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Atomistic simulation of Cu–Ni precipitates hardening in<i>α</i>-iron

2015· article· en· W1981130085 on OpenAlexaff
Guocai Lv, Hao Zhang, Xinfu He, Wen Yang, Yanjing Su

Bibliographic record

VenueJournal of Physics D Applied Physics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMetallurgyCrystallographyHardening (computing)Precipitation hardeningChemistryMicrostructureNanotechnology

Abstract

fetched live from OpenAlex

In this paper, we investigated the interaction of an edge dislocation with Cu precipitates with a spherical geometry and with Cu–Ni precipitates that possess a Cu core with an outer Ni shell, commonly observed in reactor pressure vessel (RPV) steels. We applied molecular dynamics techniques to explore the critical stress required to unpin the dislocation (CSRUD), the breakaway dislocation line shape when the dislocation leaves the precipitates and the transition of Cu atoms within precipitates. The results indicate that the CSRUD of the Cu–Ni precipitates with a diameter less than 2.38 nm is larger than that of Cu precipitates that contain the same number of Cu atoms, while for a diameter larger than 2.38 nm, the CSRUD of Cu–Ni precipitates is weaker, which is related to the bcc to fcc-like or hcp-like atoms transformation in precipitates. The dislocations interact with Cu and Cu–Ni precipitates via the cut mechanism.

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 score0.239
Threshold uncertainty score0.639

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 head0.042
GPT teacher head0.296
Teacher spread0.254 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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