Continuous Flow Synthesis and Simulation-Supported Investigation of Tunable Plasmonic Gold Patchy Nanoparticles
Bibliographic record
Abstract
This is the dataset for the following manuscript "Continuous Flow Synthesis and Simulation-Supported Investigation of Tunable Plasmonic Gold Patchy Nanoparticles". Abstract Plasmonic nanoparticles have intriguing optical properties which make them suitable candidates for sensing or theranostic applications. Anisotropic patchy particles, where metal is locally deposited on the surface of a core particle, exhibit plasmon resonances that can be specifically adjusted for these applications. However, many existing synthesis routes are complex, yield too little material, or provide particles with limited optical tunability. In this work, we present a simple and scalable continuous flow synthesis of gold-on-polystyrene patchy particles with widely adjustable optical properties. By increasing the chloride concentration in the electroless deposition of gold, we slow down the redox reduction kinetics and obtain a dense patch morphology as well as a reduced nucleation rate. The latter is counteracted by introducing a low-level seeding approach where a small number of gold nanocrystals heterocoagulate with the core particles prior to patch growth. Seeding and patch growth are performed in a continuous flow set-up with two T-shaped milli-mixers. The resulting patchy particle samples exhibit a tunable dipolar plasmon peak between 600 nm and 1100 nm. We also investigate the structure-property relationship for our gold patchy particles using Finite Element Method simulations. After identifying a suitable patch shape model, we elucidate the influence of individual geometric parameters on the optical properties and show that the relationship holds true for a large range of patch coverages. Finally, we apply the relationship to explain the time-dependent change in the optical properties of as-synthesized patches by correlating it with the patch shape transformation revealed by electron microscopy. The uploaded data are sorted by figure.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".