Effect of carbon nanoparticle addition on epoxy cure
Bibliographic record
Abstract
The thesis reports studies of cure kinetics and the glass transition temperature advancements of three commercial epoxy resin systems: MY 750 / HY 5922 (Vantico), MTM 44 -1 (ACG) and 8552 (Hexcel Composites). This investigation was conducted with the utilisation of Differential Scanning Calorimetry (DSC) and Temperature Modulated DSC (TMDSC). Appropriate phenomenological cure kinetics models were built to predict the degree of cure as a function of temperature/time profile. The validity of superposition of dynamic and isothermal experimental data was established. Rheological measurements were performed in order to determine the gelation region under given cure conditions. The cure modelling methodology was validated against an international Round-Robin exercise led by the University of British Columbia (Canada). The effects of carbon nanoparticle incorporation on the cure kinetics and the glass transition temperature advancement of two of the epoxy systems were also studied. Cure kinetics models were developed for the nanocomposites containing commercial multiwalled carbon nanotubes and a direct comparison was made with the models of the neat resin systems. The glass transition temperature advancement is shown to be affected in the early stages of the cure. The state of the dispersion of the nanoparticles was studied in order to correlate it with the observed effects upon the cure and on the morphology of the cured samples. The presence of carbon nanotube clusters is shown to have an influence on the phase separation in the MTM 44-1 resin system. As a potential industrial application of this study, optical fibre refractometers were utilised as an on-line cure monitoring technique. A good correlation was established between the measured refractive index changes during the cure and the degree of cure predicted by the above mentioned models, for the neat resin systems and their nanocomposites.
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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 | 0.001 |
| 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".