Development of biocomposites from preprocessed wood barks
Bibliographic record
Abstract
Due to the rapid advancement of material science and technology, it is anticipated that the next growth area for biomaterials will be in developing energy-efficient processes and lightweight performance products for automotive applications. In this context, wood barks represent a low cost feedstock to produce biocomposites but there has been limited work on investigating its feasibility. The main emphasis of this work is on the utilization of preprocessed yellow birch barks and thermoplastic polypropylene to develop low cost biocomposites, more sustainable and renewable materials, whilst reducing weight and cost and maintaining reliability. The barks were utilized in two different forms. In the first case, the barks were used directly to prepare the biocomposites, while prior extraction of tannins from the barks was performed in a second case. Both forms of the barks were melt-compounded with polypropylene at different bark/polypropylene ratios in a twin-screw extruder to enhance the interaction between them. A coupling agent was used to improve the bark-matrix interface and maximize the desired biocomposite mechanical properties. In addition, low cost calcium oxide filler was added to further replace the fraction of the PP in the blend. The morphology, the mechanical properties, the water sensitivity and the thermal stability of the obtained biocomposites were also investigated and will be presented. A comprehensive mass and energy balance was established and a systematic strategy was implemented for adjusting and optimizing energy use in the integrated twin-screw extrusion system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".