Dimensionality engineering of metal halide perovskites
Bibliographic record
Abstract
Metal halide perovskites are a class of materials that are ideal for photodetectors and solar cells due to their excellent optoelectronic properties. Their low-cost and low temperature synthesis have made them attractive for extensive research aimed at revolutionizing the semiconductor industry. The rich chemistry of metal halide perovskites allows compositional engineering resulting in facile tuning of the desired optoelectronic properties. Moreover, using different experimental synthesis and deposition techniques such as solution processing, chemical vapor deposition and hot-injection methods, the dimensionality of the perovskites can be altered from 3D to 0D, each structure opening a new realm of applications due to their unique properties. Dimensionality engineering includes both morphological engineering-reducing the thickness of 3D perovskite into atomically thin films-and molecular engineering-incorporating long-chain organic cations into the perovskite mixture and changing the composition at the molecular level. The optoelectronic properties of the perovskite structure including its band gap, binding energy and carrier mobility depend on both its composition and dimensionality. The plethora of different photodetectors and solar cells that have been made with different compositions and dimensions of perovskite will be reviewed here. We will conclude our review by discussing the kinetics and dynamics of different dimensionalities, their inherent stability and toxicity issues, and how reaching similar performance to 3D in lower dimensionalities and their large-scale deployment can be achieved.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".