Modification of papermaking grade fillers: A brief review
Bibliographic record
Abstract
The use of fillers in paper products can provide cost and energy savings, improved paper properties, increased productivities, and specifically desired paper functionalities. There are many problems associated with the use of fillers, such as unsuitability of calcium carbonate fillers in acid papermaking, negative effects of filler loading on paper strength, sizing, and retention, and tendencies of fillers to cause abrasion and dusting. In order to solve these problems and to make better use of fillers, many methods have been proposed, among which filler modification has been a hot topic. The available technologies of filler modification mainly include modification with inorganic substances, modification with natural polymers or their derivatives, modification with water-soluble synthetic polymers, modification with surfactants, modification with polymer latexes, hydrophobic modification, cationic modification, surface nano-structuring, physical modification by compressing, calcination or grinding, and modification for use in functional papers. The methods of filler modification can provide improved acid tolerant and optical properties of fillers, enhanced fiber-filler bonding, improved filler retention and filler sizabilities, alleviated filler abrasiveness, improved filler dispersability, and functionalization of filled papers. Filler modification has been an indispensable way to accelerate the development of high filler technology in papermaking, which is likely to create additional benefits to papermaking industry in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".