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Is Hydrogen Diffusion along Grain Boundaries Fast or Slow? Atomistic Origin and Mechanistic Modeling

2019· article· en· W2947827472 on OpenAlexafffund
Xiao Zhou, Normand Mousseau, Jun Song

Bibliographic record

VenuePhysical Review Letters · 2019
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversité de MontréalRegroupement Québécois sur les Matériaux de PointeMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsKinetic Monte CarloThermal diffusivityDiffusionMaterials scienceGrain boundaryChemical physicsEmbrittlementTrappingDislocationHydrogen embrittlementHydrogenMonte Carlo methodDiffusion barrierCondensed matter physicsStatistical physicsMicrostructureThermodynamicsPhysicsNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

We perform comprehensive first-principles calculations and kinetic Monte Carlo simulations to explicitly elucidate the distinct roles of grain boundaries (GBs) in affecting hydrogen (H) diffusion in fcc nickel (Ni). We demonstrate the transition between slow and fast H diffusion along the GB with an abrupt change in H diffusivity. Low-angle GBs are shown to comprise isolated high-barrier regions to trap and inhibit H diffusion, with H diffusivity well prescribed by the classical trapping model, while high-angle GBs are shown to provide interconnected low-barrier channels to facilitate H transport. On the basis of the dislocation description of the GB and the Frank-Bilby model, the slow-fast diffusion transition is identified to result from dislocation core overlapping and is accurately predicted. The present Letter provides key mechanistic insights towards interpreting various experimental studies of H diffusion in metals, new critical knowledge for predictive modeling of H embrittlement, and better understanding of the kinetics of H and other interstitial impurities in microstructures.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.027
GPT teacher head0.304
Teacher spread0.277 · 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.

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

Citations97
Published2019
Admission routes2
Has abstractyes

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