Silver Incorporation into Gold Patchy Nanoparticles: Influence on the Tunability and Long-Term Stability of Plasmonic Properties
Bibliographic record
Abstract
This is the preprint of the following published manuscript "Silver Incorporation into Gold Patchy Nanoparticles: Influence on the Tunability and Long-Term Stability of Plasmonic Properties". The preprint is the submitted version of the manuscript. Abstract Gold patchy nanoparticles, anisotropic structures in which a thin gold patch partially coats a spherical, dielectric core, exhibit tunable optical resonances but often succumb to morphological changes over time. Here, we address this challenge by incorporating silver post-synthesis in a continuous-flow process that can adjust and preserve the plasmonic properties. Using a triple cascade T-mixer, our synthesis proceeds in three sequential steps: (1) seeding positively charged polystyrene cores with 3–6 nm gold nanocrystals, (2) growing gold patches under high-chloride conditions and with a large excess of the reducing agent ascorbic acid to promote dense lateral spreading of gold, and (3) adding silver nitrate only after the gold patches have fully formed. We show that silver incorporation occurs over at least 45 minutes, influenced by the residual reducing agent, the interplay between soluble silver chloride complexes, and the partial precipitation–redissolution of AgCl. By adjusting the relative amounts of gold and silver precursors, we independently tune patch dimensions, composition, and optical properties. High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and energy-dispersive X-ray spectroscopy (STEM-EDX) confirm the spatial distribution of silver, including its enrichment at the patch surface and underside. Significantly, a silver mole fraction of 0.15 delivers long-term stability, where the patch shape remains constant and the localized surface plasmon resonance (LSPR) position does not shift over four months. Lower silver fractions fail to suppress gradual restructuring, while much higher silver contents lead to the dissolution of silver-rich regions and a red-shift in the LSPR. Overall, this post-synthetic approach to incorporating silver in gold patchy nanoparticles ensures both enhanced tunability and improved stability, paving the way for advanced photonic and sensing applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".