Isolation of Cellulose Nanofibers from <i>Oryza sativa</i> Residues via TEMPO Mediated Oxidation
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) or cellulose nanofibers (CNFs) with different morphologies, chemical, mechanical and physical properties can be obtained when microcrystalline cellulose is subjected to enzymatic, chemical or mechanical treatment. With the aim of utilizing cellulose nanofibrils (CNFs) from Oryza sativa, we isolated microcrystalline cellulose using the Kraft process, followed by successive fiber fibrillation using mechanical grinding, then (2,2,6,6-Tetramethylpiperidin-1-yl)oxyl (TEMPO) mediated oxidation. Analysis of pulp fibers obtained after each treatment step revealed that fiber properties such as length, crystallinity and crystal size changed when the pulp was subjected to mechanical grinding, ultrasonication and TEMPO mediated oxidation. The degree of crystallinity of the fibers increased while crystal size and fiber length decreased after each treatment. TEMPO mediated oxidation led to a decrease in fiber length and an increase in degree of crystallinity of the fibers as compared to mechanical treatment and ultrasonication. It further introduced carboxyl functional groups (COOH) on the surface of the fibrils, which implies that the nanofibers obtained in this study could be further functionalized. Hence, TEMPO mediated oxidation offers the possibility of further chemical functionalization of cellulose nanofibers isolated from agricultural residues.
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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".