Sustainability Meets Functionality: Green Design Approaches to Cellulose-Based Materials
Bibliographic record
Abstract
Cellulose has gained significant attention as a sustainable resource due to its abundance, renewability, and biodegradability, making it a promising alternative to nonbiodegradable materials. Various cellulose-based materials (CBMs) have been engineered to improve the properties of natural cellulose. However, achieving full sustainability of CBMs remains challenging, primarily on account of the intensive pretreatment and fabrication processes involved. Therefore, this review highlights recent advances in balancing functionality and sustainability in CBMs. The first section examines the key parameters and mechanisms that influence the mechanical, thermal, barrier, and optical properties of CBMs, alongside their promising applications. Additionally, this review offers a comprehensive discussion on the sustainability of CBMs, focusing on (nano)cellulose extraction from renewable sources using green solvents, eco-friendly and scalable fabrication processes, and sustainable end-of-life strategies such as biodegradation and recycling. Overall, this review offers guidelines for designing functional and green CBMs, contributing to the broader goal of a circular, zero-waste society.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".