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Record W4391401398 · doi:10.1080/21663831.2024.2309271

800 <sup>o</sup> C-stable D0 <sub>22</sub> superlattice in a NiCrFe-based medium entropy alloy

2024· article· en· W4391401398 on OpenAlexfundno aff
Mei Ai Lin, Jianlin Lu, Yiming Chen, Zhongsheng Yang, Xin Liu, Kaiwei Zhang, Zhijun Wang, Junjie Li, Jincheng Wang, Feng He

Bibliographic record

VenueMaterials Research Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsnot available
FundersEnvironmental Studies Research FundsNorthwestern Polytechnical UniversityNational Natural Science Foundation of ChinaShanghai Science and Technology Development Foundation
KeywordsSuperlatticeMaterials scienceMetastabilityAlloyThermal stabilityThermal diffusivityCondensed matter physicsPhase diagramCrystallographyPhase (matter)ThermodynamicsMetallurgyOptoelectronicsChemical engineeringChemistry

Abstract

fetched live from OpenAlex

The metastable-stable transition and rapid coarsening of metastable D022 phase remains its Achilles hell, restricting the application of D022-strengthened alloys at service temperatures higher than 650°C. Hence, it is crucial to improve thermal stability of D022 phase. In this work, we report an 800°C-stable D022-Ni3(W0.24Ti0.38Ta0.19Nb0.19) superlattice in the Ni58Cr23Fe10W5Ti2Ta1Nb1 MEA. Experimental results and theoretical analysis showed that the exceptional thermal stability is due to Nb-sublattice occupying elements, low diffusivity of W, and extremely small lattice misfit between the D022 phase and FCC matrix. These findings shed lights on developing high-performance D022-hardened alloys capable of operating at temperatures above 650°C.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.023
GPT teacher head0.274
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2024
Admission routes1
Has abstractyes

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