Removal of 17β-estradiol (E2) and its chlorination by-products from water and wastewater using non-imprinted polymer (NIP) particles
Bibliographic record
Abstract
Endocrine disrupting compounds and their chlorination by-products are two classes of emerging contaminants. Surface water and wastewater treatment technologies have limitations in removing these contaminants. This study evaluated the ability of non-imprinted polymer particles (NIP) to remove the endocrine disruptor 17beta-estradiol (E2) and its chlorination by-products from water and wastewater. NIP effectively removed 98% of 10 mg/L E2 from wastewater. NIP were also effective in removing chlorination by-products of E2 by 84.9% after 10 mg/L E2 in water was chlorinated at 5 mg/L. In the presence of 5 mg/L humic acid, NIP were able to achieve removal of 10 mg/L E2 by greater than 99.9%. Furthermore, after chlorination of 10 mg/L E2 and 5 mg/L humic acid at 10 mg/L chlorine, NIP were also able to remove the chlorination by-products formed as well as the remaining E2 by greater than 99.9%. The presence of 5 mg/L humic acid did not adversely affect the adsorption efficiency. The results of this research indicate that NIPs have good potential as a final treatment step for surface water and wastewater treatment.
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) | 0.000 | 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".