Understanding the role of Si doping on surface charge and optical properties: Photoluminescence study of intrinsic and Si-doped InN nanowires
Bibliographic record
Abstract
In the present work, the photoluminescence (PL) characteristics of intrinsic and Si-doped InN nanowires are studied in detail. For intrinsic InN nanowires, the emission is due to band-to-band carrier recombination with the peak energy at $\ensuremath{\sim}$0.64 eV (at 300 K) and may involve free-exciton emission at low temperatures. The PL spectra exhibit a strong dependence on optical excitation power and temperature, which can be well characterized by the presence of very low residual electron density and the absence or a negligible level of surface electron accumulation. In comparison, the emission of Si-doped InN nanowires is characterized by the presence of two distinct peaks located at $\ensuremath{\sim}$0.65 and $\ensuremath{\sim}$0.73--0.75 eV (at 300 K). Detailed studies further suggest that these low-energy and high-energy peaks can be ascribed to band-to-band carrier recombination in the relatively low-doped nanowire bulk region and Mahan exciton emission in the high-doped nanowire near-surface region, respectively; this is a natural consequence of dopant surface segregation. The resulting surface electron accumulation and Fermi-level pinning, due to the enhanced surface doping, are confirmed by angle-resolved x-ray photoelectron spectroscopy measurements on Si-doped InN nanowires, which is in direct contrast to the absence or a negligible level of surface electron accumulation in intrinsic InN nanowires. This work elucidates the role of charge-carrier concentration and distribution on the optical properties of InN nanowires.
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".