Incorporation of inorganic and organic nanoparticles within moisture curable polyhydroxyurethane matrices
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".