Balancing thermal conductivity, dielectric, and tribological properties in polyamide 1010 with <scp>2D</scp> nanomaterials
Bibliographic record
Abstract
Abstract Low electrical conductivity and high heat dissipation are crucial for electronic packaging materials. Additionally, friction is critical for the lifespan and energy efficiency of components. To address these requirements, polymer nanocomposites based on bio‐based polyamide 1010 and ultra‐low contents of 2D nanomaterials were produced by melt‐blending. Graphene oxide, hexagonal boron nitride, and molybdenum disulfide were selected for their two‐dimensional structure and electrical insulation, providing high thermal conductivity while preserving the polymer's dielectric nature. Hybrid nanocomposites were also produced to explore potential synergistic effects. Results showed all compositions maintained the polymer's intrinsic dielectric properties. Although the friction coefficient increased slightly compared with neat polyamide, all nanocomposites remained within the low‐friction range required for low‐friction materials. Thermal conductivity improved by 5%–10% compared with unfilled polyamide, with hybrid systems performing slightly better, indicating a minor synergistic effect. Despite these enhancements being modest compared with the literature, achieving high thermal conductivity usually requires over 20 wt% of nanofiller, which is detrimental to mechanical performance. In this study, at most 0.5 wt% was used, with composites being obtained directly through melt‐blending. This highlights their potential as low‐content additives for thermal interface materials without compromising other essential properties.
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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) | 0.001 | 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".