Effect of End-Tethered Methoxy-PEO Chain Density on Uremic Toxin Adsorption
Bibliographic record
Abstract
In 2023, around 850 million people globally were affected by chronic kidney disease, which leads to the retention of uremic toxins and excess fluid in the blood. This study examines the adsorption of these toxins to poly(ethylene oxide) (PEO) films, known for their low-fouling properties. The gold surfaces were treated with 5 mM end-thiolated methoxy-terminated PEO ( m -PEO) and analyzed using dynamic contact angle measurements, X-ray photoelectron spectroscopy, and spectroscopic ellipsometry to confirm the PEO film’s presence and determine chain density. The adsorption of 25 different uremic toxins to m -PEO films was evaluated by using liquid chromatography–mass spectrometry (LC/MS), focusing on their binding affinity and adsorption dynamics. Results showed the effective modification of surfaces with m -PEO, with a notable change in contact angles and chain density (∼0.5 and 0.8 chains/nm 2 ). Interestingly, pyruvic acid showed significant adsorption, whereas other toxins, such as hippuric acid, creatinine, and xanthosine had minimal interactions with the film. This indicates that the adsorption of these toxins is not primarily concentration driven and is rather dependent on the chemical structure of each toxin. These findings provide important insights for designing low-fouling coatings for biomedical devices.
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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".