High-performance integrated perovskite and organic solar cells with efficient near-infrared harvesting
Bibliographic record
Abstract
The integration of planar-type perovskite (Eg ~ 1.5 eV) solar cells (PSCs) with a bulk-heterojunction (BHJ) composite comprising a near-infrared (NIR) absorbing conjugated polymer (Eg < 1.4 eV) and a fullerene derivative is a promising approach to overcoming the narrow absorption limit of typical PSCs. Nevertheless, integrated solar cells (ISCs) suffer from low fill factors (FFs) and inefficient NIR harvesting, mainly due to poor charge transport in the BHJ films. Here, we successfully demonstrate highly efficient P-I-N perovskite/BHJ ISCs with an enhanced FF and improved NIR harvesting by introducing a novel n-type semiconducting polymer and a new processing additive into the BHJ films. The optimized ISCs exhibit a power conversion efficiency (PCE) of 16.36%, which far surpasses that of the reference PSCs (~ 14.70%) due to the increased current density (Jsc ~ 20.04 mA cm-2) resulting from the additional NIR harvesting. Meanwhile, the optimized ISCs maintain a high FF of 77% and an open-circuit voltage (Voc) of 1.06 V. These results indicate that this approach is a versatile means of overcoming the absorption and theoretical efficiency limits of state-ofthe- art PSCs.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".