Design of the shape, size, and distribution of the array of crystalline ZnO nanowires
Bibliographic record
Abstract
As an ongoing research in our laboratories we are investigating the development of a gas ionization sensor (GIS). GIS is one of the most efficient gas sensors in terms of selectivity, reversibility, fast response time, low noise, and durability. This gas detector is based on breakdown of the gases inside two-parallel plates biased with high electric potential. The incorporation of the nanowires (NWs) in GIS results in a decreased applied voltage on the device. As the structure, size, and distribution of NWs affect the amplification properties of electric field inside a gas detector, in this work a technique to optimize NWs geometrical shape and their distribution for achieving the highest electric field inside the gas sensor is studied. ZnO NWs were chosen for this purpose as ZnO NWs possess specific characteristics such as reversibility, sensitivity, long life, repeatability, and possibility to grow them with different geometrical shapes. The design of ZnO NWs grown using a low cost and high throughput electrochemical fabrication process is investigated. The induced electric field influenced by structural parameters of NWs apexes was assessed using finite element method (COMSOL).
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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".