Effect of Unbleached Rice Straw Cellulose Nanofibers on the Properties of Polysulfone Membranes
Bibliographic record
Abstract
In addition to their lower cost and more environmentally friendly nature, cellulose nanofibers isolated from unbleached pulps offer different surface properties and functionality than those isolated from bleached pulps. At the same time, nanofibers isolated from unbleached pulps keep interesting properties such as hydrophilicity and mechanical strength, close to those isolated from bleached pulps. In the current work, rice straw nanofibers (RSNF) isolated from unbleached neutral sulfite pulp (lignin content 14%) were used with polysulfone (PSF) polymer to make membrane via phase inversion. The effect of RSNF on microstructure, porosity, hydrophilicity, mechanical properties, water flux, and fouling of PSF membranes was studied. In addition, the prepared membranes were tested to remove lime nanoparticles, an example of medium-size nanoparticles. The results showed that using RSNF at loadings from 0.5 to 2 wt.% can significantly increase hydrophilicity, porosity, water flux, and antifouling properties of PSF. RSNF also brought about an increase in rejection of lime nanoparticles (up to 98% rejection) from their aqueous suspension, and at the same time, with increasing flux across the membranes. Tensile strength of the membranes improved by ~29% with addition of RSNF and the maximum improvement was obtained on using 0.5% of RSNF, while Young's modulus improved by ~40% at the same RSNF loading. As compared to previous published results on using cellulose nanofibers isolated from bleached pulps, the obtained results in the current work showed potential application of nanofibers isolated from unbleached pulps for improving important properties of PSF membranes, such as hydrophilicity, water flux, rejection, and antifouling properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".