Use of nanofibrillated cellulose (NFC) in thermoplastic biocomposites for eco-responsible automotive interior parts
Bibliographic record
Abstract
Current environmental changes mandate for immediate actions to be taken by players from all industries. Automotive industry needs to decrease vehicles fuel consumption and, implicitly, their emissions. The main solutions are the weight reduction of vehicles, the introduction of renewable, bio-sourced and of recycled materials in the car parts. In North America, thermoplastics account for ~8 % of the total vehicle weight, i.e. ~155 kg/vehicle. This proportion is expected to increase due to continuous growing of the replacement of metals by fiber reinforced plastics. The utilization of the most common automotive plastics, such as polypropylene (PP) (interior applications) and polyamides (PA) (under-the-hood applications), can be reduced by using cellulosic fibers in their composition, a renewable and abundant natural polymer. This work presents the use of nanofibrillated cellulose (NFC) in the development of thermoplastic biocomposites for automotive interior parts. NFC, cellulose fibrils belonging to the nanocellulose reinforcement category, are produced from Northern Bleached Softwood Kraft pulp (NBSK) by a chemical-free top-down mechanical refining process which results in fibrils of high strength, purity, aspect ratio, and surface area. NFC fibrils were used in mixtures with NBSK fibers. The effect of NFC amount in NBSK:NFC mixtures, i.e. 100:0, 90:10, 80:20, and 70:30, was explored in PP and PA6 biocomposites containing 20 wt.% NBSK:NFC mixtures. Up to 50% increments in mechanical and thermal properties were observed when the mixture of NBSK:NFC 80:20 was used. This mixture, at different contents, was compounded further in PP, PA6, and polylactic acid (PLA) matrices to obtain new biocomposites. Mechanical and thermal properties of biocomposites presented increments when compared at initial matrices but, also, when compared to composites currently used in car parts. Biocomposites based on PP and PLA were also tested for their recyclability and proved, after at least five cycles of recycling, a preservation of their performance. The biocomposites were used in injection molding to fabricate prototypes for automotive interior applications, and, as well, in the fabrication of filaments for 3D printing and 3D printed objects. These eco-friendly bio-based materials shown great potential to replace the conventional petroleum-based ones currently used in automotive interior parts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".