Plasmon-Enhanced Triplet–Triplet Annihilation Using Silver Nanoplates
Bibliographic record
Abstract
Photon upconversion processes have attracted substantial interest as a means of circumventing the Shockley–Queisser limit for single-junction photovoltaic devices. Despite this promise, the quantum yield of most upconversion processes is very low at the light intensities typical of solar radiation (∼100 mW/cm 2 ). Additionally, bimolecular upconversion processes that rely on molecular diffusion (e.g., triplet–triplet annihilation) typically see further reductions in quantum yield when the upconverting chromophores are confined to a solid state or thin film matrix. Here we report a plasmon-based enhancement of the triplet–triplet annihilation process when silver nanoplates are embedded in poly(methyl methacrylate) thin films containing the upconverting materials palladium(II) octaethylporphyrin and 9,10-diphenylanthracene. The silver nanoplates are synthesized with localized surface plasmon resonance bands tailored to overlap strongly with the Q-band of the porphyrin, leading to enhanced light absorption within the film and higher overall triplet concentrations. Optimization of the silver nanoplate loading leads to a nearly 10-fold increase in the upconverted light intensity compared with control samples containing no silver.
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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".