Light trapping in a-Si:H thin film solar cells using silver nanostructures
Bibliographic record
Abstract
Plasmonic thin film solar cells (modified with metallic nanostructures) often display enhanced light absorption due to surface plasmon resonance (SPR). However, the plasmonic field localization may not be significantly beneficial to improved photocurrent conversion efficiency for all types of cell configurations. For instance, the integration of random metallic nanoparticles (NPs) into thin film solar cells often introduces additional texturing. This texturing might also contribute to enhanced photon-current efficiency. An experimental systematic investigation to decouple both the plasmonic and the texturing contributions is hard to realize for cells modified with randomly deposited metallic nanoparticles. This work presents an experimental and computational investigation of well-defined plasmonic (Ag) nanoparticles, fabricated by nanosphere lithography, integrated to the back contact of hydrogenated amorphous silicon (a-Si:H) solar cells. The size, shape, periodicity and the vertical position of the Ag nanoparticles were well-controlled. The experimental results suggested that a-Si:H solar cells modified with a periodic arrangement of Ag NPs (700 nm periodicity) fabricated just at the top of the metal contact in the back reflector yields the highest improvement in terms of current density (JSC). Finite-difference time-domain (FDTD) simulations also indicated that Ag nanoparticles located at the top of the metal contact in the back reflector is expected to lead to the most efficient light confinement inside the a-Si:H absorber intrinsic layer (i-layer).
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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".