MétaCan
Menu
Back to cohort
Record W4213339609 · doi:10.6000/1929-5030.2022.11.04

Nano-Metal Stability and its Outcomes

2022· article· en· W4213339609 on OpenAlexvenueno aff
Michael Vigdorowitsch

Bibliographic record

VenueJournal of Applied Solution Chemistry and Modeling · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
Fundersnot available
KeywordsVacancy defectSurface tensionNano-Stability (learning theory)Yield (engineering)ThermodynamicsChemistryMetalMaterials scienceChemical physicsCrystallographyPhysicsComposite materialMetallurgy

Abstract

fetched live from OpenAlex

A thermodynamics-based approach to determining stability conditions for metallic nano-ensembles is proposed. It is related to that [ultra-]dispersing the substance leads to changes in thermodynamic potentials (TPs) compared to those of a massive crystal of the same nature. This dimensional phenomenon consists of two components. Among those are a reduction of TPs due to vacancy-related effects and an increase of TPs due to the surface tension effect. Even linear, exponential, and normal distributions of particles on their size in the ensemble have been considered. The resulting equations have been applied to the nano-ensembles of either In or Au particles. The presence of ultra-small particles in an ensemble makes the vacancy-related effect more apparent than the surface tension effect, promoting system stability. With the decrease in the number of ultra-small particles, the vacancy-related effect drastically diminishes and can yield to the surface tension effect, thereby leading to the loss of stability.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
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 head0.036
GPT teacher head0.248
Teacher spread0.212 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Solution Chemistry and ModelingSame topicnanoparticles nucleation surface interactionsFrench-language works237,207