Comparative Analysis of Cellulose, Hemicellulose and Lignin on The Physical and Thermal Properties of Wood Sawdust for Bio-Composite Material Fillers
Bibliographic record
Abstract
This research compares the physical and thermal characteristics of three kinds of wood sawdust applied to bio-composites filler.Wood Sawdust of Sengon (softwood), Pine (softwood), and Teak (hardwood) have a crystalline structure (cellulose).The hydrochloric acid test found cellulose and other lignocellulosic content, such as hemicellulose and lignin.It contributed to the plant's strength.Adding a good filler in the polymer as a matrix with a high cellulose composition can increase the inter-mechanical bonding of bio-composites.Sengon sawdust has 48.98% cellulose content and contributes to the highest crystallinity index of 52.8%, calculated by the X-ray diffraction test.A high aspect ratio (L/D) on the bio-composite positively impacted the mechanical strength of bio-composite materials.The Scanning Electron Microscope (SEM) can show the morphology and calculate the aspect ratio of wood sawdust.Aspect ratio of wood sawdust from high to low i.e.Sengon (5.8), Pine (3.9), and Teak (1.5), respectively.Fourier transform infrared (FTIR) test to detect the twelve absorbance frequencies of the cellulose, hemicellulose, and lignin.Thermal degradation of all wood sawdust has the same initial degradation temperature (Tonset) by 255℃ and maximum degradation temperature (Tmax2) by 300℃.
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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".