Vertical Field-Effect Near-Infrared Phototransistor with High Responsivity and Detectivity Based on a Au Nanowire Porous Source and a Mixed PbSe-HfO<sub>2</sub> Sensing Layer
Bibliographic record
Abstract
Near-infrared (NIR) detection is essential for applications in optical communications, biomedical imaging, and environmental monitoring. However, conventional NIR photodiodes face inherent trade-offs between responsivity and noise, largely due to thermal excitation in narrow-bandgap materials often necessitating cryogenic cooling to achieve an acceptable performance. In this study, we demonstrate a vertical field-effect NIR phototransistor (VFEPT) that integrates a mixed lead selenide quantum dot (PbSeQD)–hafnium dioxide nanoparticle (HfO 2 NP) sensing layer with a porous gold nanowire source, enabling significantly improved photodetection capabilities. The high permittivity of HfO 2 facilitates substantial charge accumulation and modulates the Schottky junction, while its low electrical conductivity suppresses leakage current even in the presence of narrow-bandgap PbSeQDs. This synergistic configuration effectively reduces the dark current, minimizes noise, and enhances detectivity. Additionally, the large capacitance between the gate and the source boosts charge accumulation, resulting in an amplified photocurrent and enhanced responsivity. Under NIR illumination, PbSeQDs efficiently absorb photons and generate electron–hole pairs, reinforcing the gate–source electric field and further increasing charge accumulation, thereby yielding a substantial photocurrent gain while maintaining low noise levels. The proposed VFEPT achieves a high responsivity of 256 A W – 1 and a detectivity of 2.5 × 10 15 Jones at 1550 nm, outperforming conventional NIR photodiodes and demonstrating exceptional potential for low-noise, high-responsivity NIR detection.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".