Optimization of Amylose to Amylopectin Ratio and Degree of Substitution in Quaternized Starch as a Tool to Improve Paper Strength
Bibliographic record
Abstract
Abstract Cationic starch serves as a prevalent wet‐end additive in the papermaking industry. Yet, the definitive effects of the ratio between the two principal components—amylose (AM) and amylopectin (AP)—and the degree of substitution (DS) on the mechanical strength enhancement of paper sheets remain incompletely understood. This study synthesizes a series of quaternized amylose (QAM) and quaternized amylopectin (QAP) with varying DS levels and blended these derivatives in diverse ratios. Subsequently, these mixtures are integrated into pulp suspensions to ascertain the DS and the AM‐to‐AP ratio that optimize paper strength. These results indicate that a QAM‐to‐QAP blend with a DS of 0.08 at a ratio of 2:8 yielded the most pronounced improvement in paper mechanical properties. This precise formulation significantly enhances tensile, burst, and tear strength indices, increasing by approximately 55.0%, 44.4%, and 78.4%, respectively, under the most favorable conditions and with an additive dosage of 1 wt.%. This investigation provides substantive and actionable knowledge for selecting starch additives in the wet‐end of papermaking, which can significantly augment the efficiency and efficacy of paper manufacturing processes.
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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".