Core-satellite assembly of gold nanoshells on solid gold nanoparticles for a color coding plasmonic nanosensor
Bibliographic record
Abstract
the layer-by-layer assembly of nanoparticles linked by DNA. We used finite-difference time-domain simulations to help guide the geometrical design, and characterized the optical properties and morphology of the solid-shell nanoparticle assemblies using darkfield microscopy, single-nanostructure spectroscopy, and scanning electron microscopy. Plasmon coupling yielded resonant peaks at longer wavelengths in the red to near-infrared range for solid-shell assemblies compared with solid-solid nanoparticle assemblies. We examined sensing with the solid-shell assemblies using adenosine triphosphate (ATP) as a model target and ATP-aptamer as the linker. Binding of ATP induced disassembly and led to a decrease in the scattering intensity and a color change from red to green. The new morphology of the core-satellite assembly enabled plasmonic color-coding of multiplexed sensors. We demonstrate this potential by fabricating two types of assemblies using DNA linkers that target different molecules - ATP and a model nucleic acid. Our work expands the capability of chip-based plasmonic nanoparticle assemblies for the analysis of multiple, different types of biomolecules in small sample sizes including the microenvironment and single cells.
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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 | 0.000 |
| 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".