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Record W4294719266 · doi:10.1016/j.nocx.2022.100117

Space-time rigidity and viscoelasticity of glass forming liquids: The case of chalcogenides

2022· article· en· W4294719266 on OpenAlexafffund
Hugo M. Flores‐Ruiz, J. Quetzalcóatl Toledo-Marín, Cristian F. Moukarzel, Gerardo G. Naumis

Bibliographic record

VenueJournal of Non-Crystalline Solids X · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Dynamics and Properties
Canadian institutionsBritish Columbia Children's Hospital
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoMitacsConsejo Nacional de Ciencia y Tecnología
KeywordsViscoelasticitySupercoolingRigidity (electromagnetism)Glass transitionConfiguration entropyEntropy (arrow of time)RheologyMaterials scienceChalcogenideRelaxation (psychology)ThermodynamicsStatistical physicsCondensed matter physicsPhysicsComposite material

Abstract

fetched live from OpenAlex

The viscoelasticity of glass-forming fluids contains sustantial information about space-time rigidity. Viscoelasticity and rheology provide alternative experimental, computational and theoretical ways to asses chemical composition effects in the relaxation of supercooled liquids near the glass transition. In particular, the transverse current correlation and transversal dynamical structure factor contain space-time information allowing to relate the dynamical gap of transversal vibrational modes with floppy modes and relaxation times in the liquid. Here, a short revision is made of the subject, including simulations of Tellurium, a typical chalcogenide glass. Our results are similar to those obtained for typical metallic liquids. To rationalize this result, an statistical mechanics analysis in the strain ensemble is performed by using a model that incorporates flexibility and hard-core potentials. This shows that the entropy is akin to a hard-cord fluid as angular bonds only renormalize the entropy if they are not substantially affected by temperature effects. Finally, a comparison is made with Selenium, where bond breaking effects do not allow such a straight-forward treatment.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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 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

Citations3
Published2022
Admission routes2
Has abstractyes

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