Hydration and Sorption Properties of Raw and Milled Flax Fibers
Bibliographic record
Abstract
Abstract The physicochemical and hydration properties of mechanically modified flax fibers (FFs) were investigated herein. Raw flax fibers (FF-R) were ball-milled and sieved through mesh with various aperture sizes (420, 210, and 125 μm) to achieve modified samples, denoted as FF-420, FF-210, and FF-125, respectively. The physicochemical and hydration properties of FF-R with variable particle sizes were characterized using several complementary techniques: microscopy (SEM), spectroscopy (FT-IR, XRD, and XPS), thermoanalytical methods (DSC and TGA), adsorption isotherms using gas/dye probes, and solvent swelling studies in liquid H2O. The hydration of FF biomass is governed by the micropore structure and availability of active surface sites, as revealed by the adsorption isotherm results and the TGA/DSC profiles of the hydrated samples. Gravimetric water swelling, water retention values, and vapor adsorption results provide further support that particle size reduction of FF-R upon milling parallels the changes in surface chemical and physicochemical properties relevant to adsorption/hydration in the modified FF materials. This study outlines a facile strategy for the valorization and tuning of the physicochemical properties of agricultural FF biomass via mechanical treatment for diverse applications in biomedicine, energy recovery, food, and biosorbents for environmental remediation.
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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".