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An investigation of rapid surface melting in nanowires

2024· article· en· W4403668168 on OpenAlexaff
Benhour Amirian, Kaan Inal

Bibliographic record

VenueInternational Journal of Solids and Structures · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNanowireMaterials scienceSurface (topology)Composite materialNanotechnologyGeometryMathematics

Abstract

fetched live from OpenAlex

The investigation into virtual melting phenomena in nanowires holds significant relevance owing to its profound impact on material durability under extreme loading conditions. Thus, the exploration of this pivotal plastic deformation mechanism is undertaken utilizing the phase-field methodology. Employing a monolithic solver, we solve the coupled highly nonlinear time-dependent Ginzburg–Landau equation and dynamic elasticity equation. Our analysis encompasses the consideration of surface tension stress in conjunction with a coherent solid–liquid interface subjected to uniaxial transformation strain, thereby unveiling intriguing facets of melting phenomena. The investigation delves into the influence of transformation strain, kinetic coefficient, and temperature on the thickness of the solid–liquid interface and its corresponding velocity. This analysis is conducted through meticulous comparison with existing experimental data and molecular dynamics simulation. Moreover, employing the phase-field method yields precise descriptions of the system kinetics, capturing virtual melting phenomena in both pristine and flawed nanowire configurations. • Explores virtual melting in aluminum (Al) nanowires under high-pressure deviatoric stresses. • Dyhamic phase-field modeling of rapid thermomechanical melting to predict extremely large driving forces. • Developed an extensible monolithically-solved open-source finite element solver for melting under shock loading.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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