Additive Engineering of Ruddlesden–Popper Perovskites with MXene Nanoflakes: Toward Enhanced Photoresponsivity, Detectivity, and Stability
Bibliographic record
Abstract
Ruddlesden–Popper perovskites (RPPs) have drawn a lot of attention due to their structural stability under ambient atmosphere compared to bulk counterparts. However, their relatively low photoresponsivity, due to quantum and dielectric confinement effects, is still a key challenge in the development of efficient photodetectors. Present work reports one-step additive engineering of the RPP ((CH) 2 (MA) n −1 Pb n Br 3 n +1, n = 4) absorber layer using Ti 3 C 2 T x MXene nanoflakes, which formed a robust heterointerface. Surface functional groups of MXene retard the crystallization rate of RPP thin films, thereby spurring the enhancement of the optical, structural, and morphological properties of RPP-MXene hybrids. Unencapsulated hybrid ( n = 4 + 1.5 mM) photodetectors have shown improved responsivity (∼151 A/W) and detectivity (∼14.21 × 10 12 Hz 1/2 /W) at 2.0 V bias under laser illumination (λ ex ∼ 405 nm, P in ∼ 0.62 mW/cm 2 ), outperforming pristine devices significantly. Over 50 cycles, hybrid devices maintained stable peak photocurrent, while photocurrent of pristine devices degraded by ∼35%, indicating a unique platform to explore a wide range of MXenes to overcome the stability issues associated with perovskites.
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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) | 0.000 | 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 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".