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Record W3043285443 · doi:10.1080/08927022.2020.1788217

Mechanical properties of nanocrystalline aluminium: a molecular dynamics investigation

2020· article· en· W3043285443 on OpenAlexaff
Sabir Subedi, Stephen M. Handrigan, Liam S. Morrissey, Sam Nakhla

Bibliographic record

VenueMolecular Simulation · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNanocrystalline materialMaterials scienceGrain sizeAluminiumGrain boundaryComposite materialStackingDeformation (meteorology)Molecular dynamicsGrain boundary strengtheningDeformation mechanismMetallurgyMicrostructureNanotechnologyChemistryComputational chemistry

Abstract

fetched live from OpenAlex

Uniaxial deformation was performed using molecular dynamics to estimate the mechanical properties of nanocrystalline aluminium. It was observed that the stacking faults and sliding of the grain boundaries affected the mechanical properties. In addition, accumulation of atoms near grain boundaries during deformation hardened the nanocrystalline material as the grain diameter increased (reverse Hall-Petch relation). Further, the effects of strain rate and temperature were investigated with various mean grain diameters. Investigation showed that mechanical properties were independent of tested strain rates (109–1010 s−1) and that the nanocrystalline material softened with increasing temperature. The elastic modulus was then compared to experimental results from literature at room temperature. The change in crystalline structure was observed with respect to percent strain and various mean grain diameters of nanocrystalline aluminium. It was observed that stacking faults increased with decreased mean grain diameter, which led to reduced mechanical properties.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.227
Teacher spread0.202 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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