Plasmonic Nanocrystal Solar Cells Utilizing Strongly Confined Radiation
Bibliographic record
Abstract
The ability of metal nanoparticles to concentrate light via the plasmon resonance represents a unique opportunity for funneling the solar energy in photovoltaic devices. The absorption enhancement in plasmonic solar cells is predicted to be particularly prominent when the size of metal features falls below 20 nm, causing the strong confinement of radiation modes. Unfortunately, the ultrashort lifetime of such near-field radiation makes harvesting the plasmon energy in small-diameter nanoparticles a challenging task. Here, we develop plasmonic solar cells that harness the near-field emission of 5 nm Au nanoparticles by transferring the plasmon energy to band gap transitions of PbS semiconductor nanocrystals. The interfaces of Au and PbS domains were designed to support a rapid energy transfer at rates that outpace the thermal dephasing of plasmon modes. We demonstrate that central to the device operation is the inorganic passivation of Au nanoparticles with a wide gap semiconductor, which reduces carrier scattering and simultaneously improves the stability of heat-prone plasmonic films. The contribution of the Au near-field emission toward the charge carrier generation was manifested through the observation of an enhanced short circuit current and improved power conversion efficiency of mixed (Au, PbS) solar cells, as measured relative to PbS-only devices.
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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".