Ultrahigh Efficiency Hybrid Biomass-Inorganic Coagulation Strategy for Water Treatment
Bibliographic record
Abstract
Coagulation is a vital water treatment process for removing colloids, natural organic matter, and microorganisms, such as bacteria and algae. While aluminum and iron salts are highly effective conventional coagulants, their use raises concerns including health risks linked to aluminum exposure, altered water taste, and secondary contamination from exceeding the amount of iron residues. Biomass-derived coagulants, obtained from renewable resources such as plant extracts and agricultural residues, have emerged as sustainable alternatives due to their biodegradability and minimal sludge production. However, their lower coagulation efficiency, particularly in treating complex or highly turbid water, limits widespread application. This study investigates a hybrid coagulation strategy combining cellulose-derived materials dual polymer-grafted cellulose nanocrystals with reduced doses of iron salts to enhance performance and overcome limitations. This synergistic approach significantly enhances coagulation efficiency, achieving 90% turbidity reduction in soluble humic substances in water with minimal coagulant dosage. In addition, this method is a competitive alternative to the use of synthetic flocculant polyacrylamide, offering environmental benefits. In this way, the potential hybrid coagulation mechanism is studied as well. Additionally, the strategy provides a universal framework for integrating various modified biomass-derived coagulants with metallic salts, optimizing dissolved organic matter removal. By advancing this hybrid methodology, the research bridges performance gaps in biomass-based systems. This work not only highlights the potential of hybrid coagulants for natural organic matter removal but also demonstrates their adaptability across diverse environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".