Evolution of Bionanocomposites: Innovations and Applications in Food Packaging
Bibliographic record
Abstract
Bionanocomposites are emerging as a pivotal innovation in sustainable food packaging, leveraging the strengths of biopolymers enhanced with nanoparticles for improved functionality. The increasing demand for sustainable packaging solutions, coupled with advancements in nanotechnology, has driven research in this field over the past decade. This review covers the full spectrum of developments in the field, from the classification and synthesis of bionanocomposites to their applications in food packaging and current research trends. A detailed trend analysis using Web of Science data highlighted the growth in bionanocomposite research, with over 17,000 articles published on this topic. Notably, more than 2000 of these articles focus specifically on packaging applications. This review also investigates the application trends for various food products, including fruits and vegetables, grains, meat, dairy products, bakery items, nuts, and oils. The review identifies a marked increase in publications related to bionanocomposite packaging since 2008. Notably, research on packaging applications has increasingly concentrated on fruits and vegetables, followed by meat, dairy products like cheese, and bakery products such as bread. A comprehensive analysis of research trends before 2010 and in 2024 underscores a shift from fundamental material science towards practical, real-world applications. This review provides valuable insights into the transformative potential of bionanocomposites for food packaging technologies and their role in advancing environmentally sustainable solutions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".