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Molecular simulation of silica gels: Formation, dilution, and drying

2019· article· en· W2958608438 on OpenAlex
Romain Dupuis, Laurent Karim Béland, Roland J.‐M. Pellenq

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysical Review Materials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCollege of Natural Resources and Sciences, Humboldt State UniversityCompute Canada
KeywordsMaterials scienceDilutionSiliconColloidChemical engineeringComposite materialThermodynamics

Abstract

fetched live from OpenAlex

The formation and ageing of gels is a complex issue that has to be resolved to investigate manifold synthetic materials, among them: porous materials such as cement, high-quality glass fiber, and geomaterials for radioactive waste sealing. Herein, a coupling between a grand canonical Monte Carlo and the parallel tempering methods is developed. The gain in simulation time is of, at least, two orders of magnitude; therefore, we are able to move at will on the water to silicon ratio axis and to observe the restructuring of gels during dilution and drying. At high water to silicon ratio, a colloidal-like structure is obtained, mostly constituted of silicate chains. As humidity is an essential aspect of gels, affecting their physical and mechanical properties, the effect of drying is herein investigated. In agreement with experiments, the structure becomes denser, crosslinks between silicate chains increase and glasslike structures are observed locally.

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.

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)0.001
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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