EFFECT OF CELLULOSE FIBER SURFACE TREATMENT TO REPLACE CARBON BLACK IN NATURAL RUBBER HYBRID COMPOSITES
Bibliographic record
Abstract
ABSTRACT In recent years, cellulose fibers have attracted considerable attention as biofillers for natural rubber (NR) composites. However, neat cellulose cannot be used as a substitute for conventional fillers due to its poor compatibility with NR. Therefore, a new surface treatment via maleic anhydride grafted to polyisoprene (MAPI) in solution was developed to improve the filler–matrix interaction. Different contents of carbon black (CB) and cellulose fibers (before and after modification) were used as a hybrid filler system to investigate the possibility of CB substitution in NR composites. First, contact angle, Fourier transformed infrared spectrometry (FTIR), and scanning electron microscopy (SEM) techniques were used to confirm the successful cellulose surface treatment. Second, morphological analysis, Payne effect, and swelling behavior of the rubber compounds in toluene confirmed the effect of cellulose treatment on improving the interfacial filler–matrix adhesion. Finally, the results showed that the composite filled with 20 phr modified cellulose and 20 phr CB (50% replacement of CB) exhibited even better results than the composite filled with 40 phr of CB, since the tensile strength was only 7% lower, but the elongation at break, tensile modulus at 100%, and storage modulus at 25 °C were respectively 35%, 24%, and 22% higher.
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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 | 0.000 | 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".