Progress in bioplastics blends, compatibilization, modifications, and AI-driven innovations for material applications
Bibliographic record
Abstract
Many bioplastics offer potential advantages over petroleum-based plastics, such as renewability, improved sustainability, and, in some cases, biodegradability or lower toxicity. However, in most cases, their limited mechanical performance, processing stability, or higher production costs hinder widespread adoption. Blending is a key strategy to overcome these limitations; however, the inherent immiscibility of most biopolymers leads to challenges like coarse morphology and poor interfacial adhesion. This review aims to provide an in-depth analysis of bioplastic blends by examining the fundamental principles (thermodynamic interactions, process kinematics, and morphology development) that control their behavior. It critically evaluates a broad spectrum of compatibilization strategies that span non-reactive and reactive methods and those utilizing nanofillers, aimed at stabilizing blend microstructures and enhancing material performance. A novel aspect of this work is its integration of these material science concepts with important end-of-life considerations, including biodegradability and recyclability challenges. Furthermore, it highlights the transformative role of artificial intelligence (AI) and machine learning (ML) as novel instruments for accelerating the design and optimization of next-generation bioplastic formulations. Overall, this review concludes that unlocking the full potential of bioplastics for high-performance industrial applications necessitates a holistic approach that integrates tailored blending strategies with advanced computational design, thus paving the way for the realization of a circular bioeconomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".