Screening Effects Between Field-Enhancing Patterned Carbon Nanotubes: A Numerical Study
Bibliographic record
Abstract
A numerical investigation of the topographic field-enhancement factor for structures including individual vertically aligned carbon nanotubes (VACNTs) and arrays of VACNT is presented. Some previously reported results for simple structures are reviewed first. Then, the extent of the zones of field enhancement and significant screening effects surrounding a given structure is discussed. The investigation with combined VACNT confirms the criterion that the spacing between identical CNT should be about twice their height to minimize screening effects. This statement is generalized to structures having different height ratios. The possibility of combining patterns of different height VACNT to minimize screening effects while allowing a larger surface density of such emitters is then investigated. The results show that height anisotropies in VACNT arrays can significantly reduce the field-emission current for a given applied field. A subsequent study that takes into account Joule heating and radiation losses during field emission demonstrates that, for height anisotropies larger than 5%, the VACNT tips reach temperatures above the onset temperature for selective field-assisted evaporation. This phenomenon occurs before the field-emission current from the nonideal films matches the targeted current value deduced from ideal VACNT arrays.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".