Surface characterization of hollow fiber membranes used in artificial kidney
Bibliographic record
Abstract
Abstract The internal and external curved surfaces of polysulfone hollow fiber membranes were characterized by atomic force microscopy (AFM), contact angle measurement (CAM), and scanning electron microscopy (SEM) with the aim of improving the membrane surface properties for blood compatibility. Novel approaches were applied to evaluate a number of properties, including the roughness, pore size, nodule size, and wettability of the surfaces of the hollow fibers. CAM studies were carried out by directly observing the liquid meniscus at the surfaces of hollow fibers. Observation of the meniscus and measurement of the contact angle became possible by using an imaging system developed in our laboratory. AFM and SEM studies were also conducted on the surfaces of the hollow fiber membranes by cutting them at an inclined angle. The effect of the molecular weight of poly(ethylene glycol) (PEG) in the polymer blend on the surface properties of the hollow fibers was studied. Increasing the PEG molecular weight increased the average pore size whereas it decreased the contact angle. The contact angle depended on the microscopic surface morphology, including nodule size and roughness parameters. The theoretical prediction along with the experimental data showed that the measured contact angle would be greater than the value intrinsic to the membrane material because of the formation of composite surface structures. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4386–4400, 2006
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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) | 0.000 | 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 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".