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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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