Low-Cost Carbon Fillers to Improve Mechanical Properties and Conductivity of Epoxy Composites
Bibliographic record
Abstract
In recent years, low-cost carbons derived from recycled materials have been gaining attention for their potentials as filler in composites and in other applications. The electrical and mechanical properties of polymer composites can be tuned using different percentages and different kind of fillers: either low-cost (e.g., carbon black), ecofriendly (e.g., biochar), or sophisticated (e.g., carbon nanotubes). In this work, the mechanical and electrical behavior of composites with biochar and multiwall carbon nanotubes dispersed in epoxy resin are compared. Superior mechanical properties (ultimate tensile strength, strain at break) were noticed at low heat-treated biochar (concentrations 2⁻4 wt %). Furthermore, dielectric properties in the microwave range comparable to low carbon nanotubes loadings can be achieved by employing larger but manageable amounts of biochar (20 wt %), rending the production of composites for structural and functional application cost-effective.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".