4-Nitrophthalic Acid as an Additive for Low-Cost, Stable, and Efficient Carbon-Based Perovskite Solar Cells
Bibliographic record
Abstract
Recent developments in perovskite solar cells (PSCs) have opened enormous possibilities for industrial applications. The quality of the perovskite film determines its efficiency and stability. Surface defects can negatively impact the solar cell performance. Additive treatment can efficiently minimize defect and trap states at the grain boundaries and surface of perovskite films. In this work, we introduce 4-Nitrophthalic acid (NPA) as an additive to improve perovskite film quality, crystalline structure, larger crystal grains, power conversion efficiency (PCE), and stability. NPA’s carboxylic (−COOH), benzene, and nitro (−NO 2 ) groups interact with Pb 2+ to significantly reduce surface defects. Moreover, the addition of NPA efficiently suppresses nonradiative recombination and enhances interfacial charge separation and transfer. The NPA additive’s benzene group provides hydrophobic properties, enhancing the moisture stability of the fabricated CPSCs. The greatest PCE for carbon-based PSCs using a 2 mg of NPA device is 11.06%, which is higher than the control MAPbI 3 CPSCs (9.69%). Furthermore, the long-term stability of the CPSC with 2 mg of NPA added is considerably enhanced, and its PCE retains 84% of its initial value after being preserved for 650 h. Our results suggest that the NPA additive approach is a potential method for increasing the performance of 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".