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Record W6982414192

Incorporation of inorganic and organic nanoparticles within moisture curable polyhydroxyurethane matrices

2025· dissertation· en· W6982414192 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsMoistureNanoparticleDecompositionDeposition (geology)Water content
DOInot available

Abstract

fetched live from OpenAlex

In 2020, polyurethanes (PUs) ranked as the 6th most produced plastic worldwide.While PUs are used in a variety of applications, such as coatings, adhesives, sealants, and foams, their production typically involves diisocyanates-compounds that are carcinogenic, mutagenic, and reprotoxic.Moreover, diisocyanate synthesis often requires phosgene gas, a highly toxic compound.Due to these concerns, there has been growing demand for non-isocyanate polyurethanes (NIPUs) as safer alternatives.One promising NIPU pathway involves producing polyhydroxyurethanes (PHUs) through the polyaddition reaction of polycyclic carbonates and polyamines.Unfortunately, PHUs are often hindered by weaker mechanical properties, which limits their broader application.This study aims to investigate how the addition of organic and inorganic fillers impacts the mechanical properties of a moisture-curing PHU matrix.Amino modified silica was chosen as an organic filler capable of covalently bonding to the matrix and compared to a non-functional fumed silica.To begin, a PHU prepolymer was synthesized via the polyaddition of poly(propylene glycol) terminated with amino groups (Jeffamine-D2000) and diglycerol dicarbonate.The prepolymer was then characterized using NMR, GPC, FTIR, DSC, and TGA.Crosslinking was accomplished after dissolution in ethyl acetate and addition of a moisture-curing agent ([3-(2,3-epoxypropoxy)propyl]-trimethoxysilane or N-(2-aminoethyl)-3-aminopropyltrimethoxysilane), a catalyst (triethylamine), and one of the fillers.Baseline samples without any fillers were also synthesized for comparative analysis.The fillers were added at weight fractions of 1 %, 3.5 %, and 6 % relative to the matrix.To evaluate the extent of cross-linking, gel content and swelling tests were conducted in water, tetrahydrofuran, and toluene.Gel contents ranged between 76.7 % and 98.5 %, while swelling ranged from 4.9 % to 8.2 % for samples submerged in water and 60.3 % to 69.7 % in organic solvents.According to the nanoindentation and tensile results, the addition of a filler substantially increased the hardness, the elastic modulus, Young's modulus (0.0280 MPa), the ultimate stress (0.1175 MPa), and the ultimate strain (13.18 %) compared to the bare hybrid PHU.Indeed, the mechanical properties of the non-filled sample were so poor that tensile tests could not be performed.Among the samples, the 3.5 wt% fumed silica formulation exhibited the best performance in nanoindentation tests with a hardness of 2.010 MPa and a modulus of 5.067 MPa.

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 score0.005
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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
Published2025
Admission routes1
Has abstractyes

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