Plasmonic Enhancement of Dye Sensitized Solar Cells in the Red-to-near-Infrared Region using Triangular Core–Shell Ag@SiO<sub>2</sub> Nanoparticles
Bibliographic record
Abstract
Recently, plasmonic metal nanoparticles have been shown to be very effective in increasing the light harvesting efficiency (LHE) of dye-sensitized solar cells (DSSCs). Most commonly, spherical nanoparticles composed of silver or gold are used for this application; however, the localized surface plasmon resonances of these isotropic particles have maxima in the 400-550 nm range, limiting any plasmonic enhancements to wavelengths below 600 nm. Herein, we demonstrate that the incorporation of anisotropic, triangular silver nanoprisms in the photoanode of DSSCs can dramatically increase the LHE in the red and near-infrared regions. Core-shell Ag@SiO2 nanoprisms were synthesized and incorporated in various quantities into the titania pastes used to prepare the photoanodes. This optimization led to an overall 32 ± 17% increase in the power conversion efficiency (PCE) of cells made using 0.05% (w/w) of the Ag@SiO2 composite. Measurements of the incident photon-to-current efficiency provided further evidence that this increase is a result of improved light harvesting in the red and near-infrared regions. The effect of shell thickness on nanoparticle stability was also investigated, and it was found that thick (30 nm) silica shells provide the best protection against corrosion by the triiodide-containing electrolyte, while still enabling large improvements in PCE to be realized.
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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".