Tuning the surface charge of cellulose super-bridging agents for improved performance during wastewater treatment
Bibliographic record
Abstract
Wastewater treatment is challenged by refractory contaminants and rising water demand, while conventional coagulation-flocculation suffers from low throughput and is sensitive to the influent water conditions. It has been shown that cellulose fibers can act as super-bridging agents offering enhanced turbidity removal when used in conjunction with traditional coagulants and flocculants. Yet, the chemical modification of cellulose fibers and their performance under varying influent conditions remain largely unexplored. In this study, recycled cellulose fibers are modified with quaternary ammonium groups, imparting a positive charge that significantly improves performance. Where conventional treatment (without fibers) reduces turbidity from 62 to 25 NTU, the addition of modified fibers lowers the effluent turbidity to 3 NTU. To assess the robustness of this strategy, influent pH, ionic strength, and turbidity are varied in controlled laboratory experiments. Modified fibers achieve turbidity below 5 NTU across all pHs (7–8.9) and initial turbidities (62–285 NTU) tested, whereas the conventional method is unable to reduce turbidity when pH exceeds 7.7. Furthermore, the modified fibers enhance the removal of metals such as Ni, Mn, Zn, Cr, Fe, and Pb. In total, pristine and modified fiber-enhanced treatments remove 52 % and 65 % of metals, respectively, compared to 39 % with the conventional method. Therefore, modified cellulose fibers represent an interesting strategy for improving the coagulation- flocculation treatment strategy.
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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".