Ultralow Self-Doping in Two-dimensional Hybrid Perovskite Single Crystals
Bibliographic record
Abstract
Abstract Unintentional self-doping in semiconductors through shallow defects is detrimental to optoelectronic device performance. It adversely affects junction properties and it introduces electronic noise. This is especially acute for solution-processed semiconductors, including hybrid perovskites, which are usually high in defects due to rapid crystallization. Here, we uncover extremely low self-doping concentrations in single crystals of the two-dimensional perovskites (C6H5C2H4NH3)2PbI4·(CH3NH3PbI3)n−1 (n = 1, 2, and 3), over three orders of magnitude lower than those of typical three-dimensional hybrid perovskites, by analyzing their conductivity behavior. We propose that crystallization of hybrid perovskites containing large organic cations suppresses defect formation and thus favors a low self-doping level. To exemplify the benefits of this effect, we demonstrate extraordinarily high light-detectivity (1013 Jones) in (C6H5C2H4NH3)2PbI4·(CH3NH3PbI3)n−1 photoconductors due to the reduced electronic noise, which makes them particularly attractive for the detection of weak light signals. Furthermore, the low self-doping concentration reduces the equilibrium charge carrier concentration in (C6H5C2H4NH3)2PbI4·(CH3NH3PbI3)n−1, advantageous in the design of p–i–n heterojunction solar cells by optimizing band alignment and promoting carrier depletion in the intrinsic perovskite layer, thereby enhancing charge extraction.
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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".