Dimensionality engineering of metal halide perovskites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it