Comprehensive Characterization of Raw and Oxalic Acid Treated Ripen Cellulosic Biofiber from Areca Catechu Inflorescence as Substitute for Harmful Synthetic Products
Bibliographic record
Abstract
The inherent characteristics of natural fibers include a low density and a high strength-to-weight ratio, making them promising candidates for lightweight applications. The mechanical features of these strands are prejudiced by their chemical compositions and the cross-sectional area being the most variable factor affecting strength. In this research, strands were obtained by processing the ripen inflorescence of Areca tree and subjected to treatment with an oxalic acid (C2H2O4) solution to enhance their properties. The extracted fibers underwent examination for chemical, physical, mechanical, and morphological properties. The study findings indicate that fibers treated with a 4 wt.% C2H2O4 solution for 60 min exhibit superior properties. The strands segregated from the ripen inflorescence of areca tree treated with 4 wt.% C2H2O4 solution for 60 min exhibited a rise in cellulose proportion by 26.7%, tensile strength by 13.5%, crystallinity index by 16.6%, thermal endurance by 6.3% and appreciable surface roughness compared to the untreated fibers as viewed through Scanning Electron Microscope (SEM). The Fourier Transform Infrared (FTIR) analysis endorsed the observations of chemical analysis. The characterization of areca inflorescence fibers in this study highlights significant advantages for the advancement of composite materials.
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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 | 0.001 | 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".