Application of lime kiln dust as a filler in the natural rubber industry
Bibliographic record
Abstract
Abstract Lime kiln dust (LKD) obtained from kraft chemical recovery systems by conversion of calcium carbonate (CaCO3) back into calcium oxide (CaO) for reuse in the causticizing process, is mainly composed of CaCO3. A two‐stage conventional mixing procedure was used to incorporate LKD into natural rubber (NR). For comparison purposes, four commercial fillers, stearic acid coated CaCO3, ground CaCO3, silica, and carbon black, were also used. The effect of these fillers on the curing characteristics and mechanical properties of NR materials at various loadings ranging from 0 to 60 phr were studied. The results indicate that the use of LKD filler resulted in a lower Mooney viscosity and shorter curing time in the NR materials. The incorporation of LKD into NR improved the Young's modulus and hardness but decreased the tensile strength and tear strength. However, LKD was better in processability than the commercial fillers. Scanning electron micrographs revealed that the morphology of the rubbers filled with reinforcing fillers, such as silica and carbon black, was finer and more homogeneous compared to the those of the rubbers filled with LKD and commercial CaCO3. The dispersion of LKD and commercial CaCO3 fillers in the rubber matrix was discontinuous, which in turn, generated a weak structure compared with the reinforcing fillers. According to these observations, LKD could be used as a cheaper filler for NR materials where improved mechanical properties are not critical. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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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".