High-Performance Photodetector Based on Bandgap-Engineered MOFs/CNFs Nanocomposites for Violet Light Detection
Bibliographic record
Abstract
An innovative strategy is introduced, integrating metal–organic frameworks (MOFs) and bare carbon nanofibers (CNFs) within a nanocomposite, to address the challenges related to the broad absorption spectrum of traditional photodetectors. This advancement aims to eliminate the need for additional filters or intricate surface patterning in traditional photodetectors to achieve spectral selectivity, which is associated with high costs, and to facilitate enhanced spectral selectivity in the visible range, thereby paving the way for their widespread commercial adoption. By strategically leveraging the inherent strengths of both MOFs and CNFs while addressing their individual limitations, our method not only resolves challenges related to 3D MOFs and CNFs in spectrally selective photodetection but also significantly enhances optoelectronic properties. The resulting nanocomposite exhibits spectrally selective detection of violet light in the visible range, achieving a significantly enhanced responsivity of 27 A/W, detectivity of 1.66 × 10 11 Jones, and high external quantum efficiency of 8215% at a low optical power intensity of 27 μW/cm 2 at room temperature. This approach holds significant promise to advance photodetection technology in various applications requiring high-performance and spectrally selective detectors.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".