Harnessing Bioelectrochemical and Anaerobic Systems for the Degradation of Bioplastics: Application Potential and Future Directions
Bibliographic record
Abstract
As the environmental burden of traditional plastics continues to grow, bioplastics (BPs) have emerged as a promising alternative due to their renewable origins and potential for biodegradability. However, the most popular anaerobic systems (ASs)—anaerobic digestion (AD), acidogenic fermentation (AF), and enzyme hydrolysis (EH)—for BPs degradation still face many challenges, e.g., low degradation efficiency, process instability, etc. As a sustainable clean energy technology, bioelectrochemical systems (BESs) have demonstrated strong potential in the treatment of complex organic waste when integrated with ASs. Nevertheless, research on the synergistic degradation of BPs using BES-ASs remains relatively limited. This review systematically summarizes commonly used anaerobic degradation methods for BPs, along with their advantages and limitations, and highlights the BES-AS as an innovative strategy to enhance BPs degradation efficiency. BESs can accelerate the decomposition of complex polymer structures through the activity of electroactive microorganisms, while also offering benefits such as energy recovery and real-time process monitoring. When coupled with anaerobic digestion, the BES-AS demonstrates significant synergistic effects, improving degradation efficiency and promoting the production of high-value-added products such as volatile fatty acids (VFAs) and biogas, thereby showing great application potential. This review outlines current research progress, identifies key knowledge gaps in mechanism elucidation, system design, source recovery, etc., and proposes future research directions. These include system optimization, microbial community engineering, development of advanced electrode materials, and omics-based mechanistic studies. Advancing multidisciplinary integration is expected to accelerate the practical application of BES-ASs in BP waste management and contribute to achieving the goals of sustainability, efficiency, and circular utilization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".