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Record W2477935371 · doi:10.1039/c6cp03419e

Size-dependent concentrations of thermal vacancies in solid films

2016· article· en· W2477935371 on OpenAlexaff
Panpan Gao, Quan Wu, Xi Li, Hongxin Ma, Hao Zhang, Alex A. Volinsky, Lijie Qiao, Yanjing Su

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersOffice of International Science and Engineering
KeywordsVacancy defectThermalMaterials scienceThin filmChemical engineeringChemical physicsCrystallographyNanotechnologyChemistryThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Solid films are considered as typical model systems to study size effects on thermal vacancy concentration in nanomaterials. By combining the generalized Young-Laplace equation with the chemical potential of vacancies, a strict size-dependent thermodynamic model of vacancies, which includes the surface intrinsic elastic parameters of the eigenstress, Young's modulus and the geometric size of the solid films, was established. The vacancy concentration changes in the film with respect to the bulk value, depending on the geometric size and surface stress sign of the solid films. Atomistic simulations of Au and Pt films verified the developed thermodynamic model. These results provide physical insights into the size-dependent thermal vacancy concentration in nanomaterials.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.012
GPT teacher head0.239
Teacher spread0.226 · 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.

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

Citations8
Published2016
Admission routes1
Has abstractyes

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