Real-Time Study of the Adsorption and Grafting Process of Biomolecules by Means of Bloch Surface Wave Biosensors
Bibliographic record
Abstract
A combined label-free and fluorescence surface optical technique was used to quantify the mass deposited in binary biomolecular coatings. These coatings were constituted by fibronectin (FN), to stimulate endothelialization, and phosphorylcholine (PRC), for its hemocompatibility, which are two properties of relevance for cardiovascular applications. One-dimensional photonic crystals sustaining a Bloch surface wave were used to characterize different FN/PRC coatings deposited by a combination of adsorption and grafting processes. In particular, the label-free results permitted to quantitatively assess the mass deposited in FN adsorbed (185 ng/cm 2 ) and grafted (160 ng/cm 2 ). PRC binding to grafted FN coatings was also quantified, showing a coverage as low as 10 and 12 ng/cm 2 for adsorbed and grafted PRC, respectively. Moreover, desorption of FN deposited by adsorption was detected and quantified upon the addition of PRC. The data obtained by the surface optical technique were complemented by water contact angle and X-ray photoelectron spectroscopy (XPS) analyses. The results were in accordance with those obtained previously by qualitative and semiquantitative techniques (XPS, time-of-flight secondary ion mass spectrometry) on several substrates (PTFE and stainless steel), confirming that grafted FN coatings show higher stability than those obtained by FN adsorption.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".