Fenton-Based Treatment of Flax Biomass for Modification of Its Fiber Structure and Physicochemical Properties
Bibliographic record
Abstract
The availability of a sustainable technique for degumming lignocellulose fibers is a challenge for the fiber processing industry. Removal of non-cellulosic content from lignocellulose fibers is essential for improving their mechanical and chemical properties, which makes the fibers more suitable for various applications. Herein, a catalytic Fenton-based oxidation process was employed to isolate microcellulose fibers from raw flax fibers. Various complementary methods such as FT-IR/NMR spectroscopy and TGA were used to obtain insight into the thermal behavior of the treated fibers. The morphology of the fibers was studied using Scanning Electron Microscopy (SEM), whereas the surface chemical properties of the fibers was evaluated by a dye-based adsorption method, along with a potentiometric point-of-zero-charge method. To obtain fibers with suitable properties, such as uniform fiber diameter, several Fenton reaction parameters were optimized: pH (7), reaction time (15 h), iron sulfate (2 wt.%), and hydrogen peroxide (10 wt.%). The results indicate that, under the specified conditions, the average diameter of the raw fibers (12.3 ± 0.5 µm) was reduced by 58%, resulting in an average diameter of 5.2 ± 0.3 µm for the treated fibers. We demonstrate that the treated fibers had a lower dye adsorption capacity for methylene blue, consistent with the smoother surface features of the treated fibers over the raw flax fibers. Overall, this study contributes to utilization of the Fenton reaction an efficient oxidation technique for the production of lignocellulose fibers with improved physicochemical properties, such as reduced fiber diameter distribution, in contrast with traditional alkali-based chemical treatment.
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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".