Natural fibre-reinforced thermoplastic composites: A bibliometric analysis and review of eco-friendly solutions in exterior automotive components
Bibliographic record
Abstract
The automotive industries are consistently working towards the advancement of lightweight components to enhance fuel efficiency. Thermoplastic matrix composites have been found to provide several notable benefits, including potential weight reduction, recyclability, specific strength, corrosion resistance, cost-efficiency, and design versatility. The variety of material forms and combinations in fibre-reinforced thermoplastic polymers has expanded, opening new possibilities for their use in the automotive sector and beyond. Natural fibres, due to their biodegradability, widespread availability in nature, and cost-effectiveness, offer an opportunity to broaden the range of materials used while reducing reliance on petroleum-based products. Moreover, various modifications are employed to enhance the properties of natural fibres, making them more competitive with synthetic fibres for these applications. This review provides a thorough bibliometric and technical overview of the use of natural fibres as reinforcement fibres in the thermoplastic composites. A particular focus is placed on plant fibres and their potential applications as composite reinforcement for automotive applications, especially exterior components. The review also provides an overview of the properties of commonly available thermoplastic matrices and the various composite fabrication techniques. This information can assist in making informed decisions when selecting the most suitable material and process for specific product development. Furthermore, this review examines the mechanical properties of natural fibre reinforced thermoplastic composites and sheds light on the important challenges that must be addressed.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.050 | 0.046 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".