Biodegradation of polyethylene terephthalate microplastics by bacterial communities from activated sludge
Bibliographic record
Abstract
Abstract The emerging accumulation of microplastics (MPs) in global waters is of increasing concern and it is posing great health risks to both humans and aquatic species, yet suitable technologies to remove MPs are lacking. The objective of this study was to investigate activated sludge as a source of promising biocatalysts for the removal of MPs in water. Bacterial communities in activated sludge were first screened for their potential to degrade hydrolyzable plastics from polyethylene terephthalate (PET) pre‐treated at 100°C for 1 hour. The consortium grew on a mineral medium with PET MPs as the sole carbon and energy source. To further assess its degrading potential, the consortium was put through a standardized CO 2 evolution test at a temperature of 30°C, pH 7‐7.5, reactor residence time 168 days, and PET concentration of 2.63 g/L. The biodegradation extent was further validated through assessment of morphological/structural changes on the PET by means of SEM, DSC, FTIR, and viscometry analyses. Upon incubation, the consortium degraded 17% of PET. The molecular weight remained unchanged, reflecting a degradation via surface erosion. Furthermore, the biodegradation was significantly enhanced at high oxygen flow rates. Two bacterial strains within the consortium were isolated and identified as Bacillus cereus SEHD031MH and Agromyces mediolanus PNP3. Both strains thrived when individually cultured with PET while only B. cereus showed enzymatic activity during a clear‐zone test. The examined bacterial strains possess a promising PET‐degrading activity that can be further investigated and applied to the elimination of MPs water/wastewater through innovative and effective technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".