Impact of fiber-based super-bridging agents on contaminant removal via settling and screening: microplastics, textile fibers, and turbidity
Bibliographic record
Abstract
The water treatment industry relies heavily on coagulation and flocculation processes. This technology requires large amounts of chemicals and large settling tanks for floc separation. The flocs formed during conventional treatment are small (<100 µm), which limit their removal by gravitational separation. To improve floc separation, fiber-based super-bridging agents have been added to the coagulation/flocculation process. When fibers were used in combination with a coagulant and flocculant, the flocs formed were 10 – 100 times larger, and settling was remarkably improved. The tested super-bridging agents also led to a 50% reduction in demand for both the coagulant and flocculant. The use of super-bridging agents is an effective technique for reducing turbidity and improving the removal of emerging contaminants in both synthetic and natural surface water. Formation of very large flocs with fibers also allowed the replacement of settling by screening without any effect on removal of monitored contaminants. Fibrous treatment removed up to 78% of turbidity when using a 5000 µm screen mesh size, compared to only 45% with conventional treatment (coagulant and flocculant, no fibers). Super-bridging agents also drastically improved microplastic removal. The fibrous treatment removed 80% of 15 µm polyethylene beads, compared to only 20% with the conventional treatment. Such low removal indicates potential concerns regarding the effective removal of smaller microplastics in existing water treatment plants.
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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) | 0.000 | 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".