Polarizable High‐Index Nanoparticles Used for Light‐Induced Crystal‐Silicon Passivation and Dielectric Antenna for High‐Efficiency Solar Cell
Bibliographic record
Abstract
Heterojunction crystal‐silicon (c‐Si) solar cells generate current from harvesting light via sweeping excited carriers away under built‐in asymmetry through amorphous silicon layers. However, loss of energy beyond the bandgap is known to hinder their efficiency. Herein, a strategy is provided to convert short‐wavelength energy into a polarization electrical field and the light dielectric antenna effect, where enhanced surface passivation and carrier collection occur simultaneously. By depositing an ultrathin polarizable nanoparticle layer on a transparent conducting oxide (TCO), a light‐induced electrical field is built up by light‐harvesting accumulation, which benefits for c‐Si surface passivation. An enchanting open‐circuit voltage (Voc) of 736.6 mV for the light‐induced field‐effect solar cell is achieved. In addition, the high‐index dielectric nanoparticles generate more photons to be absorbed in the long‐wavelength in c‐Si solar cells, which results in enhanced short‐circuit current (Jsc). As a result, benefiting from the light‐induced building‐up extra field and dielectric antenna effect, the device yields a power conversion efficiency of 22.41%, with the maximal improvement of Voc (≈4 mV), Jsc (≈0.4 mA cm−2), and fill factor (≈1%), respectively. This work provides a strategy to enhance solar cell efficiency by continuously harvesting beyond‐bandgap light to offer an additional asymmetrical field and the light antenna effect.
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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".