2D layered halide perovskite for field-effect transistors
Bibliographic record
Abstract
Field-effect transistors are crucial components for modern electronics, generating significant research and profitable interest. Metal halide perovskites have recently emerged as a pioneering active material in solar cells, generating interest in their potential use in other electronic and (opto)electronic devices, including field-effect transistors and phototransistors. However, before they can be commercialized, they still face significant challenges owing to their immanent instabilities with respect to heat, moisture, and light. In contrast, due to their exceptional environmental stability, the newly emerging two-dimensional Ruddlesden–Popper type perovskites have garnered significant recognition. The current state of the field is covered in this review article, as are the problems, and a perspective for the scenarios of perovskite field-effect transistors. The effects of temperature, light, and measurement conditions are taken into account, as well as the physics of the device and the fundamental mechanisms that drive these devices, such as ion migration and ionic defects. Subsequently, the performance of perovskite transistors and phototransistors described so far is analyzed and critically evaluated. Finally, the major roadblocks to perovskite transistor advancement are identified and explored. The lessons learned from other perovskite optoelectronic devices are investigated in order to address these obstacles and bring these devices closer to industrial implementation.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".