Reducing shadowing losses in silicon solar cells using cellulose nanocrystal: polymer hybrid diffusers
Bibliographic record
Abstract
Gridline shadowing is one of the main factors affecting the performance of silicon solar cells. In this demonstration, a straightforward, scalable approach is reported to reduce shadowing losses from metallic contacts on silicon solar cells by employing cellulose nanocrystals (CNC) mixed in a polymer- polydimethylsiloxane. The method is highly compatible with current solar cell module manufacturing. The CNC:polymer (CNP) hybrid diffusers, offering highly efficient broadband light diffusion, are applied atop the metallization areas to deflect the light impinging on metallic gridlines toward uncovered active areas on the solar cell. Simulations showed that the CNP diffuser is an excellent candidate for reducing shadowing losses within a wide range of incident angles, as it can reduce more than 30% of shadowing losses at normal incidence, and nearly 50% of the lost light can be recycled at the incident angle of 60°. Taking advantage of reduced shadowing losses, a new 6-busbar technology based on the CNP diffusers is proposed with lower manufacturing complexity and higher overall efficiency.
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 | 0.000 |
| 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".