Effect of Biochar on the Thermal and Dimensional Stability of Poly(Vinyl Chloride) <scp>(PVC)</scp> Composites
Bibliographic record
Abstract
ABSTRACT This study investigated the employment of biomass‐derived biochar as performance‐enhancing filler of PVC. Mechanical, thermal, rheological, and morphological properties were examined, with calcium carbonate (CaCO 3 ) used for comparison. Composites were prepared via melt processing followed by injection molding to generate test specimens. High filler concentrations caused significantly improved mechanical properties like tensile strength and modulus. At 23 wt.%, the biochar displayed similar tensile strength as the CaCO 3 ‐filled composites, while providing weight reduction benefits, suggesting biochar could replace traditional fillers in construction materials. However, higher filler content beyond 23 wt.% led to a sharp decline in properties, indicating a limit to filler usage. Biochar addition also increased the composite's glass transition ( T g ) and thermal stability. Due to their mechanical property performance, thermal permanence, and low carbon footprint, biochar can be a suitable and sustainable alternative reinforcing filler of PVC. Challenges, such as aggregation and poor interfacial adhesion, can be addressed by optimizing processing parameters, incorporation of compatibilizers, and tuning filler levels.
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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".