Gold Nanoprisms: Synthetic Approaches for Mastering Plasmonic Properties and Implications for Biomedical Applications
Bibliographic record
Abstract
In a comprehensive approach to the preparation of gold nanoprisms (AuNPRs), we first discuss three developed synthetic strategies to produce 2D planar-twinned morphologies with plasmonic control for practical applications. The developed synthetic procedures do not employ cytotoxic reagents, e.g., cetyltrimethylammonium bromide, and no shape purification is required. The primary synthesis of AuNPRs is based on iodide (or triiodide) redox mediation that enables shape selection with >99% yield. Another synthetic route involves low pH using citric acid, where 2D growth is achieved via redox equilibrium with an excess of the precursor. In a related approach, reduction with hydrogen peroxide in the presence of thiols and halides to slow the growth at the AuNPR surface enables the most efficient preparation of AuNPRs (<15 min). The main synthetic emphasis was placed on tuning of the localized surface plasmon resonance (LSPR) peaks of AuNPRs in a wide range from 540 to 1000+ nm. Having developed several synthetic procedures, we discuss important factors including crucial delays in reagent addition and the role of impurities, such as silver ions, that are disruptive for AuNPR formation even at submicromolar concentrations. On the basis of this knowledge and practical developments, general guidelines for the kinetic growth of AuNPRs are formulated to offer the synthetic protocols that are versatile and reproducible and can be readily scaled. Consequently, the developed synthetic approaches and a general understanding of the formation of AuNPRs with tunable LSPR will benefit a diverse range of researchers working with plasmonic metal nanoparticles, especially in the area of biomedical applications.
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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".