A cationic polymer enhanced PAC for the removal of dissolved aquatic organic carbon and organic nitrogen from surface waters
Bibliographic record
Abstract
Abstract Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) are important components of the aquatic environment and may produce harmful disinfection by‐products through chlorination associated with water treatment processes. Therefore, it is necessary to remove them from the water. Coagulation is a cost‐effective water treatment technique as the key unit in the pretreatment of drinking water but deals with DON poorly. We present a study to investigate the enhancement of poly aluminum chloride (PAC) using a cationic polymer for the removal of both DON and DOC. The cationic dimethyl diallyl ammonium chloride (PDMDAAC) polymer was hybridized with PAC to remove DON and DOC. The results showed that the PDMDAAC increased the charge neutralization capacity of PAC and floc aggregation, thereby increasing the settling efficiency of the flocs. The PDMDAAC increased the amount of colloidal species in PAC, which was beneficial to the formation of adsorption‐bridging. With the increase of pH, a greater proportion of colloidal adsorption sites were used in the removal of DON. The DOC, DON, and turbidity removal were dependent on multiple interactions through charge neutralization, adsorption‐bridging, and floc sweeping. However, DOC and DON removal were primarily determined by adsorption‐bridging and floc sweeping, while turbidity removal was mainly dependent on charge neutralization.
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 | 0.000 | 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".