Photochemical Synthesis of Monodisperse Size-Controlled Silver Decahedral Nanoparticles and Their Remarkable Optical Properties
Bibliographic record
Abstract
Monodisperse decahedral silver nanoparticles have been synthesized with excellent shape selectivity (>99%) by novel photochemical transformation of aqueous silver nanoparticle precursors. The procedure employs intense white light and is very robust and reproducible. The precursor solution transforms from a mixture of shapes dominated by small silver platelets into the decahedra, driven by superior stability of decahedral seeds. The decahedra size can be varied by adjusting intensity and spectral properties of the irradiating light. Furthermore, the decahedra can be controllably photochemically regrown to larger sizes, while fully preserving the monodispersity. Silver decahedra exhibit remarkable optical properties featuring a bicolored appearance due to the interplay between plasmonic adsorption and scattering. The sharp plasmon resonances of silver decahedra were tunable from 455 to 570 nm through size variation. Finally, silver decahedra exhibited greatly superior enhancement of Raman scattering compared to other silver nanoparticles. Overall, our findings highlight the importance of pentagonal symmetry in metal nanoparticles and offer a powerful general approach to monodisperse shapes via selective regrowth of appropriately identified and stabilized intermediates.
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".