Investigation on a Novel Fly Ash Based Calcium Silicate Filler: Effect of Particle Size on Paper Properties
Bibliographic record
Abstract
Value-added utilization of fly ash has recently gained a strong interest. As a solid waste, fly ash can be used as a paper filler, and the recent innovation on the production of high-brightness fly ash products further facilitated such applications. This work reports the results on using the novel fly ash based fillers in the paper making process, with a focus on the effect of filler particle size. In comparison with ground calcium carbonate (GCC) commonly used as paper fillers, the original fly ash based calcium silicate filler (FACS) has a larger particle size (27.6 μm), a much lower true density (1.3–1.4 g/cm 3 ), a higher specific surface area (121 m 2 /g), and a similar brightness (91% ISO). FACS exhibits porous, aggregated, and needle-like morphologies based on the results of scanning electron microscope image analysis. Ball milling decreased the particle size, broadened the particle size distribution, and improved the brightness while changing its morphology. The paper bulk increased dramatically when the original FACS was used due to its large particle size and narrow particle size distribution. With ball milling, the paper bulk and porosity decreased with decreasing particle size at the same filler content, while the tensile index increased. In addition, the ball milled FACS-filled paper had better light scattering coefficient and brightness than the GCC-filled paper.
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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 | 0.001 |
| 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".