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Record W2618705271 · doi:10.1139/cjp-2016-0926

Dynamic characterization of Cu–Zr binary bulk metallic glasses: A molecular dynamics study

2017· article· en· W2618705271 on OpenAlexvenueno aff
Muhammad Imran, Fayyaz Hussain, Saba Altaf, Abdul Rehman, Muhammad Arshad Javid, Muhammad Ismail, M.A. Rashid, R.M. Arif Khalil, Abdul Sattar, Shabbir Ahmad

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsnot available
Fundersnot available
KeywordsAmorphous metalMolecular dynamicsQuenching (fluorescence)Atom probeAtom (system on chip)Yield (engineering)Materials scienceCharacterization (materials science)Thermal expansionStrain rateMetalThermodynamicsAnalytical Chemistry (journal)Composite materialMicrostructurePhysicsMetallurgyNanotechnologyAlloy

Abstract

fetched live from OpenAlex

In the present study, a molecular dynamics simulation employing embedded atom method potential is performed to investigate the formation and characterization of CuZr bulk metallic glasses (BMGs). To elucidate the effect of component concentration of three samples of BMGs including Cu25Zr75, Cu50Zr50, and Cu75Zr25 that are formed by melt quenching. The local structure of BMGs is analyzed by means of radial distribution function and local atomic number density, ρ. The mechanical behavior of three compositions is investigated using uniaxial compressive loading at a constant strain rate. It is revealed from the results that yield strength increases with increasing Cu concentration. Thermal expansion of CuZr BMGs is examined and variation in length and volume is measured. The analysis revealed that Cu25Zr75, and Cu50Zr50 exhibited the typical expansion behavior while Cu75Zr25 showed an anomalous behavior.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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