Quantifying Surface Coverage of Colloidal Silica by a Cationic Peptide Using a Combined Centrifugation/Time-Resolved Fluorescence Anisotropy Approach
Bibliographic record
Abstract
Recent experimental studies have shown that time-resolved fluorescence anisotropy (TRFA) is a promising methodology for in situ characterization of the surface modification of aqueous silica nanocolloids. Here we provide a more fundamental insight into the principle of this approach and discuss how the adsorption parameters for a cationic peptide, Lys-Trp-Lys (denoted using the standard shortform KWK), onto Ludox nanoparticles (NPs) are linked to the rotational dynamics of rhodamine 6G (R6G) dispersed in the KWK/Ludox mixture. First, the adsorption isotherm of KWK on hydrophilic controlled pore glass (CPG-3000) was obtained using the traditional centrifugation method, which provides the total molar amount of KWK per unit surface area of the silica. Assuming that both CPG and Ludox particles possess identical surface properties when suspended in the same aqueous buffer, both materials should also have identical adsorption properties. Thus, the adsorbed amount of KWK per unit area at a given total KWK concentration, as determined by the centrifugation method, can be plotted against the fractions of R6G anisotropy decay components at the same KWK concentration to relate the anisotropy components to the absolute surface coverage. Using this approach, it was determined that the concentration of KWK at which the CPG surface was saturated corresponded to the condition g = 0 in the R6G decay, where g is the fraction of the nondecaying anisotropy component. This condition means that there is no R6G bound to the fraction of Ludox NPs with a radius R > 2.5 nm at maximum KWK coverage, consistent with the adsorbed peptide forming a continuous layer on the Ludox surface. Hence, the g value obtained from TRFA analysis can be used to assess the absolute surface coverage of monolayer coatings on colloidal nanoparticles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".