MétaCan
Menu
Back to cohort
Record W2564555237 · doi:10.1615/atomizspr.2016016052

COALESCENCE AND AGGLOMERATION OF DROPLETS SPRAYED ON A SUBSTRATE

2016· article· en· W2564555237 on OpenAlex

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.

Bibliographic record

VenueAtomization and Sprays · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoalescence (physics)Economies of agglomerationMaterials scienceSubstrate (aquarium)BusinessNanotechnologyEconomic geographyMechanicsChemical engineeringEconomicsPhysicsGeologyAstrobiologyEngineering

Abstract

fetched live from OpenAlex

The coalescence of droplets of a highly viscous liquid (87 wt% glycerin in water) sprayed onto a solid surface was studied. Experiments were done on the merger of two droplets deposited sequentially on a flat surface and also on liquid droplets sprayed onto a surface. Two unequal sized droplets were deposited on a steel plate, with the smaller one overlapping the larger. The different curvatures of the two droplets produce different capillary pressures in them, driving them to merge. The smaller droplet was pulled into the larger one; the greater the difference in size between the droplets, and the larger their separation, the more rapid the droplet motion. Experiments were also done in which liquid was sprayed onto a transparent surface and the motion of the impacting droplets photographed from below. Measurements of the wetted surface area were done using image analysis. The wetted area first increased as liquid was deposited. Once the spray was turned off the area decreased as smaller droplets merged with larger ones. Droplet coalescence prevented the formation of a uniform film and liquid accumulated in separate patches.

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)0.000
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 score0.646
Threshold uncertainty score0.177

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 head0.168
Teacher spread0.163 · 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