Defect Tolerance of Lead-Halide Perovskite (100) Surface Relative to Bulk: Band Bending, Surface States, and Characteristics of Vacancies
Bibliographic record
Abstract
We characterized the formation of vacancies at a surface slab model and contrasted the results with the bulk of lead-halide perovskites by using cubic and tetragonal CsPbI 3 as representative structures. The defect-free CsI-terminated (100) surface does not trap charge carriers. In the presence of defects (vacancies), the surface is expected to exhibit a p -type behavior. The formation energy of cesium vacancies V Cs – is lower at the surface than in the bulk, while iodine vacancies V I + have a similar energy (around 0.25–0.4 eV) within the range of chemical potentials compatible with solution processing synthesis conditions. Lead–iodine divacancies ( V PbI – ) are expected to dominate lead-only vacancies at the surfaces. Major surface vacancies create shallow host-like energy states with a small Franck–Condon shift, making them electronically harmless (same as in bulk). The spin–orbit coupling contributes to the defect tolerance of lead-halide perovskite surfaces by causing delocalization of electronic states associated with n -type defects and retraction of the lowest unoccupied states from the surface due to a mixing of Pb-p x, y, z orbitals. These results explain a high optoelectronic performance of two-dimensional structures, nanoparticles, and polycrystalline thin films of lead-halide perovskites despite the abundance of interfaces in these materials.
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 | 0.000 |
| 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".