Properties and Flocculation Efficiency of Highly Cationized Starch Derivatives
Bibliographic record
Abstract
Abstract In this study the flocculation efficiency of cationic starch derivatives of varying degrees of substitution (DS) and different amylose/amylopectin ratios was investigated and compared to structural properties. The molar masses, Mw, radii of gyration, RG, and the molar mass distributions of the starch samples were determined by applying the method of flow field‐flow‐fractionation combined with multi‐angle light scattering and interferometric refractometry. In dewatering experiments potato starch derivatives of differing DS have optimum dosages that correspond to their cationicity. The lowest flocculant dosage yielding maximum dewatering results was determined for the most strongly substituted sample (DS = 1.48), while the least substituted starch derivative (DS = 0.28) showed the highest flocculant demand. Furthermore, the molar mass distribution and coil dimensions of derivatives seem to have a great influence on flocculation efficiency, because a 1.5‐fold increase in molar mass can compensate a 1.5‐fold decrease in DS. Toxicological investigations on synthetic and natural flocculants employing hens fertile egg screening test (HEST) led to the conclusion that cationic starches of DS < 0.95 are less toxic than commercial synthetic flocculants. When the results of flocculation experiments and toxicological data were taken into account, a moderate DS of 0.6 was deemed to give the best compromise between dewatering efficiency, cost‐effectiveness and ecological safety.
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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) | 0.000 | 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".