Flocculation of yeast suspension by a cationic polymer: Characterization of flocculent-cell interaction
Bibliographic record
Abstract
Addition of a cationic polymer can lead to the flocculation of yeast cells thus forming macroscopic floes [1]. Flocculation occurs by two main mechanisms (a) formation of macromolecular bridges between the particles, and (b) surface potential and charge reduction due to the adsorption of highly charged poly electrolytes on oppositely charged particles. The efficiency of flocculation is determined by the structure of polymer layers formed on the surface, i.e., the spacial distribution of adsorbed segments near the interface, the adsorption layer thickness, number and length of loops and tails protruding from the surface into the solution per unit area [2], In studying the flocculation behavior of microbial cells, many interaction forces need to be considered, such as, gravitational force, van der Waals attractive force, electrostatic repulsive force and specific shortrange force [3], In this study, we have investigated the flocculation of yeast cell suspensions by a cationic polymeric flocculent. The flocculation behavior of yeast cell suspensions at different times during the flocculation process is explained in terms of floe size, physicochemical surface characteristics of yeast and cell surface electric potentials (zeta potentials), dynamic mobility and Kappa value measurements. The flocculent particle size was visualized by a video camera, SILICON VIDEO® 2112 CCD. The initial rate of flocculation and growth of floe particles were dependant on the flocculent dosage. Particle size increased with increasing flocculent dosage up to certain point and then decreased. The magnitude of negative value of the zeta potential decreased and finally reached a positive plateau with increasing flocculent dose. However, during the flocculation process zeta potential was negative and magnitude decreased with the increasing flocculent dose. Our results indicate that homoflocculation occurs at the beginning of flocculation process. However, aggregation occurs at higher flocculent doses as the flocculation proceeds.
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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".