Creatinine adsorption capacity of electrospun polyacrylonitrile (<scp>PAN</scp>)‐zeolite nanofiber membranes for potential artificial kidney applications
Bibliographic record
Abstract
ABSTRACT Innovative dialysis membranes are needed for dialysis, which is the primary treatment for patients with end stage renal disease. In this study, we developed a polyacrylonitrile zeolite nanofiber composite membrane using an electrospinning process to adsorb uremic toxins through molecular sieve mechanism. Scanning electron microscope images revealed that the average diameter of the fiber fabricated with 10 wt % polyacrylonitrile was 673 nm and that of polyacrilonitirle‐zeolite membranes were 277−419 nm. The creatinine adsorption behavior of 500‐KOA (L), 720‐KOA (Farrierite), 840‐NHA (ZSM‐5), and 940‐HOA (Beta) zeolite powders were investigated. Among all the zeolites, 940‐HOA zeolites showed the best performance. The creatinine adsorption capacity of 940‐zeolite powders increased from 2234 µg/g in 50 µmol/L creatinine solution to 25423 µg/g in 625 µmol/L creatinine solution. The speed of adsorption was very quick; 0.025 g of 940‐zeolite powders can eliminate 91% of 2 µmol creatinine in 5 min. The zeolites incorporated inside the membrane had higher creatinine adsorption capacity than free zeolites. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42418.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".