Simultaneously Passivating Cation and Anion Defects in Metal Halide Perovskite Solar Cells Using a Zwitterionic Amino Acid Additive
Bibliographic record
Abstract
Abstract Ionic defects (e.g., organic cations and halide anions), preferably residing along grain boundaries (GBs) and on perovskite film surfaces, are known to be a major source of the notorious environmental instability of perovskite solar cells (PeSCs). Although passivating ionic defects is desirable, previous approaches using Lewis base or acid molecules as additives suppress only the negatively or positively charged defects, thus leaving oppositely charged defects. In this work, both the cationic and anionic defects inside methyl ammonium lead tri‐iodide (MAPbI3) are simultaneously passivated by introducing a zwitterionic form of the amino acid, L‐alanine, into the precursor solution as an additive. L‐alanine has both positive (NH3+) and negative (COO−) functional groups at a specific solvent pH, thereby passivating both the cation and anion defects in MAPbI3. The addition of L‐alanine increases the grain size of the perovskite crystals and lengthens the charge carrier lifetime (τ > 1 µs), leading to improved power conversion efficiencies (PCEs) of 20.3% (from 18.3% without an additive) for small‐area (4.64 mm2) devices and 15.6% (from 13.5%) for large‐area submodules (9.06 cm2). More importantly, the authors’ approach also significantly enhances the shelf storage and photoirradiation stabilities of PeSCs.
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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".