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Record W4205587650 · doi:10.14800/iogr.1183

Dynamics of Drops as Confined Systems Containing Nanoparticles-Reformation of Nanoparticles and Evaporation of Water

2021· article· en· W4205587650 on OpenAlexaff
Vijitha Mohan, Xianjie Qiu, P. Neogi

Bibliographic record

VenueImproved Oil and Gas Recovery · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNanoparticleMaterials scienceEvaporationSinteringBrineChemical engineeringDrop (telecommunication)Contact angleTurbidityComposite materialNanotechnologyMineralogyChemistryOrganic chemistryMeteorologyGeology

Abstract

fetched live from OpenAlex

Brine is used to displace crude oil in a reservoir and its performance improves when brine contains nanoparticles. It is the presence of nanoparticles in confinements, such as at dynamic contact lines and in thin films, which is of importance. The investigators here have determined a fast way to obtain confined systems by evaporating a small drop of water containing nanoparticles. Water droplets containing nanoparticles of alumina or silica were evaporated on surfaces of polyethylene terephthalate sheets, which are partially wet by water, and glass which is fully wet. After the liquid in the drops evaporated, the residues were examined under a microscope and sintering and melting effects, crystals growth and dendritic formations for alumina and monoliths for silica were seen. The coffee stains are seen in most cases; however measurements show that the contact lines are not always pinned. Turbidity measurements showed that no significant reformation could have taken place in the bulk liquid. A simple model for evaporation, based on geometric measurements, showed that much of the film seen on the solid arose out of evaporation of water that forced the particles down. Sintering rates into the interior could be quantified and shown to be unstable.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.397

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)0.0000.000

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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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