Recycled silica as a renewable and sustainable alternative to carbon black in natural rubber foams
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Sustainable natural rubber foams were prepared by replacing petroleum‐based carbon black (CB) with recycled silica (SiO 2 ) 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 (Δ M ) by 54%, scorch time ( t s ) by 50% and optimum curing time ( t 90 ) 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 × 10 3 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it