Application of polyelectrolytes for contaminant removal and recovery during water and wastewater treatment: A critical review
Bibliographic record
Abstract
The combination of polymeric characteristics and electrolyte behaviour endow aqueous polyelectrolytes with a strong potential for use in water and wastewater treatment. A correct and effective application of polyelectrolytes or polyelectrolyte complexes can remove different types of contaminants from aqueous solutions efficiently. Polyelectrolytes can be utilized directly as a water treatment material or indirectly as additives or modifiers to improve the effectiveness of existing water treatment processes. Previous reviews on this general research topic focused mainly on the function of polyelectrolytes in coagulation and flocculation processes, but they neglected other potential functions during water treatment processes. The current review introduces the typical polyelectrolytes utilized in water processing, including their properties and their interaction with contaminant species in water, and then summarizes and reviews the various unique applications of polyelectrolytes in water processing, including the polyelectrolyte enhanced ultrafiltration (PEUF) process, the application of polyelectrolytes to efficiently functionalize membranes and adsorbents, and the formation of polyelectrolyte-surfactant aggregates (PSAs) to recover metallic species from water. Finally, the challenges and opportunities for future investigation of the application of polyelectrolytes in water processing and treatment are discussed. • Typical polyelectrolytes utilized in water processing are summarized. • Various unique applications of polyelectrolytes in water processing are reviewed. • PEUF process, modification of membranes and adsorbents, and formation of PSAs to recover metals are emphasized. • The challenges and opportunities for future investigation of the application of polyelectrolytes are discussed.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".