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Record W2316507424 · doi:10.1063/1.4946784

Vacancy enhanced formation and phase transition of Cu-rich precipitates in <i>α</i> - iron under neutron irradiation

2016· article· en· W2316507424 on OpenAlexaff
Guocai Lv, Hao Zhang, Xiaohui He, Weiming Yang, Yanjing Su

Bibliographic record

VenueAIP Advances · 2016
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVacancy defectMaterials scienceCrystallographyMolecular dynamicsStacking faultPhase transitionPhase (matter)DislocationPrecipitationChemical physicsDiffusionCondensed matter physicsThermodynamicsChemistryComputational chemistryPhysics

Abstract

fetched live from OpenAlex

In this paper, we employed both molecular statics and molecular dynamics simulation methods to investigate the role of vacancies in the formation and phase transition of Cu-rich precipitates in α-iron. The results indicated that vacancies promoted the diffusion of Cu atoms to form Cu-rich precipitates. After Cu-rich precipitates formed, they further trapped vacancies. The supersaturated vacancy concentration in the Cu-rich precipitate induced a shear strain, which triggered the phase transition from bcc to fcc structure by transforming the initial bcc (110) plane into fcc (111) plane. In addition, the formation of the fcc-twin structure and the stacking fault structure in the Cu-rich precipitates was observed in dynamics simulations.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.225

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 communication0.0000.001
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 head0.006
GPT teacher head0.228
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations3
Published2016
Admission routes1
Has abstractyes

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