Synergistic enhancement of creep resistance and thermal stability in epoxy nanocomposites reinforced with graphene nanoplatelets and halloysite nanotubes for optoelectronic applications
Bibliographic record
Abstract
Abstract Epoxies used in optoelectronic applications are prone to creep due to low‐magnitude stresses caused by uneven thermal expansion, residual stresses, or component weight in elevated temperature environments. To address this, the effect of adding graphene nanoplatelets (GNP, 0.1–1 wt.%) and halloysite nanotubes (HNT, 1–10 wt.%) on the creep behavior of epoxy nanocomposites was investigated through 1‐h creep tests at 60°C under a 5 MPa load. Results showed that adding 0.1 wt.% GNP and 10 wt.% HNT led to significant reductions in strain rate, by 78.2% and 77.4%, respectively. A synergistic effect was observed when both nanoparticles were combined, improving dispersion and overall performance. The hybrid nanocomposite containing 1 wt.% HNT and 0.25 wt.% GNP demonstrated the most balanced improvement, with a 68.5% reduction in strain rate and enhanced dispersion. Characterization of the particle distribution using optical microscopy and SEM revealed that GNP particles tended to agglomerate more than HNT, while higher HNT loadings caused particle clustering and settling. The hybrid nanocomposite effectively mitigated these issues, making it a promising candidate for high‐temperature, high‐performance applications in optoelectronics, where improved mechanical and thermal stability are essential. Highlights 0.1 wt.% GNP and 10 wt.% HNT reduce the strain rate by 78.2% and 77.4%, respectively. Synergistic effect of 0.25 wt.% GNP and 1 wt.% HNT enhances epoxy creep resistance by 68.5%. GNP and HNT enhance creep strain by reinforcing the polymer network and load transfer. Optimized epoxy nanocomposite is ideal for high‐temperature optoelectronic applications.
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".