Efficient Inverted Perovskite Solar Cells via Improved Sequential Deposition
Bibliographic record
Abstract
Inverted-structure metal halide perovskite solar cells (PSCs) have attractive advantages like low-temperature processability and outstanding device stability. The two-step sequential deposition method shows the benefits of easy fabrication and decent performance repeatability. Nevertheless, it is still challenging to achieve high-performance inverted PSCs with similar or equal power conversion efficiencies (PCEs) compared to the regular-structure counterparts via this deposition method. Here, an improved two-step sequential deposition technique is demonstrated via treating the bottom organic hole-selective layer with the binary modulation system composed of a polyelectrolyte and an ammonium salt. Such improved sequential deposition method leads to the spontaneous refinement of up and buried interfaces for the perovskite films, contributing to high film quality with significantly reduced defect density and better charge transportation. As a result, the optimized PSCs show a large enhancement in the open-circuit voltage by 100 mV and a dramatic lift in the PCE from 18.1% to 23.4%, delivering the current state-of-the-art performances for inverted PSCs. Moreover, good operational and thermal stability is achieved upon the improved inverted PSCs. This innovative strategy helps gain a deeper insight into the perovskite crystal growth and defect modulation in the inverted PSCs based on the two-step sequential deposition method.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".