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Record W7058571062

Nanoscale mechanisms of hydrogen segregation and diffusion in metals

2018· dissertation· en· W7058571062 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsHydrogen embrittlementGrain boundaryHydrogenMicrostructureKinetic Monte CarloEnergeticsDiffusionWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Hydrogen embrittlement (HE), a phenomenon known as hydrogen deteriorating mechanical properties of structural metals to cause premature failure, continues to haunt the industry in the design and application of structural metals since its first discovery more than one century ago.Despite continuous research effort on HE, there remains no consensus on the exact mechanism underlying the occurrence of HE.However, it is widely recognized that HE is microstructure-sensitive, namely microstructural heterogeneities play a vital role in determining the material's susceptibility to HE.This thesis represents a body of research studies dedicated to understanding the key mechanistic aspects underlying the interplay between microstructures and hydrogen in structural metals, in order to provide insights towards developing effective microstructure engineering routes to moderate or prevent HE.Focusing on two main categories of microstructural entities in structural metals, dislocations and grain boundaries, the thesis systematically investigated the interactions between hydrogen and microstructures at the atomic scale, employing comprehensive molecular dynamics simulations, first principles calculations and Kinetic Monte Carlo simulations.The simulation results were then interpreted in the framework of continuum mechanics to develop physics-based predictive models to reveal the structure-property relationships underlying energetics and kinetic behaviors of hydrogen at dislocations and grain boundaries.This thesis is in the manuscript-based format, composed of four articles shown in Chapter 4-7, including predictive assessments of hydrogen energetics and segregation at grain boundaries ont été proposés dans le but d'évaluer l'énergie et la cinétique de l'hydrogène dans les joints de grain et les dislocations.Cette thèse est présentée sous forme de manuscrit composé de quatre articles introduits dans les chapitres 4 à 7. Dans le chapitre 4, le spectre d'énergie de l'hydrogène demeurant à différents joints de grains examinés fut déterminé.Le chapitre 5 s'intéresse à la cinétique de l'hydrogène aux joints de grains.Dans le chapitre 6, le rôle de l'hydrogène sur la nucléation des dislocations est expliqué, et le chapitre 7 traite sur le piégeage et la diffusion de l'hydrogène dans les dislocations.Ces articles ont non seulement fourni une compréhension de l'énergie et de la cinétique de l'hydrogène dans les hétérogénéités microstructurales, mais ont également approfondi la compréhension mécanistique de la fragilisation de l'hydrogène.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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