Influence of Particle Size on the Binding Activity of Proteins Adsorbed onto Gold Nanoparticles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We used optical extinction spectroscopy to study the structure of proteins adsorbed onto gold nanoparticles of sizes 5-60 nm and their resulting biological binding activity. For these studies, proteins differing in size and shape, with well-characterized and specific interactions-rabbit immunoglobulin G (IgG), goat anti-rabbit IgG (anti-IgG), Staphylococcal protein A, streptavidin, and biotin-were used as model systems. Protein interaction with gold nanoparticles was probed by optical extinction measurements of localized surface plasmon resonance (LSPR) of the gold nanoparticles. Binding of the ligands in solution to protein molecules already immobilized on the surface of gold causes a small but detectable shift in the LSPR peak of the gold nanoparticles. This shift can be used to probe the binding activity of the adsorbed protein. Within the context of Mie theory calculations, the thickness of the adsorbed protein layer as well as its apparent refractive index is shown to depend on the size of the gold nanoparticle. The results suggest that proteins can adopt different orientations that depend on the size of the gold nanospheres. These different orientations, in turn, can result in different levels of biological activity. For example, we find that IgG adsorbed on spheres with diameter ≥20 nm does not bind to protein A. This study illustrates the principle that the size of nanoparticles can strongly influence the binding activity of adsorbed proteins. In addition to the importance of this in cases of direct exposure of proteins to nanoparticles, the results have implications for proteins adsorbed to materials with nanometer scale surface roughness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it