Panchromatic Enhancement of Light-Harvesting Efficiency in Dye-Sensitized Solar Cells Using Thermally Annealed Au@SiO<sub>2</sub> Triangular Nanoprisms
Bibliographic record
Abstract
Plasmonic enhancement is an attractive method for improving the efficiency of dye-sensitized solar cells (DSSCs). Plasmonic materials with sharp features, such as triangular metal nanoparticles, show stronger plasmonic effects than their spherical analogues; however, these nanoparticles are also often thermally unstable. In this work, we investigated the thermal stability of Au@SiO2 triangular nanoprisms by annealing at different temperatures. Morphological changes were observed at temperatures greater than 250 °C, which resulted in a blue shift of the localized surface plasmon resonance (LSPR). Annealing at 450 °C led to a further blue shift; however, this resulted in better overlap of the LSPR with the absorption spectrum of black dye. By introducing 0.05% (w/w) Au@SiO2 nanoprisms into DSSCs, we were able to achieve a panchromatic enhancement of the light-harvesting efficiency. This led to a 15% increase in the power conversion efficiency from 3.9 ± 0.6% to 4.4 ± 0.4%.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".