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Record W2904402989 · doi:10.1139/cjp-2018-0046

Effect of vacancies on the nucleation of Cr precipitates at grain boundary in α-Fe

2018· article· en· W2904402989 on OpenAlexvenueno aff
Yangyong Dai, Li Yang, Jinlan Nie, Shuming Peng, Xiao Long, Xiaosong Zhou, Xiaotao Zu, L. Liu, Fei Gao

Bibliographic record

VenueCanadian Journal of Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsnot available
FundersEducation Department of Sichuan ProvinceNational Natural Science Foundation of China
KeywordsNucleationVacancy defectGrain boundaryRelaxation (psychology)Condensed matter physicsChemical physicsCrystallographyPrecipitationPhysicsMolecular physicsMaterials scienceMicrostructureThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Effect of local vacancies on the nucleation of Cr precipitates at Σ3 ⟨110⟩ {112} grain boundary (GB) in α-Fe has been studied using molecular dynamics with a two-band embedded atomic model potential. Radiation-induced vacancies and Cr atoms were directly introduced into the GB core. The local vacancies affect the accumulation of Cr atoms and the evolution of the GB. It is of interest to find that high vacancy concentrations enhance the long-distance migration of Cr, which is mainly correlated to the vacancy migration mechanism, thus leading to the formation of large vacancy-diluted Cr precipitates near the GB plane. Also, the large vacancy clusters are found to be depleted by Cr atoms during relaxation. The accumulation of vacancies and nucleation of Cr precipitates at the GB lead to significant deformation of the GB structure, resulting in the displacement and broadening of the GB. Without vacancies, the GB tends to climb perpendicular to the GB axis. The current research could help in understanding the nucleation mechanism of Cr precipitates at the GB in α-Fe.

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 score0.005
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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