Design method for generating multiple colors with thickness-modulated thin-film optical filters for silicon solar cells
Bibliographic record
Abstract
This study explores an innovative approach to enhance the esthetic appeal while having minimal impact on the functional performance of solar-charged electric vehicles. We propose replacing the standard antireflective coating on solar cells with a custom-designed notch filter. This optical filter ensures high transmission across the solar spectrum and creates a distinct color rendering effect in the visible range, thereby making the cells more visually appealing. We utilized niobium pentoxide and silicon dioxide for their excellent optical, mechanical, and corrosive properties to fabricate filters reflecting at specific wavelengths, producing vibrant blue, green, and red color renderings at 400, 550, and 632 nm, respectively. Theoretical relative photocurrent density losses of only ∼7%,∼10%, and ∼14% were observed for blue, green, and red colors, respectively, due to the presence of these filters when compared to a silicon solar cell with a standard antireflective coating. Using optilayer and matlab software, we precisely designed filters with just two to four layers, achieving simplicity and effectiveness. Gradual evolution optimization followed by thin layer removal optimization produced automated and consistent thickness-modulated multilayered optical filter designs over a wide range of user inputs. Our designs were fabricated using magnetron sputtering and validated through variable angle spectroscopic ellipsometry and reflectance spectroscopy, showing strong agreement with our simulations. With a minimal trade-off in the functionality and efficiency of solar cells, this method transforms standard solar cells into esthetically pleasing components, broadening their appeal and potential applications in consumer products.
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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 | 0.001 | 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".