Quantitative Analysis of Thiolated Ligand Exchange on Gold Nanoparticles Monitored by <sup>1</sup>H NMR Spectroscopy
Bibliographic record
Abstract
We use nuclear magnetic resonance spectroscopy methods to quantify the extent of ligand exchange between different types of thiolated molecules on the surface of gold nanoparticles. Specifically, we determine ligand density values for single-moiety ligand shells and then use these data to describe ligand exchange behavior with a second, thiolated molecule. Using these techniques, we identify trends in gold nanoparticle functionalization efficiency with respect to ligand type, concentration, and reaction time as well as distinguish between functionalization pathways where the new ligand may either replace the existing ligand shell (exchange) or add to it ("backfilling"). Specifically, we find that gold nanoparticles functionalized with thiolated macromolecules, such as poly(ethylene glycol) (1 kDa), exhibit ligand exchange efficiencies ranging from 70% to 95% depending on the structure of the incoming ligand. Conversely, gold nanoparticles functionalized with small-molecule thiolated ligands exhibit exchange efficiencies as low as 2% when exposed to thiolated molecules under identical exchange conditions. Taken together, the reported results provide advances in the fundamental understanding of mixed ligand shell formation and will be important for the preparation of gold nanoparticles in a variety of biomedical, optoelectronic, and catalytic applications.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".