Benzoylation strategy for enhancing the light stability of kenaf fibers through lignin removal and radical scavenging
Bibliographic record
Abstract
Enhancing photostability of lignocellulosic fibers is essential for their long-term use in light exposure applications. In this study, benzoylation was applied to kenaf fibers to suppress ultraviolet (UV)-induced yellowing and improve their light fastness. Structural analyses confirmed that esterification of hydroxyl groups and partial removal of lignin were successfully achieved during the benzoylation reaction. After 500 h of UV irradiation, benzoylated kenaf (BKF) showed a distinct whitening phenomenon, in contrast to the gradual yellowing of unmodified kenaf. This whitening effect was attributed to the initial photostabilization, and the discoloration was characterized by a plateau after the initial 48 h. The results confirmed that BKF effectively inhibited the formation of light-induced free radicals and mitigated subsequent surface oxidation. In the component-specific study, lignin was identified as the primary contributor to yellowing. In addition, the photobleaching behavior of benzoylated hemicellulose closely mirrored that of BKF, suggesting its pivotal role in the whitening effect observed in BKF. These results demonstrated that the photostability of natural fibers can be effectively improved through benzoylation by removing chromophore-forming lignin and introducing aromatic ester groups that mitigate radical propagation and oxidative degradation.
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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".