Selective area grown AlInGaN nanowire arrays with core–shell structures for photovoltaics on silicon
Bibliographic record
Abstract
To pave the way for InGaN-on-Si integrated photovoltaics, uniform and close-packed n-GaN/(Al)InGaN/p-GaN nanowire (NW) arrays with a ∼0.29 μm thick absorption segment of ∼2.35 eV energy bandgap were fabricated on a Si substrate using Ti-mask selective area growth (SAG) in a molecular beam epitaxy (MBE) chamber. Instead of using thick and insulting buffer layers, this SAG process was realized by employing a 3 nm AlN/GaN: Ge buffer layer to facilitate electrical and thermal conduction between NWs and Si. Scanning transmission electron microscopy and high-resolution electron energy loss spectroscopy mapping revealed the discontinuities of AlN and the embedments of GaN:Ge which contribute to a negligible resistance of the NWs-on-Si interface. AlInGaN active segment exhibits core-shell structures, which suppress nonradiative surface recombination at NW surfaces. Working of AlInGaN core-shell NW solar cells was demonstrated with improved open-circuit voltage (Voc) and higher energy conversion efficiency (η) than those reported for InGaN NW solar cells. Stable output characteristics including the Voc of 1.41 V and η of 2.46% were obtained under 30-Sun illuminations. Such NWs-on-Si devices use Si substrate as the bottom electrode. With a low series resistance of ∼1 Ω, this work paves the way to monolithically integrate MBE-SAG III-nitride devices and Si-based electronics, such as Si solar cells and CMOS devices.
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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".