Enhancement of transport properties in single ZnSe nanowire field-effect transistors
Bibliographic record
Abstract
Wide-gap semiconductors are excellent candidates for next-generation optoelectronic devices, including tunable emitters and detectors. ZnSe nanowire-based devices show great promise in blue emission applications, since they can be easily and reproducibly fabricated. However, their utility is limited by deep level defect states that inhibit optoelectronic device performance. The primary objective of this work is to show how the performance of ZnSe nanowire devices improves when nanowires are subjected to a post-growth anneal treatment in a zinc-rich atmosphere. We use low temperature photoluminescence spectroscopy to determine the primary recombination mechanisms and associated defect states. We then characterize the electronic properties of ZnSe nanowire field effect transistors fabricated from both as-grown and Zn-annealed nanowires, and measure an order-of-magnitude improvement to the electrical conductivity and mobility after the annealing treatment. We show that annealing reduces the concentration of zinc vacancies, which are responsible for strong compensation and high amounts of scattering in the as-grown 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".