Best Practice of Identifying Chemical Constituents and Evolution of Sn-Containing Perovskites by Photoelectron Spectroscopy
Bibliographic record
Abstract
Tin-containing halide perovskites are promising for tandem solar cells but face stability issues due to tin oxidation, even under low-oxygen conditions. A comprehensive understanding of the tin chemical states at the surface and within the bulk after oxidation is essential for developing strategies to mitigate tin oxidation. In this study, we investigate the nature and evolution of oxidation products near the surface of tin-containing perovskites using X-ray photoelectron spectroscopy (XPS) and gas-cluster ion-beam (GCIB) sputter profiling. We demonstrate that peaks previously attributed to Sn 2+ and Sn 4+ are now reassigned to Sn 2+ (-I) and Sn x + (-O), respectively, with a thin Sn oxide layer formed on the perovskite surface. Under inert conditions, Sn x + and O species do not penetrate the bulk but significantly alter the Sn/Pb and I/(Sn+Pb) ratios. Furthermore, Sn diffusion from the bulk to the surface occurs alongside the A-site cation (N species and Cs + ) and iodine depletion, even without external stimuli. These findings provide critical insights into the complex interplay between tin’s oxidation states and the stability of tin-based perovskite solar cells.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".