Characterization and optimization of alkali-treated yushania alpina bamboo fiber properties: case study of ethiopia species
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The mechanical properties of single Yushania alpina bamboo fibers have not been explored. This is a serious limitation on their application. The main purpose of this work is to fill up information gaps to prepare for the growing usage of Ethiopian bamboo in a variety of applications. This study looks at the characterization and optimization of Y.alpina bamboo fiber properties extracted both chemically and mechanically. Using response surface methodology (RSM) the mechanical properties were optimized and linear, quadratic and interaction of independent variables were determined. Samples of length 25–30 cm were harvested at various ages from the middle of the stem which was then soaked in different NaOH concentrations weight by volume for different times. Using a rolling machine that has three rollers, the fiber is mechanically extracted. The optimal mechanical properties were observed at plant age of 1.8 years, alkali concentration of 10%, and a soaking duration of 2.0 days. The model is significant ( P ≤ 0.005) with a 95% confidence level for predicted values that were closer to the measured values, indicating that the model's fit to the measured properties was strong at the optimized values. The optimized points of age and soaking duration ware subjected to chemical, thermal and morphological analysis for each corresponding NaOH Concentration (6, 12, and 18%) levels. Scanning electron microscopy (SEM) was employed to examine the microstructure of the fibers and discovered that the 18% NaOH treated fiber resulted in more wrinkles in the surface of bamboo fibers when compared with the 6 and 12%NaOH Bamboo fiber. Using thermogravimetric analysis (TGA) and differential thermal gravimetric (DTG), the study investigated weight loss increased as alkali concentration increased but the scenario functioned for proper concentration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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