Heterovalent Dopant Incorporation for Bandgap and Type Engineering of Perovskite Crystals
Bibliographic record
Abstract
Controllable doping of semiconductors is a fundamental technological requirement for electronic and optoelectronic devices. As intrinsic semiconductors, hybrid perovskites have so far been a phenomenal success in photovoltaics. The inability to dope these materials heterovalently (or aliovalently) has greatly limited their wider utilizations in electronics. Here we show an efficient in situ chemical route that achieves the controlled incorporation of trivalent cations (Bi(3+), Au(3+), or In(3+)) by exploiting the retrograde solubility behavior of perovskites. We term the new method dopant incorporation in the retrograde regime. We achieve Bi(3+) incorporation that leads to bandgap tuning (∼300 meV), 10(4) fold enhancement in electrical conductivity, and a change in the sign of majority charge carriers from positive to negative. This work demonstrates the successful incorporation of dopants into perovskite crystals while preserving the host lattice structure, opening new avenues to tailor the electronic and optoelectronic properties of this rapidly emerging class of solution-processed semiconductors.
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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".