Discrimination of Proteins Using an Array of Surfactant-Stabilized Gold Nanoparticles
Bibliographic record
Abstract
Protein analysis is a fundamental aspect of biochemical research. Gold nanoparticles are an emerging platform for various biological applications given their high surface area, biocompatibility, and unique optical properties. The colorimetric properties of gold nanoparticles make them ideal for point-of-care diagnostics. Different aspects of gold nanoparticle-protein interactions have been investigated to predict the effect of protein adsorption on colloidal stability, but the role of surfactants is often overlooked, despite their potential to alter both protein and nanoparticle properties. Herein we present a method by which gold nanoparticles can be prepared in various surfactants and used for array-based quantification and identification of proteins. The exchange of surfactant not only changed the zeta potential of those gold nanoparticles but also drastically altered their aggregation response to five different proteins (bovine serum albumin, human serum albumin, immunoglobulin G, lysozyme, and hemoglobin) in a concentration-dependent manner. Finally, we demonstrate that varying surfactant concentration can be used to control assay sensitivity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".