundaryPhase (matter)PrecipitationMetallurgySolid solutionComposite materialChemistryCreep

Abstract

fetched live from OpenAlex

AlCrFeNiTi0.5Mox (x = 0, 0.1, 0.2, 0.3 and 0.4) high-entropy alloys (HEAs) were prepared by arc melting and investigated in terms of microstructure, mechanical properties, and wear resistance. All the as-cast HEAs are composed of one disordered BCC phase (BCC) and one ordered BCC (B2) phase. The added Mo acted as a solid solute in the BCC phase. When Mo molar ratio was more than 0.3, a new type or modified BCC phase formed at the grain boundary, which was enriched with both Mo and Ti. Strength, hardness, and ductility of AlCrFeNiTi0.5 were markedly increased with the Mo addition. The increase in hardness was caused by Mo-solute strengthened disordered BCC phase and precipitation-strengthening by precipitation of hard (Mo, Ti)-rich BCC phase at grain boundaries. The improved ductility was largely attributed to reduced interfacial lattice mismatch between the BCC and B2 phase. The Mo-free AlCrFeNiTi0.5 showed the highest wear loss, about 2.5 times as large as that of AlCrFeNiTi0.5Mo0.4 alloy, which possessed the highest hardness, yield strength, maximum strength, and ductility.

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 score0.065
Threshold uncertainty score0.695

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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