Impact of renewable carbon on the properties of composites made by using three types of polymers having different polarity
Bibliographic record
Abstract
Abstract Pine wood derived biocarbon (BioC) is investigated as a renewable alternative to carbon black (CB) for plastics and composites applications. Three different polymers with different polarity were used to prepare the composites: polypropylene (PP), polylactic acid (PLA), and polyamide 6 (PA6). Comparatively, CB had a nodule size of ~300 nm and surface area of 8 m2/g, whereas BioC showed an average particle size of ~950 nm and surface area of ~260 m2/g, respectively. CB, in the composites, was found in large aggregations in the flow direction (FD), while BioC particles showed a better dispersion. Aggregation of CB affected mostly the mechanical strength of the composites. Furthermore, it was found that the overall performance of composites was influenced more by the polarity of the phases, rather than the particle size or the surface area of the fillers. Even when the polarity of the particles had an expected trend (PA6 > PLA > PP with BioC > CB), the work of cohesion obtained for the composites was PA6‐BioC > PP‐BioC > PLA‐BioC, showing, in particular, that the chain‐to‐chain intermolecular forces in neat PLA are stronger as compared to those developed by the particle‐matrix interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".