A comprehensive review of natural fibers and their composites: An eco-friendly alternative to conventional materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breakthroughs in materials science are the driving force behind many of today's industrial advancements in our fast-changing high-tech world. Composite materials have proven valuable in numerous sectors, including automotive, aerospace, aeronautics, naval, and sports, due to their exceptional mechanical properties and lightweight nature. However, environmental concerns have led to a decrease in the use of fossil fuel-derived materials. Additionally, efforts to reduce greenhouse gas emissions and improve fuel efficiency require lightweight materials with a lower carbon footprint, highlighting the importance of natural fiber composites. Natural fiber composites are made from renewable resources, comprising reinforcements made of natural fibers such as jute, flax, ramie, hemp, cotton, sisal, and kenaf, and a matrix, preferably derived from biomass, which may or may not be biodegradable. However, plant fibers have certain drawbacks when combined with polymers. Due to the presence of hydroxyl groups in lignocellulose, plant fibers are hydrophilic, making them incompatible with hydrophobic thermoplastics and prone to moisture damage. These limitations pose challenges for using plant fibers as polymer reinforcement. To improve adhesion between fibers and the polymer matrix and reduce moisture absorption, surface modifications are typically required. Various methods, such as alkaline, silane, or other chemical treatments, have been developed to enhance fiber-matrix compatibility and improve composite quality. Although natural fiber composites are still in development and their applications are limited, they hold great promise as a sustainable alternative to conventional materials.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it