Optimizing A Plastination Technique For Preserving Natural Fibre Composites
Bibliographic record
Abstract
Natural fibre-reinforced polymer composites (NFRPs) are highly sensitive to moisture. Exposure to water causes the fibres to swell and weakens their bond to the polymer matrix, thus deteriorating the composite's strength. Plastination is a new process that removes water from natural fibres and replaces it with a polymer-improving their resistance to moisture degradation. The process steps include dehydration where the water is replaced with acetone, forced polymer impregnation where the acetone is replaced by a polymer using vacuum pressure, and polymer curing. It has been shown that this process can reduce the degradation in mechanical properties of bamboo after soaking in water. Although these results are promising, plastination is lengthy, complex, and in need of optimization. In this work, the methods used to optimize the plastination process for treating bamboo will be presented, along with experimental results. Both the polymer curing, and dehydration steps have been improved by changing to a different polymer and using a higher process temperature, respectively. The forced polymer impregnation step was improved by lowering the vacuum pressure more quickly. Future work will focus on comparing the mechanical and physical properties of untreated bamboo samples with those of samples treated with the optimized plastination process. As well, the preliminary results from a feasibility study on the plastination of flax woven fabrics will be discussed.
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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 | 0.000 | 0.000 |
| 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".