Porous perovskite films integrated with Au–Pt nanowire-based electrodes for highly flexible large-area photodetectors
Bibliographic record
Abstract
Abstract Flexible, large-area, and stable perovskite photodetectors have drawn increasing widespread research attention for next-generation wearable and portable optoelectronic devices. However, high mechanical durability coupled with large device area and enhanced environmental stability has not been demonstrated yet to attain practical viability. Herein, a highly bendable, stable, and large-area (3 cm 2 ) flexible polystyrene incorporated perovskite photodetector is presented. Due to the formation of a porous polystyrene-perovskite composite film in a single step it allows unprecedented mechanical stability, maintaining 85% of its original photocurrent value after 10,000 bending cycles at a bending angle of 120°. Equally crucial, the solution-processed self-assembled Pt–Au nanochains were developed to provide a simple and fast method of patterning the conductive and flexible electrodes onto the filter substrate. The optimized polystyrene-perovskite photodetector exhibits a high responsivity up to 2.73 A W −1 , a maximum specific detectivity of 6.2 × 10 13 Jones, and a superior switching ratio of 1.0 × 10 4 . In addition, the polystyrene-perovskite photodetector yields excellent stability under the combined stresses of moisture, ambient air, and room light, and retains 92% of its original performance for over 30 days. All these results demonstrate that this work provides a facile and cost-effective approach that paves the way to develop high-performance, stable, and highly flexible optoelectronic devices.
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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".