Flocculation of reed pulp suspensions by quaternary chitosan‐nanoparticle SiO<sub>2</sub>retention aid systems
Bibliographic record
Abstract
Abstract The microparticle retention aid system has been a focus on the studies of paper‐making chemicals.N‐(2‐Hydroxy‐3‐trimethylammonio)‐propyl chitosan chlorider [quaternary chitosan (QCS)]—nanoparticle SiO2dual component system was investigated in this work. The adsorpton kinetic experiments indicated that there was a very fast (<1 min) polymer adsorption under good mixing conditions. Adsorption of QCS onto the fiber surface was followed by a rearrangement to reach an equilibrium conformation. At the same time, QCS chains, existing on the surface of fiber, could permeate into the porous of the fiber, resulting in the reducing of zeta potential of the cellulosic fiber. In addition, the flocculation would be increased with the increasing of SiO2when the fiber substrates surfaces was net positively charged by an adsorbed QCS layer. It was also found that ionic strength decreased significantly the flocculation efficiency in pure QCS system, whereas the turbidity of the reed pulp suspension increased slightly with the increasing of NaCl concentration in QCS‐SiO2systems. The effect of shear force on the flocculation was tested. It was shown shear led to floc breakage and decreased the flocculation. These phenomena were very obvious for one‐component system (QCS or C‐St), but the microparticle system (QCS‐SiO2or C‐St‐SiO2) was shear resistance. Dynamic drainage experiment indicated that the turbidity of white water was decreased with the increasing of dosage of SiO2in experimental level. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
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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) | 0.000 | 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".