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Record W2799983299 · doi:10.1139/tcsme-2013-0073

MECHANICAL BEHAVIOR OF COPPER THIN FILMS SUBJECTED TO VARIOUS STRAIN RATE LOADINGS

2013· article· en· W2799983299 on OpenAlexvenueno aff
Kuan-Jung Chung, Chi-Feng Lin, Wei-Cheng Chiang

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2013
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialUltimate tensile strengthStrain rateCopperStrain (injury)BrittlenessSlow strain rate testingStress (linguistics)Tensile testingPolyimideModulusYoung's modulusYield (engineering)Elastic modulusMetallurgy

Abstract

fetched live from OpenAlex

In this study, a micro-force tensile testing machine (MTS Tytron 250) was applied to test the polyimide samples coated with different thicknesses of copper (500–1500 nm). The experiments using different strain rates (1.6 × 10−4 to 1.6 × 10−2 s−1) were conducted to the test vehicles. The results showed that the stress and strain of Cu films were strongly correlated with the strain rate and film thickness. The mechanical strength, yield stress, Young’s modulus, and maximum tensile stress, increase as the strain rate increases or the thickness decreases. Strain rate sensitivity rapidly increases as the thickness decreases from 750 to 500 nm to imply that the workhardening rate increases while the thickness decreases, resulting in a higher probability of brittle failure.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.225
Teacher spread0.213 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicHigh-Velocity Impact and Material BehaviorFrench-language works237,207