Three-dimensional scanning near field optical microscopy (3D-SNOM) imaging of random arrays of copper nanoparticles: implications for plasmonic solar cell enhancement
Bibliographic record
Abstract
In order to investigate the suitability of random arrays of nanoparticles for plasmonic enhancement in the visible-near infrared range, we introduced three-dimensional scanning near-field optical microscopy (3D-SNOM) imaging as a useful technique to probe the intensity of near-field radiation scattered by random systems of nanoparticles at heights up to several hundred nm from their surface. We demonstrated our technique using random arrays of copper nanoparticles (Cu-NPs) at different particle diameter and concentration. Bright regions in the 3D-SNOM images, corresponding to constructive interference of forward-scattered plasmonic waves, were obtained at heights Δz ≥ 220 nm from the surface for random arrays of Cu-NPs of ∼ 60-100 nm in diameter. These heights are too large to use Cu-NPs in contact of the active layer for light harvesting in thin organic solar cells, which are typically no thicker than 200 nm. Using a 200 nm transparent spacer between the system of Cu-NPs and the solar cell active layer, we demonstrate that forward-scattered light can be conveyed in 200 nm thin film solar cells. This architecture increases the solar cell photoconversion efficiency by a factor of 3. Our 3D-SNOM technique is general enough to be suitable for a large number of other applications in nanoplasmonics.
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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) | 0.000 | 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 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".