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Record W2101812612 · doi:10.14288/1.0040971

HYDRATE PARTICLES ADHESION FORCE MEASUREMENTS: EFFECTS OF TEMPERATURE, LOW DOSAGE INHIBITORS, AND INTERFACIAL ENERGY

2016· article· en· W2101812612 on OpenAlexaff
Craig J. Taylor, Laura E. Dieker, Kelly T. Miller, Carolyn A. Koh, E. Dendy Sloan

Bibliographic record

VenuecIRcle (University of British Columbia) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdhesionCapillary actionSurface energyMaterials scienceParticle (ecology)Capillary pressureContact angleChemistryComposite materialChemical engineering

Abstract

fetched live from OpenAlex

Micromechanical adhesion force measurements were performed on tetrahydrofuran (THF) hydrate particles in n-decane. The experiments were performed at atmospheric pressure over the temperature range 261–275 K. A scoping study characterized the effects of temperature, anti-agglomerants, and interfacial energy on the particle adhesion forces. The adhesion force between hydrate particles was found to increase with temperature and the interfacial energy of the surrounding liquid. The adhesion force of hydrates was directly proportional to the contact time and contact force. Both sorbitan monolaurate (Span20) and poly-N-vinyl caprolactam (PVCap) decreased the adhesion force between the hydrate particles. The measured forces and trends were explained by a capillary bridge between the particles.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.153
Teacher spread0.148 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

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