Antibiotic-metal complexes in wastewaters: fate and treatment trajectory
Bibliographic record
Abstract
Unregulated usage, improper disposal, and leakage from pharmaceutical use and manufacturing sites have led to high detection levels of antibiotic residues in wastewater and surface water. The existing water treatment technologies are insufficient for removing trace antibiotics and these residual antibiotics tend to interact with co-existing metal ions and form antibiotic-metal complexes (AMCs) with altered bioactivity profile and physicochemical properties. Typically, antibiotics, including tetracyclines, fluoroquinolones, and sulphonamides, interact with heavy metals such as Fe2+, Co2+, Cu2+, Ni2+, to form AMCs which are more persistent and toxic than parent compounds. Although many studies have reported antibiotics detection, determination, distribution and risks associated with their environmental persistence, very few investigations are published on understanding the chemistry of these complexes in the wastewater and sludge matrix. This review, therefore, summarizes the structural features of both antibiotics and metals that facilitate complexation in wastewater. Further, this work critically appraises the treatment methods employed for antibiotic removal, individually and combined with metals, highlights the knowledge gaps, and delineates future perspectives for their 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".