Effect of Graphene Nanoplatelet Addition on the Conductive Behavior of Solution Mixing Processed Polylactide Biopolymer Nanocomposites
Bibliographic record
Abstract
Abstract The fabrication of highly thermally and electrically conductive polymer materials is of interest for multiple applications, for example, in electronics packaging and biosensors. Polylactic acid (PLA), a commercially available and biodegradable polyesters, is widely used and studied since it is considered a environmentally friendly alternative to petrochemical-based synthetic polymers. In the present study, graphene nanoplatelets (GNP) reinforced PLA composites were prepared via solution blending followed by a compression molding process. Various physical and thermo-mechanical analyses were performed with the prepared composites. For instance, the electrical conductivity of PLA/GNP composites at various filler loadings was determined using four-point probe resistivity measurements. An electrical conductivity of 0.1 S/cm was achieved at a GNP loading of 12 wt%. Using the hot-disk transient plane source method, anisotropic thermal conductivity properties were evaluated. An in-plane and through-plane thermal conductivity of 0.87 W/mK and 0.58 W/mK was achieved with the addition of 6 wt% GNP, which is a nearly twofold and over fourfold increase compared to neat PLA, respectively.
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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".