Imperfectly geometric shapes of nanograting structures as solar absorbers with superior performance for solar cells
Bibliographic record
Abstract
The expectation of perfectly geometric shapes of subwavelength grating (SWG) structures such as smoothness of sidewalls and sharp corners and nonexistence of grating defects is not realistic due to micro/nanofabrication processes. This work numerically investigates optical properties of an optimal solar absorber comprising a single-layered silicon (Si) SWG deposited on a finite Si substrate, with a careful consideration given to effects of various types of its imperfect geometry. The absorptance spectra of the solar absorber with different geometric shapes, namely, the grating with attached nanometer-sized features at the top and bottom of sidewalls and periodic defects within four and ten grating periods are investigated comprehensively. It is found that the grating with attached features at the bottom absorbs more energy than both the one at the top and the perfect grating. In addition, it is shown that the grating with defects in each fourth period exhibits the highest average absorptance (91%) compared with that of the grating having defects in each tenth period (89%), the grating with attached features (89%), and the perfect one (86%). Moreover, the results indicate that the absorptance spectrum of the imperfect structures is insensitive to angles of incidence. Furthermore, the absorptance enhancement is clearly demonstrated by computing magnetic field, energy density, and Poynting vector distributions. The results presented in this study prove that imperfect geometries of the nanograting structure display a higher absorptance than the perfect one, and provide such a practical guideline for nanofabrication capabilities necessary to be considered by structure designers.
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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".