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Record W7132916488

The Effect of Aging Time in Dispersion on Polymer Diffusion in Films Formed From a Waterborne 2-Component Polyurethane Formulation

2023· dissertation· W7132916488 on OpenAlexaff
Margaret Zhang

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolyolPolyurethaneDispersion (optics)PolymerIsocyanateDiffusion
DOInot available

Abstract

fetched live from OpenAlex

Waterborne polyurethane coatings are environmentally friendly alternatives to their solventborne counterparts. Waterborne 2-component polyurethane coatings comprise polyol nanoparticles and polyisocyanates (PIC) and are of great interest due to their excellent mechanical properties. I used fluorescence resonance energy transfer to study polymer diffusion in films formed from a dispersion consisting of a polyol latex containing primary hydroxyl groups (Mn(GPC) ≈ 5000 g/mol, Đ ≈ 2, Tg ≈ 15 °C) and a hydrophilically modified PIC (hmPIC) (Basonate® HW1000). My contribution was to examine the effect of aging time in the dispersion before applying the coating. I found that aging dispersions of the polyol plus hmPIC up to 9 h did not reduce hmPIC effectiveness in the coating. After 9 h of aging in the dispersion, a substantial fraction of the isocyanate groups had reacted with water, and the ability of the hmPIC to promote polymer diffusion in the films was greatly reduced.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.283
Teacher spread0.274 · 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 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
Published2023
Admission routes1
Has abstractyes

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