Bibliographic record
Abstract
Photonic crystal nanostructures are the foundation for many optical applications such as nanochip waveguides, optical fibres, and high-Q nanocavities. Recently, researchers have begun to explore the use of photonic crystal slabs to increase the overall absorption of sunlight in thin-film solar photovoltaic (PV) cells. Currently, amorphous silicon (a-Si:H) thin-film technologies can only achieve efficiencies of up to 16% in laboratories and less than 10% in manufactured commercial products. The difficulty in improving these efficiencies arises from the inherent band gap properties of the crystalline silicon layer: the natural photonic bandgap in the near infrared (IR) region of light prohibits almost a third of the entire available solar spectrum from being absorbed. Some of this loss can be salvaged by increasing the thickness of the silicon layer, but this drives the price of the cell up and has very limited potential. However, using photonic crystal nanostructures in the active layer of the cell can decrease the reflection of light at the surface and increase the photon path within the film, enhancing the collection and conversion efficiencies over a broad spectrum. The absorption can be further increased by introducing pseudo-disorder within the structures. The purpose of this study was to explore the physical parameters of this disorder and quantitatively optimize absorption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".