Optically invariant InGaN nanowire light-emitting diodes on flexible substrates under mechanical manipulation
Bibliographic record
Abstract
Abstract The integration of GaN-based light-emitting diodes (LEDs) onto flexible platforms provides opportunities for conformal lighting, wearable electronics, and bendable displays. While this technology may enhance the functionality of the light source, the development of flexible GaN LEDs suffers from performance degradation, when mechanical bending is applied during operation. A unique approach to eliminate the degradation employs dot-in-wire structures, using cylindrical light-emitting heterostructures that protrude above the flexible platform, separating the active light-emitting region from the bending substrate. Here, we demonstrate the optical enhancement of nanowire light emitters by changing the geometric orientation within a 1 × 1 mm2 array of nanowires on a flexible platform through bending of the substrate platform. The flexible structures were achieved by transferring GaN nanowire LEDs from sapphire substrates onto flexible polyethylene terephthalate (PET) using a “paste-and-cut” integration process. The I–V characteristics of the nanowire LEDs showed negligible change after integration onto the PET, with a turn-on voltage of 2.5 V and a forward current of 400 μA at 4 V. A significant advantage for the nanowire devices on PET was demonstrated by tilting the LEDs through substrate bending that increased the electroluminescence (EL) intensity, while the I–V characteristics and the EL peak position remained constant. Through finite-element analysis and three-dimensional finite-difference time-domain modeling, it was determined that tilting the protruding devices changed the effective distance between the structures, enhancing their electromagnetic coupling to increase light output without affecting the electrical properties or peak emission wavelength of the LEDs.
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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".