Autoclaved aerated concrete waste (AACW): An alternative filler material for the natural rubber industry
Bibliographic record
Abstract
Material waste from the production of autoclaved aerated concrete, a porous material, should be considered as a valuable byproduct for use as a filler material for the rubber industry. Natural rubber (NR) composites filled with different loading (over the range of 0–60 phr) of autoclaved aerated concrete waste (AACW) as a new eco‐friendly material were produced using two roll mills and then were studied for their cure characteristics, mechanical and aging properties, and morphology, and also compared with commercial fillers, calcium carbonate (CaCO3), and silica (SiO2). In most cases, the cure characteristics and mechanical and aging properties of the SiO2‐filled NR composites were significantly better than those of the AACW‐ and CaCO3‐filled NR composites. However, these properties for AACW‐filled composites appeared to be higher than CaCO3‐filled composites. The reason for this could be due to a larger surface area which is both porous and of an irregular shape of the AACW filler used. Scanning electron microscope images showed that the morphology of the rubber filled with SiO2 was finer and more homogenous compared with the rubber filled with AACW or CaCO3. Overall results revealed that the reinforcement ability of AACW‐filled NR composites was generally better when compared with CaCO3‐filled NR composites; therefore, AACW can be used effectively as a cheaper filler for production of rubber products where end‐use properties of a rubber product is specifically required. POLYM. COMPOS., 36:2030–2041, 2015. © 2014 Society of Plastics Engineer
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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".