Genetic engineering approach to address microplastic environmental pollution: a review
Bibliographic record
Abstract
Polymeric materials have desirable chemical and physical properties, leading to a wide range of applications in consumer industries. However, such properties, which include high hydrophobicity, crystallinity, strong chemical bonds and high molecular weight, inhibit natural biodegradation of plastics by wild-type microbes. This has led to the accumulation of microplastics and nanoplastics in the environment, which is projected to be 12 000 million metric t by the year 2050. Such accumulation bears serious health side effects on both terrestrial and marine ecosystems. Current methods used to control microplastics in the environment have proved inadequate due to high plastic production and extensive uses. Biological methods of controlling plastic pollution, which involve enzymes from various microbes, have emerged as an efficient, eco-friendly and sustainable alternative to plastic treatment and recycling. However, naturally occurring plastic-biodegrading enzymes possess limited biodegradation capacity due to low thermostability and biocatalytic activities, thus limiting large-scale applications. This review focuses on leveraged protein enzyme genetic engineering techniques intended to improve the catalytic performance of putative plastic-biodegrading enzymes and production of environmentally friendly bioplastics from natural fibres as a substitute for synthetic petroleum-based plastics. Genetically modified plastic-degrading enzymes possess boosted substrate interaction, increased hydrophobicity, better catalytic efficiency, increased thermostability and optimised plastic biodegradability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".