Tunable Loading of Single-Stranded DNA on Gold Nanorods through the Displacement of Polyvinylpyrrolidone
Bibliographic record
Abstract
A quantitative and tunable loading of single-stranded (ss-DNA) molecules onto gold nanorods was achieved through a new method of surfactant exchange. This new method involves the exchange of cetyltrimethylammonium bromide surfactants for an intermediate stabilizing layer of polyvinylpyrrolidone and sodium dodecylsulfate. The intermediate layer of surfactants on the anisotropic gold particles was easily displaced by thiolated ss-DNA, forming a tunable density of single-stranded DNA molecules on the surfaces of the gold nanorods. The success of this ligand exchange process was monitored in part through the combination of extinction, X-ray photoelectron, and infrared absorption spectroscopies. The number of ss-DNA molecules per nanorod for nanorods with a high density of ss-DNA molecules was quantified through a combination of fluorescence measurements and elemental analysis, and the functionality of the nanorods capped with dense monolayers of DNA was assessed using a hybridization assay. Core-satellite assemblies were successfully prepared from spherical particles containing a probe DNA molecule and a nanorod core capped with complementary ss-DNA molecules. The methods demonstrated herein for quantitatively fine tuning and maximizing, or otherwise optimizing, the loading of ss-DNA in monolayers on gold nanorods could be a useful methodology for decorating gold nanoparticles with multiple types of biofunctional molecules.
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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 | 0.000 | 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 | 0.001 |
| 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".