Recycled silica as a renewable and sustainable alternative to carbon black in natural rubber foams
Bibliographic record
Abstract
Abstract Sustainable natural rubber foams were prepared by replacing petroleum‐based carbon black (CB) with recycled silica (SiO2) nanoparticles. The total nanofiller concentration was fixed at 40 phr, while the CB/silica ratio was changed from 40/0 to 0/40. The results showed that increasing the silica content increased the curing characteristics, such as delta torque (ΔM) by 54%, scorch time (ts) by 50% and optimum curing time (t90) by 65%. But foams based on a hybrid system (20/20) produced a more homogeneous structure improving the cell nucleation step and leading to the smallest cell size (18 μm) and highest cell density (8.8 × 103 cells mm−3) due to reduced filler−filler interactions and better particle dispersion. This improved cellular morphology generated superior mechanical and thermal insulation performance, including the highest compression modulus (2.7 MPa), compressive strength (1.9 MPa) and recoverability (96.6%) combined with the lowest thermal conductivity (0.114 W m−1 K−1) at a density of 0.652 g cm−3. Nevertheless, the foam with 40 phr silica showed higher compressive modulus (26%) and compression strength (15%) compared to the reference sample having 40 phr CB, mainly due to its higher crosslink density. As a final comparison, the recycled silica, being a suitable and sustainable alternative to petroleum‐based CB, showed superior mechanical and thermal insulation properties compared to a commercial grade of silica for natural rubber foams. © 2023 The Authors. Polymer International published by John Wiley & Sons Ltd on behalf of Society of Industrial Chemistry.
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".