Influence of zeolite shape and particle size on their capacity to adsorb uremic toxin as powders and as fillers in membranes
Bibliographic record
Abstract
Abstract Membranes with zeolites are promising for performing blood dialysis because zeolites can eliminate uremic toxins through molecular sieving. Although the size and the shape of zeolite particles can potentially influence the performance of the membranes with respect of creatinine uptake level, it is not clear what sizes and shapes lead to better performance. In this paper, we carry out experiments to answer this question. Spherical microparticle 840, spherical nanoparticle P‐87 and rod‐like nanoparticle P‐371 zeolites were chosen to be used in all the experiments. Their creatinine uptake levels were first measured as powders in creatinine solutions with different concentrations, volumes and adsorption times. Then, nanofibrous membranes with zeolites were electrospun and their ability to adsorb creatinine was measured and compared against their respective powders' creatinine uptake level. The experiment shows that the zeolites have similar creatinine uptake ability as powders. However, they have significantly different creatinine uptake ability after being incorporated inside the membranes. Spherical microparticle 840 in the membrane presented the best creatinine uptake ability, at 8957 µg g−1, which was half of its powders'. On the other hand, P‐87 presented largely decreased, while P‐371 presented even lower creatinine uptake ability in membranes when compared to respective powders'. The results shows that microparticle and sphere shaped particles perform better inside the membranes. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 1594–1601, 2017.
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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 | 0.001 |
| 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".