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Record W7133064492

Droplet formation and rheology in roll coating

2006· dissertation· W7133064492 on OpenAlexfundno aff
Mohammad Pouran

Bibliographic record

VenueTSpace · 2006
Typedissertation
Language
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsBreakupRheologyCoatingNewtonian fluidProtein filamentPolymerShear thinning
DOInot available

Abstract

fetched live from OpenAlex

A laboratory device has been designed and built to simulate film splitting in roll coating, which occurs through filament stretching and breakup. At high speeds and for some coatings, the breakup leads to unwanted misting. In our simulator, a fluid sample is initially held between two closely-spaced small disks, and then the top disk is pulled upward at a high, constant rate of acceleration to mimic coating machine conditions. Formation and breakup of the resulting filament are observed using a high-speed video camera, and the images are analyzed by software to obtain the number and size of droplets. Tests with various Newtonian fluids, at a Weber number of 30, show that one droplet forms at low Ohnesorge numbers, and that more and smaller droplets form at Oh numbers above 0.1. Associative polymer solutions, prepared to be weakly-elastic fluids like coating liquids, produced even more and smaller droplets, but only for Ohnesorge numbers in the range of 0.01 to 0.1. Simplified coating formulations, which were both shear thinning and elastic, generated smaller but more droplets than those of equivalent Newtonian fluids and than those of associative polymer solutions which were less elastic. The effects of acceleration, initial film thickness and surface tension, as well as solids content of formulations, are studied as well.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
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 integrity0.0010.001
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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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
Published2006
Admission routes1
Has abstractyes

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