Thermal degradation mechanism of triangular Ag@SiO<sub>2</sub>nanoparticles
Bibliographic record
Abstract
Triangular silver nanoparticles are promising materials for light harvesting applications because of their strong plasmon bands; these absorption bands are highly tunable, and can be varied over the entire visible range based on the particle size. A general concern with these materials is that they are unstable at elevated temperatures. When thermally annealed, they suffer from changes to the particle morphology, which in turn affects their optical properties. Because of this stability issue, these materials cannot be used in applications requiring elevated temperatures. In order to address this problem, it is important to first understand the degradation mechanism. Here, we measure the changes in particle morphology, oxidation state, and coordination environment of Ag@SiO2 nanotriangles caused by thermal annealing. UV-vis spectroscopy and TEM reveal that upon annealing the Ag@SiO2 nanotriangles in air, the triangular cores are truncated and smaller nanoparticles are formed. Ag K-edge X-ray absorption spectroscopy (XANES and EXAFS) shows that the small particles consist of Ag(0), and that there is a decrease in the Ag-Ag coordination number with an increase in the annealing temperature. We hypothesize that upon annealing Ag in air, it is first oxidized to AgxO, after which it subsequently decomposes back to well-dispersed Ag(0) nanoparticles. In contrast, when the Ag@SiO2 nanotriangles are annealed in N2, since there is no possibility of oxidation, no small particles are formed. Instead, the triangular core rearranges to form a disc-like shape.
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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 | 0.000 | 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".