Effect of fiber length and treatments on the hygroscopic properties of milkweed fibers for superabsorbent applications
Bibliographic record
Abstract
This study investigated the influence of pre-treatment methods and size distribution on the hygroscopic properties of milkweed fibers (MW). Pre-treatment methods included hydrothermal treatment and hybrid treatment, which involves the hydrothermal treatment followed by the alkaline treatment. Scanning electron microscope (SEM), Fourier transform infrared (FTIR), and X-ray diffraction (XRD) were used to investigate the effect of pre-treatments on morphology, chemical composition, and crystallinity of milkweed fibers, respectively. Brunauer–Emmett–Teller (BET) surface area analysis was also used to measure the surface area of milkweed fibers after each pre-treatment. The results revealed that only after 5 min of stirring, hydrothermal treatment and hybrid treatment enhanced the water absorption capacity of non-treated MW fibers from 18 ± 4 g/g to 54 ± 4 g/g and 50 ± 1 g/g, respectively. The SEM and FTIR confirmed the removal of waxes from the surface of milkweed fibers after applying hydrothermal treatment without causing any changes to the hollow structure of fibers. Hydrothermal treatment improved hydrophilicity and hence better hygroscopic characteristics of MW fibers, as evidenced by increased absorption capacity and better water retention property. The hybrid treatment, on the other hand, caused the hollow structure of MW fibers to collapse completely, and left holes on the surface. As confirmed by FTIR and XRD, this occurred because of the dissolution of lignin and hemicellulose. As a result of these severe morphological alterations, the hybrid treated MW fibers showed inferior water retention properties compared to the hydrothermally treated sample. Further, it was observed that MW fibers with larger size distribution exhibited higher water absorption capacity and better water retention properties compared to the smaller fibers. Therefore, it can be concluded that hydrothermal treatment can improve the hydrophilicity of MW fibers and pave the way for their application as superabsorbent materials in a simple and cost-effective manner.
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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) | 0.001 | 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".