Tunnel Junction Engineered Photocarrier Dynamics in Epitaxial Semiconductor Nanowires for Efficient and Ultrafast Photoelectrochemical Photodetectors
Bibliographic record
Abstract
Photodetection using photoelectrochemical (PEC) principles is an emerging field in photonics. In recent years, using epitaxial III-nitride nanowires as photoelectrodes, high-performance PEC-type photodetectors (PDs) have been demonstrated, making the epitaxial III-nitride nanowires a scalable, high-performance PEC-PD architecture. Despite the progress, the photodetection performance improvement mainly occurs through incorporating photocatalysts into the nanowire photoelectrodes. In this study, we show that a semiconductor tunnel junction (TJ), which can be a natural component in the epitaxy process of semiconductor nanowires, can drastically improve the photodetection performance of such nanowire-based PEC-PDs. By using a three-electrode PEC cell configuration, we clearly show that an n ++ -GaN/InGaN/p ++ -GaN TJ can lead to a factor of 9× improvement on the responsivity of the InGaN nanowire photoelectrode in the blue band due to the TJ-induced photocarrier dynamics tuning, compared to the InGaN nanowire photoelectrode without the TJ. More drastically, the TJ also improves the photoresponse speed of the nanowire photoelectrode by 2 orders of magnitude, and for the electrode with the TJ an ultrafast response time of less than 10 ms is estimated. This TJ concept can also be applied to other TJ structures for other band photodetections. This study therefore sheds new light on further improving the performance of emerging epitaxial nanowire-based PEC-PDs for a wide range of applications from sensing to information processing.
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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".