Fully Epitaxial Semiconductor Photoelectrode for UV–VIS Dual-Band Photodetection
Bibliographic record
Abstract
High-performance spectrally distinctive photodetectors (PDs) are of great importance in sensing and information processing. PDs based on photoelectrochemical (PEC) principles are of particular interest due to their simple fabrication process and tunable photoresponse through both physical and chemical processes. Despite the recent advancement in PEC-PDs, they are far less ideal. For example, most of them are either not stable or not compatible with existing epitaxial semiconductor device platforms, whereas although III-nitride nanowire-based PEC-PDs can largely mitigate these drawbacks, dual-band photodetection is limited to the ultraviolet (UV) range. Herein, we show that by using fully epitaxial n-GaN/p-InGaN p–n heterojunction photoelectrodes on the Si substrate, the dual-band photodetection of III-nitride nanowire-based PEC-PDs can be extended to the visible (VIS) range. Moreover, the present photoelectrodes can also exhibit dual-polarity photocurrent under a fixed illumination condition by tuning the applied potential, extending their functionality. This study represents the first achievement of UV–VIS dual-band photodetection with simple, fully epitaxial semiconductor nanowire p–n heterojunctions. The discussion on the photocarrier dynamics further sheds light on the design of dual-band PEC-PDs based on emerging semiconductor nanowire p–n heterojunctions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".