Aggregation, stabilization, and binding between bovine serum albumin and gold nanoparticles of varying sizes
Bibliographic record
Abstract
Gold nanoparticles (GNPs) of different sizes are used in various biomedical applications. We explored the interaction between differently sized (5–60 nm) citrate-coated GNPs and bovine serum albumin (BSA). Using techniques such as dynamic light scattering, zeta potential, circular dichroism (CD) spectroscopy, time-of-flight secondary ion mass spectrometry (ToF-SIMS), and synchrotron X-ray absorption spectroscopy (XAS), we demonstrate that BSA significantly enhances the colloidal stability of GNPs by preventing aggregation. Additionally, GNPs did not induce unfolding or loss of secondary structure in BSA, as confirmed by CD and ToF-SIMS, suggesting that BSA interacts with GNPs without disrupting its native conformation. Unlike previous studies on the interaction between GNPs and L-cysteine, ToF-SIMS revealed no preferential binding of gold to any specific functional groups in BSA. XAS suggested that BSA adsorption on the GNPs changed their electronic structure (replenishing electrons in the Au 5d orbital). Collectively, our results support the conclusion that BSA adsorbs onto citrate-coated GNPs through non-covalent, long-range interactions that preserve protein structure and enhance colloidal stability.
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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) | 0.000 | 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 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".