Diclofenac Degradation by Immobilized <i>Chlamydomonas reinhardtii</i> and <i>Scenedesmus obliquus</i>
Bibliographic record
Abstract
Diclofenac (DCF), a commonly used anti-inflammatory medication, presents environmental concerns due to its presence in water bodies, resistance to conventional wastewater treatment methods, and detection at increasing concentrations (ng/L to µg/L) that highlight DCF as a global emerging pollutant. While microalgae have been effective in degrading DCF in wastewater, immobilization into a matrix offers a promising approach to enhance treatment retention and efficiency. This study aimed to evaluate the efficacy of DCF removal using immobilized freshwater microalgae. Two algal species, Chlamydomonas reinhardtii (Chlamydomonas) and Scenedesmus obliquus (Scenedesmus), were tested for 6 days in both free and immobilized forms to determine if immobilized algae could degrade DCF comparably to free cells. The findings indicate that by Day 3, immobilized Chlamydomonas and Scenedesmus removed 78.0% and 80.1% of DCF, outperforming free-cell cultures. Mixed cultures demonstrated synergistic effects, with removal amounts of 91.4% for free and 92.3% for immobilized systems. By Day 6, all conditions achieved complete DCF removal (100%). Mechanistic analysis showed 80.0% biodegradation and 20.0% bioaccumulation in free Chlamydomonas and 56.8% biodegradation with 43.2% bioaccumulation in Scenedesmus. Immobilization shifted pathways slightly: in Chlamydomonas, 61.6% of DCF removal occurred via biodegradation, 18.3% via bioaccumulation, and 20.1% via abiotic degradation. For Scenedesmus, immobilization achieved 45.6% biodegradation, 36.6% bioaccumulation, and 17.8% abiotic degradation, enhancing abiotic degradation while maintaining biodegradation efficiency. This research serves as a proof of concept for utilizing immobilized algae in DCF removal and suggests an avenue for improved wastewater treatment of emerging contaminants.
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) | 0.000 | 0.001 |
| 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".