Fostering sustainability: The environmental advantages of natural fiber composite materials – a mini review
Bibliographic record
Abstract
In recent decades, natural fiber reinforced composites (NFRCs) have become an attractive substitute for conventional materials such as glass fiber and have attracted considerable interest from researchers and academics, particularly in the context of environmental protection. Environmental factors and their impact on the fundamental properties of renewable materials are becoming an increasingly popular area of study, particularly natural fibers and their composites. While this area of research is still expanding, natural fiber-reinforced polymer composites (NFRCs) have found widespread use in a variety of engineering contexts. Natural fibers (NFs) such as pineapple leaf (PALF), bamboo, abaca, coconut fibers, jute, banana, flax, hemp, sisal, kenaf, and others have many desirable properties, but their development and use present researchers with a number of obstacles. These fibers have attracted attention due to their various advantageous properties, such as lightness, economy, biodegradability, remarkable specific strength, and competitive mechanical properties, which make them promising candidates for use as biomaterials. As a result, they can serve as alternative materials to traditional composite fibers such as glass, aramid, and carbon in various applications. In addition, natural fibers have attracted the interest of an increasing number of researchers because they are readily available in nature and as by-products of agricultural and food systems, contributing to the improvement of the environmental ecosystem. This interest coincides with the search for environmentally friendly materials to replace synthetic fibers used in the construction, automotive, and packaging industries. The use of natural fibers is not only logical but also practical, as their fibrous form can be easily extracted and strengthened by chemical, physical, or enzymatic treatments. This article provides a brief overview of NFRCs, looking at their chemical, physical, and mechanical properties. It also highlights some of the significant advances associated with NFRCs from an economic, environmental, and sustainability perspective. Additionally, it provides a concise discussion of their diverse applications, all with a focus on their positive impact on the environment.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".