Scalable Nanowire Photonic Crystals: Molding the Light Emission of InGaN
Bibliographic record
Abstract
To date, there have been no efficient semiconductor light emitters operating in the green and amber wavelengths. This study reports on the synthesis of InGaN nanowire photonic crystals, including dot‐in‐nanowires, nanotriangles, and nanorectangles with precisely controlled size, spacing, and morphology, and further demonstrates that bottom‐up InGaN photonic crystals can exhibit highly efficient and stable emission. The formation of stable and scalable band edge modes in defect‐free InGaN nanowire photonic crystals is directly measured by cathodoluminescence studies. The luminescence emission, in terms of both the peak position (λ ≈ 505 nm) and spectral linewidths (full‐width‐half‐maximum ≈ 12 nm), remains virtually invariant in the temperature range of 5–300 K and under excitation densities of 29 W cm−2 to 17.5 kW cm−2. To the best of our knowledge, this is the first demonstration of the absence of Varshni and quantum‐confined Stark effects in wurtzite InGaN light emitters—factors that contribute significantly to the efficiency droop and device instability under high‐power operation. Such distinct emission properties of InGaN photonic crystals stem directly from the strong Purcell effect, due to efficient coupling of the spontaneous emission to the highly stable and scalable band‐edge modes of InGaN photonic crystals, and are ideally suited for uncooled, high‐efficiency light‐emitting‐diode operation.
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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) | 0.001 | 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".