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Record W2092874430 · doi:10.1038/srep05167

Interfacial Free Energy Controlling Glass-Forming Ability of Cu-Zr Alloys

2014· article· en· W2092874430 on OpenAlexafffund
Dong‐Hee Kang, Hao Zhang, Hanbyeol Yoo, Hyun Hwi Lee, Sooheyong Lee, Geun Woo Lee, Hongbo Lou, X.D. Wang, Dongxian Zhang, J.Z. Jiang

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsUniversity of Alberta
FundersMinistry of Education, Science and TechnologyKorea Research Institute of Standards and ScienceNational Natural Science Foundation of ChinaWestern Canada Research Grid
KeywordsMaterials scienceEnergy (signal processing)MetallurgyPhysics

Abstract

fetched live from OpenAlex

Glass is a freezing phase of a deeply supercooled liquid. Despite its simple definition, the origin of glass forming ability (GFA) is still ambiguous, even for binary Cu-Zr alloys. Here, we directly study the stability of the supercooled Cu-Zr liquids where we find that Cu64Zr36 at a supercooled temperature shows deeper undercoolability and longer persistence than other neighbouring compositions with an equivalent driving Gibbs free energy. This observation implies that the GFA of the Cu-Zr alloys is significantly affected by crystal-liquid interfacial free energy. In particular, the crystal-liquid interfacial free energy of Cu64Zr36 in our measurement was higher than that of other neighbouring liquids and, coincidently a molecular dynamics simulation reveals a larger glass-glass interfacial energy value at this composition, which reflects more distinct configuration difference between liquid and crystal phase. The present results demonstrate that the higher crystal-liquid interfacial free energy is a prerequisite of good GFA of the Cu-Zr alloys.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 head0.202
Teacher spread0.193 · 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

Citations37
Published2014
Admission routes2
Has abstractyes

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Same venueScientific ReportsSame topicMetallic Glasses and Amorphous AlloysFrench-language works237,207