Mechanical and Thermal Properties of Epoxy Resin upon Addition of Low-Viscosity Modifier
Bibliographic record
Abstract
Thermoset-based polymer composites containing functional fillers are promising materials for a variety of applications, such as in the aerospace and medical fields. However, the resin viscosity is often unsuitably high and thus impedes a successful filler dispersion in the matrix. This challenge can be overcome by incorporating suitable low-viscosity modifiers into the prepolymer. While modifiers can aptly influence the prepolymer rheology, they can also affect the prepolymer curing behavior and the mechanical and thermal properties of the resulting matrix material. Therefore, this study investigates the effects that a commercial-grade low-viscosity additive (butyl glycidyl ether) has on a common epoxy polymer system (diglycidyl ether of bisphenol-A epoxy with a methylene dianiline curative). The weight percentage of the modifier inside the epoxy was varied from 0 to 20%. The rheological properties and cure kinetics of the resulting materials were investigated. The prepolymer viscosity decreased by 97% with 20 wt% modifier content at room temperature. Upon curing, 20 wt% modifier addition reduced the exothermic peak temperature by 12% and prolonged the time to reach the peak by 60%. For cured material samples, physical and thermo-mechanical properties were characterized. A moderate reduction in glass transition temperature and an increase in elastic modulus was observed with 20 wt% modifier content (in the order of 10%). Based on these findings, the selected material system is seen as an expedient base for material design due to the ease of processing and material availability. The present study thus provides guidance to researchers developing polymer composites requiring reduced prepolymer viscosity for successful functional filler addition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".