Plasmonic Photovoltaics Enabled By Single-Crystal Metal Films and Nanostructures
Bibliographic record
Abstract
In contrast to conventional photovoltaic devices which rely on bulk semiconductor material absorption and separation of electron-hole pairs, surface plasmon-based solar energy harvesting employs rectifying metal/dielectric interfaces to capture light and separate charges. Here, we describe the requirements for efficient hot electron extraction in plasmonic photovoltaic devices and demonstrate a new scalable and environmentally friendly electroless deposition method for single crystal epitaxial noble metals films and nanostructures. The method produces ultra-smooth, low loss, single-crystal noble metal films ideal for subtractive patterning of nanostructures through ion beam milling, and high definition, sub-wavelength single crystal nanostructures through lithographic patterning methods. We describe the nucleation and growth of these metal films and nanostructures in the absence and presence of anionic shape-control agents and examine the role of specific anions in determining the resulting film and nanostructure morphologies via scanning electron microscopy (SEM) and high resolution transmission electron microscopy (HRTEM). These effects have been exploited to yield large area patterned, and shape-controlled nanoarrays of single crystal metal nanostructures for plasmonic and metamaterial applications. These approaches offer new and cost effective routes to achieve crystalline, shape-controlled surface nanostructure to enable efficient hot electron extraction for energy harvesting applications.
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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".