Fullerene-like amorphous carbon nitride film surface properties and evaluating the initial adsorption kinetics of albumin and fibrinogen
Bibliographic record
Abstract
Fullerene-like amorphous carbon nitride films (FL-CNx) were prepared onto gold substrates by hot-wire plasma graphite sputtering using different %N2 in the Ar plasma discharge. Atomic force microscopy measurements revealed ultra-smooth films with a root mean squared roughness (RRMS) values in the range of 1.2–2.1 nm. The N/C and O/C atomic ratios were evaluated from the near-edge X-ray absorption fine structure (NEXAFS) spectra obtained for the carbon, nitrogen, and oxygen K-edge regions from scanning transmission X-ray microscopy measurements. Nitrogen-incorporation into the films showed very subtle changes in the electronic structure for films prepared by different %N2 plasma discharge gas. Contact angle showed and increase in surface wettability while Raman spectroscopy measurements showed an increase in sp2 ordered rings with nitrogen incorporation into the films, but both film properties reverse when the plasma gas discharge contained 30% N2. The binding kinetics of human serum albumin (HSA) and fibrinogen (Fib) were evaluated by surface plasmon resonance. In general, binding affinity was controlled by the rate of association kinetics ( ka) for both proteins, as the rate of dissociation kinetics ( kd) was approximately the same for FL-CNx films. The ka value for Fib was approximately 6–20 times larger compared to HSA, but nitrogen incorporated films initially lowered the ka values for both proteins, but too much %N2 plasma discharge gas increased ka. It was also demonstrated that pre-saturating an amorphous carbon surface with HSA decreased the surface capacity of Fib by approximately 34-fold.
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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 | 0.000 |
| 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".