The Food–Materials Nexus: Next Generation Bioplastics and Advanced Materials from Agri‐Food Residues
Bibliographic record
Abstract
The most recent strategies available for upcycling agri-food losses and waste (FLW) into functional bioplastics and advanced materials are reviewed and the valorization of food residuals are put in perspective, adding to the water-food-energy nexus. Low value or underutilized biomass, biocolloids, water-soluble biopolymers, polymerizable monomers, and nutrients are introduced as feasible building blocks for biotechnological conversion into bioplastics. The latter are demonstrated for their incorporation in multifunctional packaging, biomedical devices, sensors, actuators, and energy conversion and storage devices, contributing to the valorization efforts within the future circular bioeconomy. Strategies are introduced to effectively synthesize, deconstruct and reassemble or engineer FLW-derived monomeric, polymeric, and colloidal building blocks. Multifunctional bioplastics are introduced considering the structural, chemical, physical as well as the accessibility of FLW precursors. Processing techniques are analyzed within the fields of polymer chemistry and physics. The prospects of FLW streams and biomass surplus, considering their availability, interactions with water and thermal stability, are critically discussed in a near-future scenario that is expected to lead to next-generation bioplastics and advanced materials.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".