Optimized Synthetic Protocols for Preparation of Versatile Plasmonic Platform Based on Silver Nanoparticles with Pentagonal Symmetries
Bibliographic record
Abstract
The key findings in the synthesis and transformation of silver nanoparticles with pentagonal symmetries arising from regular multiple twinning are reported, researched in the last 5 years. In a one‐stage photochemical synthesis of silver decahedral (pentagonal bipyramid, J 13 solid) nanoparticles (AgDeNPs), oxidative etching by hydrogen peroxide is implemented to achieve complete conversion of the small silver platelet precursor NPs. The concentration of hydrogen peroxide is found to be optimal at 0.2 m . Such high peroxide concentration can be rationalized by its slow reactivity in a red‐ox equilibrium with borohydride and citrate. We have also adapted light‐emitting diodes (LEDs) as a light source and documented optimal exposure time, LED power, and wavelength range for convenient laboratory synthesis of high‐purity size‐selected AgDeNPs. In the absence of platelet impurities, AgDeNPs produce by the new‐generation procedure can be conveniently re‐grown into larger sizes using silver ions as a precursor. Thermal regrowth of new‐generation AgDeNPs into pentagonal silver nanorods (AgPRNPs, J 15 solid) can be reliably accomplished with the precise variation in rod length (by varying amounts of added silver) and width (by using different seed AgDeNPs). With the reported reproducible synthetic protocols that can be readily implemented in any chemistry laboratory, AgDeNPs and AgPRNPs should serve as a versatile plasmonic platform with a precisely tunable surface plasmon resonance (SPR) from ca. 430 nm (rounded AgDeNPs) to 1100+ nm (longitudinal SPR of longer AgPRNPs). The plasmonic platform based on the reported AgNPs with pentagonal symmetries should be practical for a diverse range of applications, especially plasmonic sensing and surface‐enhanced Raman spectroscopy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".