Effect of Water, Oxygen, and Air Exposure on CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3–</sub><i><sub>x</sub></i>Cl<i><sub>x</sub></i> Perovskite Surface Electronic Properties
Bibliographic record
Abstract
Abstract The stability of hybrid perovskite materials for solar cells and high surface property reproducibility are important topics of present research. To understand the impact of water, oxygen, and air exposure, as experienced during typical device fabrication and operation conditions, on the surface electronic properties of solution‐processed CH3NH3PbI3–xClx perovskite thin films, and subsequently formed interfaces to other materials, a comprehensive photoelectron spectroscopy study is performed. It is shown that pure oxygen exposure reduces the typically observed pronounced n‐type surface character, while pure water exposure increases it. Very low water partial pressure, e.g., as encountered in inert‐gas glove boxes and in high vacuum of 10−6 mbar, reduces the perovskite work function due to water physisorption, which is fully reversible upon mild annealing (room temperature to 50 °C) in ultrahigh vacuum. Upon exposure to ambient air, the effect of oxygen prevails over that of water, and the perovskite become less n‐type as seen by a 0.6 eV Fermi level shift toward midgap. The results help in understanding variations of the surface electronic properties reported for perovskites, which in turn impact the energy level alignment at heterojunctions in devices. For future studies, the crucial importance of stringent environment control is stressed upon.
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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) | 0.002 | 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".