Effect of Nanoparticle Size on Cysteine‐Gold Surface Interactions
Bibliographic record
Abstract
Abstract Gold nanoparticles (AuNPs) are considered for biomedical applications, and their size influences their effectivity and stability in the human body. This study investigates the interactions between citrate‐stabilized AuNPs (5, 10, 15, and 20 nm) and L‐Cysteine (Cys). The interactions were probed by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), cyclic voltammetry (CV), dynamic light scattering (DLS), and X‐ray absorption spectroscopy (XAS). Hydrogenated gold cysteine thiolate molecular ions, gold‐sulfur ions, and Au 3 +/− , as gold atom representatives, were all detected for the different sizes. Smaller intensity ratios of the gold‐cysteine‐related peaks versus the gold reference peaks were observed with increasing AuNP size. CV confirmed stronger interactions of smaller AuNPs with Cys. AuNPs bond strongest to the thiol group, followed by the amino group, while no gold‐carboxyl interactions were probed. The nonspecific properties of the smallest‐sized (5 nm) AuNPs stabilized (less aggregation) by the presence of Cys based on XAS, but all nanoparticle sizes showed more agglomeration in aqueous solution in the presence of Cys based on DLS. The data confirmed that the strength of the binding between AuNPs and Cys is size‐dependent, possibly caused by curvature, surface energy, and/or diffusion processes.
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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) | 0.000 | 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) | 0.001 | 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".