Nanotube field electron emission: principles, development, and applications
Bibliographic record
Abstract
There is a growing trend to apply field emission (FE) electron sources in vacuum electronic devices due to their fast response, high efficiency and low energy consumption compared to thermionic emission ones. Carbon nanotubes (CNTs) have been regarded as a promising class of electron field emitters since the 1990s and have promoted the development of FE technology greatly because of their high electrical and thermal conductivity, chemical stability, high aspect ratio and small size. Recent studies have shown that FE from CNTs has the potential to replace conventional thermionic emission in many areas and that it exhibits advanced features in practical applications. Consequently, FE from nanotubes and applications thereof have attracted much attention. This paper provides a comprehensive review of both recent advances in CNT field emitters and issues related to applications of CNT based FE. FE theories and principles are introduced, and the early development of field emitters is related. CNT emitter types and their FE performance are discussed. The current situation for applications based on nanotube FE is reviewed. Although challenges remain, the tremendous progress made in CNT FE over the past ten years indicates the field's development potential.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".