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Two-pulse laser-induced spall failure of (111) twist grain boundaries in Ni bicrystals

2025· article· en· W4410783098 on OpenAlexafffund
Mewael Isiet, Mauricio Ponga

Bibliographic record

VenueComputational Materials Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of British Columbia
FundersAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Foundation for Innovation
KeywordsSpallGrain boundaryMaterials scienceTwistLaserPulse (music)MetallurgyCondensed matter physicsMicrostructureOpticsGeometryPhysics

Abstract

fetched live from OpenAlex

Spall failure, a complex failure mechanism driven by tensile stress wave interactions, has been extensively studied in single-crystal FCC metals, revealing a precursor stage involving dislocation emission along closed-packed directions. Here we investigate the laser-induced spall failure of Ni bicrystals under a two-pulse laser configuration, exploring various misorientation angles through two-temperature molecular dynamics (MD) simulations including electronic effects to simulate light–matter interaction. Our findings demonstrate that light–matter interactions can induce spall failure at the sample center, similar to conventional plate-impact methods, when two laser-pulses are applied to the front and back surfaces of the sample. The study reveals the significant influence of misorientation angles on dislocation activity and spall behavior, where grain boundaries (GBs) play pivotal roles, either promoting or impeding dislocation interactions. Furthermore, our work highlights the potential for enhancing spall resistance by tailoring materials through misorientation angle variation.

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 score0.006
Threshold uncertainty score0.011

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)0.0010.000

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 spread0.233 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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