Biodegradation of Poly(Ethylene Terephthalate) Microplastics by Baceterial Communities From Activated Sludge
Bibliographic record
Abstract
The emerging accumulation of microplastics (MPs) within global waters and the risks they pose to both humans and aquatic species are of increasing concern, yet suitable technologies to remove MPs are lacking. In this study, bacteria with potential to degrade MPs were isolated from activated sludge as promising biocatalysts for the removal of MPs in water. The bacterial communities in activated sludge were first screened for their potential to degrade thermallytreated MPs from PET. The consortium exhibited growth on a mineral medium with PET MPs as the sole carbon and energy source, indicating the presence of degrading bacteria. To further assess its biodegradability potential, the consortium was put through a CO2 evolution test where the degradation of MPs was monitored through measuring the CO2 evolved. The test was carried out in an experimental device that was engineered and constructed according to ISO 14852. The biodegradation extent was further validated through assessment of morphological and structural changes on the MPs by means of scanning electron microscopy, differential scanning calorimetry and Fourier transform infrared spectroscopy analyses. Upon incubation, the consortium degraded 17 % of PET MPs. Three bacterial strains within the consortium were isolated and identified as Lysinibacillus macroides RW13-2, Bacillus cereus SEHD031MH and Agromyces mediolanus PNP3. The latter two thrived individually with PET while only B. cereus showed enzymatic activity during a clear-zone test. The examined bacterial strains possess a promising PETdegrading activity that can be further investigated and applied to the elimination of MPs in water/wastewater through innovative and effective technologies
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".