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Record W2736145107 · doi:10.1038/s41524-017-0031-1

Hydrogen embrittlement of grain boundaries in nickel: an atomistic study

2017· article· en· W2736145107 on OpenAlexaff
Shan Huang, Dengke Chen, Jun Song, David L. McDowell, Ting Zhu

Bibliographic record

Venuenpj Computational Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsMcGill University
FundersDivision of Civil, Mechanical and Manufacturing InnovationNational Science Foundation
KeywordsGrain boundaryHydrogen embrittlementHydrogenEmbrittlementMaterials scienceChemical physicsMetallurgyNickelChemistryMicrostructureCorrosion

Abstract

fetched live from OpenAlex

Abstract The chemomechanical degradation of metals by hydrogen is widely observed, but not clearly understood at the atomic scale. Here we report an atomistic study of hydrogen embrittlement of grain boundaries in nickel. All the possible interstitial hydrogen sites at a given grain boundary are identified by a powerful geometrical approach of division of grain boundary via polyhedral packing units of atoms. Hydrogen segregation energies are calculated at these interstitial sites to feed into the Rice–Wang thermodynamic theory of interfacial embrittlement. The hydrogen embrittlement effects are quantitatively evaluated in terms of the reduction of work of separation for hydrogen-segregated grain boundaries. We study both the fast and slow separation limits corresponding to grain boundary fracture at fixed hydrogen concentration and fixed hydrogen chemical potential, respectively. We further analyze the influences of local electron densities on hydrogen adsorption energies, thereby gaining insights into the physical limits of hydrogen embrittlement of grain boundaries.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.333
Teacher spread0.300 · 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 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

Citations76
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuenpj Computational MaterialsSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207