Development of novel multifunctional biobased polymer composites with tailored conductive network of micro-and-nano-fillers
Bibliographic record
Abstract
Biobased/green polymers and nanotechnology warrant a multidisciplinary approach to promote the development of the next generation of materials, products, and processes that are environmentally sustainable. The scientific challenge is to find the suitable applications, and thereby to create the demand for large scale production of biobased/green polymers that would foster sustainable development of these eco-friendly materials in contrast to their petroleum/fossil fuel derived counterparts. In this context, this research aims to investigate the synergistic effect of green materials and nanotechnology to develop a new family of multifunctional biobased polymer composites with promoted thermal conductivity. For instance, such composite can be used as a heat management material in the electronics industry. A series of parametric studies were conducted to elucidate the science behind materials behavior and their structure-toproperty relationships. Using biobased polymers (e.g., polylactic acid (PLA)) as the matrix, heat transfer networks were developed and structured by embedding hexagonal boron nitride (hBN) and graphene nanoplatelets (GNP) in the PLA matrix. The use of hybrid filler system, with optimized material formulation, was found to promote the composite’s effective thermal conductivity by 10-folded over neat PLA. This was achieved by promoting the development of an interconnected thermally conductive network through structuring hybrid fillers. The thermally conductive composite is expected to afford unique opportunities to injection mold three-dimensional, net-shape, lightweight, and eco-friendly microelectronic enclosures with superior heat dissipation performance.
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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".