Tuning the Surface Charge Properties of Epitaxial InN Nanowires
Bibliographic record
Abstract
We have investigated the correlated surface electronic and optical properties of [0001]-oriented epitaxial InN nanowires grown directly on silicon. By dramatically improving the epitaxial growth process, we have achieved, for the first time, intrinsic InN both within the bulk and at nonpolar InN surfaces. The near-surface Fermi-level was measured to be ∼0.55 eV above the valence band maximum for undoped InN nanowires, suggesting the absence of surface electron accumulation and Fermi-level pinning. This result is in direct contrast to the problematic degenerate two-dimensional electron gas universally observed on grown surfaces of n-type degenerate InN. We have further demonstrated that the surface charge properties of InN nanowires, including the formation of two-dimensional electron gas and the optical emission characteristics can be precisely tuned through controlled n-type doping. At relatively high doping levels in this study, the near-surface Fermi-level was found to be pinned at ∼0.95-1.3 eV above the valence band maximum. Through these trends, well captured by the effective mass and ab initio materials modeling, we have unambiguously identified the definitive role of surface doping in tuning the surface charge properties of InN.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".