Optical absorption enhancement in 3D silicon oxide nano-sandwich type solar cell
Bibliographic record
Abstract
Recent research in the field of photovoltaic and solar cell fabrication has shown the potential to significantly enhance light absorption in thin-film solar cells by using surface texturing and nanostructure coating techniques. In this paper, for the first time, we propose a new method for nano sandwich type thin-film solar cell fabrication by combining the laser amorphization (2nd solar cell generation) and laser nanofibers generation (3rd solar cell generation) techniques. In this novel technique, the crystalline silicon is irradiated by megahertz frequency femtosecond laser pulses under ambient conditions and the multi-layer of amorphorized silicon and nano fibrous layer are generated in the single-step on top of the silicon substrate. Light spectroscopy results show significant enhancement of light absorption in the generated multi layers solar cells (Silicon Oxide nanofibers / thin-film amorphorized silicon). This method is single step and no additional materials are added and both layers of the amorphorized thinfilm silicon and three-dimensional (3D) silicon oxide nanofibrous structures are grown on top of the silicon substrate after laser irradiation. Finally, we suggest how to maximize the light trapping and optical absorption of the generated nanofibers/thin-film cells by optimizing the laser pulse duration.
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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".