Photonic crystal light trapping: Beyond 30% conversion efficiency for silicon photovoltaics
Bibliographic record
Abstract
The power conversion efficiency of single-junction silicon solar cells has increased only by 1.5% despite extensive efforts over the past two decades. The current world-record efficiencies of silicon solar cells, within the 25%–26.7% range, fall well below the thermodynamic limit of 32.3%. We review the recent progress in photonic crystal light-trapping architectures poised to achieve 28%–31% conversion efficiency in flexible 3–20 μm-thick, single-junction crystalline-silicon solar cells. These photonic crystals utilize wave-interference based light-trapping, enabling solar absorption well beyond the Lambertian limit in the 300–1200 nm wavelength range. Using experimentally feasible doping profiles, carrier lifetimes, surface recombination velocities, and established Auger recombination losses, we review considerations leading to the prediction of 31% efficiency in a 15 μm-thick silicon photonic crystal cell with interdigitated back-contacts. This is beyond the conversion efficiency of any single-material photovoltaic device of any thickness.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".