Hydrothermal carbonization as a promising approach towards the removal of polyethylene microplastics and trace organic contaminants from wastewater sludge
Bibliographic record
Abstract
Microplastics from sludge need to be eliminated since they can enter the environment through agronomic utilization and threaten the organisms as well as water quality. This study provides novel insights into the concurrent removal of polyethylene microplastics and trace organic contaminants (TrOCs) from two types of sludges (oxidation ditch sludge (ODS) and aerated lagoon sludge (ALS)) using hydrothermal carbonization (HTC), a low-energy process, by analyzing the impacts of temperature and time on removal efficiency. HTC at 220°C (± 3 °C) for 3 hours removed about 71% (± 7.52%) of microplastics from sludge. Temperature and contact time were observed to have a significant effect ( p < 0.05) on microplastic removal efficiency. Morphological characterizations of residual microplastics after HTC revealed that exposure to subcritical water under hydrothermal conditions caused surface deterioration and structural changes. Several TrOCs were detected in sludges, and caffeine exhibited the highest concentration (16.34 µg/g) in the solid phase of ODS, whereas carbamazepine showed the highest concentration (0.695 µg/L) in the aqueous phase. Furthermore, caffeine had the highest concentration (0.240 µg/L) in the aqueous phase of ALS, whereas carbamazepine was the most concentrated (19.96 µg/g) in the solid phase. The initially observed TrOCs were not detected in the aqueous phase nor the solid phase after the HTC, irrespective of varying temperatures and contact times, which signifies their complete removal. These findings demonstrated that HTC is a sustainable sludge management strategy since it can effectively eliminate microplastics and TrOCs from sludge and thus minimize their detrimental effects on the environment.
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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".