Curing Kinetics Modeling of Epoxy Modified by Fully Vulcanized Elastomer Nanoparticles Using Rheometry Method
Bibliographic record
Abstract
In this study, the curing kinetics of epoxy nanocomposites containing ultra-fine full-vulcanized acrylonitrile butadiene rubber nanoparticles (UFNBRP) at different concentrations of 0, 0.5, 1 and 1.5 wt.% was investigated. In addition, the effect of curing temperatures was studied based on the rheological method under isothermal conditions. The epoxy resin/UFNBRP nanocomposites were characterized via Fourier transform infrared spectroscopy (FTIR). FTIR analysis exhibited the successful preparation of epoxy resin/UFNBRP, due to the existence of the UFNBRP characteristic peaks in the final product spectrum. The morphological structure of the epoxy resin/UFNBRP nanocomposites was investigated by both field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) studies. The FESEM and TEM studies showed UFNBRP had a spherical structure and was well dispersed in epoxy resin. The chemorheological analysis showed that due to the interactions between UFNBRP and epoxy resin, by increasing UFNBRP concentration at a constant temperature (65, 70 and 75 °C), the curing rate decreases at the gel point. Furthermore, both the curing kinetics modeling and chemorheological analysis demonstrated that the incorporation of 0.5% UFNBRP in epoxy resin matrix reduces the activation energy. The curing kinetic of epoxy resin/UFNBRP nanocomposite was best fitted with the Sestak-Berggren autocatalytic model.
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 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) | 0.001 | 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".