LSPR Tuning from 470 to 800 nm and Improved Stability of Au–Ag Nanoparticles Formed by Gold Deposition and Rebuilding in the Presence of Poly(styrenesulfonate)
Bibliographic record
Abstract
Stability and precise control over functional properties of metal nanoparticles remain a challenge for the realization of prospective applications. Our described process of shell formation and rebuilding can address both these challenges. Template silver nanoparticles (AgNPs) stabilized by poly(styrenesulfonate) are first transformed with gold deposition, after which the resulting shell rebuilds with the replaced silver. The shell formation and rebuilding are accompanied by large shifts in localized surface plasmon resonance (LSPR) peak position, which enables LSPR tuning in a range from 470 to 800 nm. Furthermore, chemical stability of Au-AgNPs is significantly improved compared to AgNPs due to gold stability. Silver templates of different shapes and sizes were demonstrated to transform to AuAg composite NPs to further extend the accessible LSPR range tuning. Stabilization of template AgNPs with poly(styrenesulfonate), in contrast to commonly used poly(vinylpyrrolidone), was found to be a key factor for shell rebuilding. The developed Au-AgNPs were shown to be advantageous for surface plasmon resonance (SPR) detection and surface-enhanced Raman spectroscopy (SERS) owing to their tunable LSPR and enhanced stability.
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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 | 0.000 |
| 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) | 0.000 | 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".