Strategies for biofilm optimization of plastic-degrading microorganisms and isolating biofilm formers from plastic-contaminated environments
Bibliographic record
Abstract
Abstract The perpetual disposal of plastic waste, combined with ineffective waste management strategies, has resulted in widespread environmental plastic pollution. Microbial plastic biodegradation represents an emerging solution to this problem. However, biodegradation studies tend to overlook the fundamental prerequisite of initial surface colonization via biofilm formation. This study had two independent but connected aims relating to plastic surface colonization by microorganisms: to enhance biofilm formation by known plastic degraders, with translational potential for improved plastic degradation, and to isolate microorganisms from microplastic contaminated environments with the ability to colonize plastic surfaces. Planktonic and biofilm responses to diverse carbon and energy sources were investigated over 7 days, using Bacillus subtilis 168, Fusarium solani (Martius) Saccardo, Ideonella sakaiensis 201-F6, Pseudomonas putida KT2440, and Rhodococcus ruber C208. This enabled optimal conditions for biofilm formation by each strain to be determined. In parallel, environmental samples containing synthetic or natural polymeric substances (anaerobic digestate, landfill leachate, and microplastic contaminated compost) were incubated with polyethylene and polyethylene terephthalate films, to isolate microorganisms capable of colonizing their surfaces. This yielded eight bacterial isolates from three genera: Bacillus, Lysinibacillus, and Proteus. These genera contain species that have been shown to degrade plastics and other recalcitrant synthetic polymers, demonstrating the success of our approach. This study also suggests that discrete plastic types may create different ecological niches which can be exploited by unique bacterial colonizers. Our findings underscore the importance of considering plastic colonization by microbial biofilms in the context of their biodegradation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".