Photo‐Detectable Light‐Emitting Devices with Epitaxial Nanowire Photonic Crystals
Bibliographic record
Abstract
There is a growing demand for multifunctional optoelectronic systems for various applications such as environmental monitoring, data processing, and information displays. Conventional approaches rely on integrating separate light‐emitting and photo‐detecting components, which leads to an increased system complexity, various fabrication challenges, and large system footprint. Bifunctional optoelectronic devices offer a promising solution by being able to emit and detect light at a single device level. Herein, III‐nitride epitaxial nanowire photonic crystals (epi‐NPCs) for bifunctional optoelectronic devices are investigated. III‐nitride semiconductors, particularly in the form of nanowires, are highly attractive for such devices due to their excellent material quality and electrical doping, as well as the tunable bandgaps from the ultraviolet (UV) to the near‐infrared. Moreover, nanowires in a format of photonic crystals enable precise control over light emission and absorption. Through engineering the photonic bands and material composition, excellent tuning of light emission properties such as the far‐field radiation pattern, spectral linewidth, as well as the light emission wavelength is shown. Notably, surface‐emitting UV lasing is made possible. It is further shown that such devices can be operated in the photodetection mode. As an example, UV photodetection is demonstrated using the UV lasing device.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".