Control of the spontaneous formation of oxide overlayers on GaP nanowires grown by physical vapor deposition
Bibliographic record
Abstract
Growth of gallium phosphide nanowires by vapor deposition of simple thermally evaporated inorganic precursors is generally accompanied by unintentional formation of thick oxide coating, which may compromise the optical and electrical properties of the nanowires. Controlling and eliminating this outer layer during thermal evaporation growth of GaP nanowires represents a barrier to simple and scalable preparation of this technologically important material. In this article, we systematically investigated the role of different parameters (temperature, hydrogen flow rate, and starting Ga/P ratio) in the synthesis of GaP nanowires, and mapped out the conditions for the growth of oxide-layer-free nanowires. Increase in temperature, hydrogen flow, and phosphorus concentration led to diminished oxide layer thickness and improved nanowire morphology. Long and straight nanowires with the near perfect stoichiometry and complete absence of oxide outer layer were obtained for 1050 °C, 100 sccm hydrogen flow rate, and Ga/P flux ratio of 0.5. In contrast to other reports, this work emphasizes the importance of introducing hydrogen flow and excess phosphorus, which provide for reducing environment and reduced rate of the reaction of Ga with O in the growth process, respectively. The ability to control dielectric medium around GaP NWs by controlling the formation of oxide overlayer was demonstrated by Raman spectroscopy. The results of this work demonstrate a full control of the multi-parameter space in the simple, inexpensive, and scalable synthesis of GaP NWs, and may provide a guideline for rational improvement of the growth conditions for other types of semiconductor nanowires.
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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) | 0.000 | 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".