A novel field emission microscopy method to study field emission characteristics of freestanding carbon nanotube arrays
Bibliographic record
Abstract
Field emission (FE) uniformity and the mechanism of emitter failure of freestanding carbon nanotube (CNT) arrays have not been well studied due to the difficulty of observing and quantifying FE performance of each emitter in CNT arrays. Herein a field emission microscopy (FEM) method based on poly(methyl methacrylate) (PMMA) thin film is proposed to study the FE uniformity and CNT emitter failure of freestanding CNT arrays. FE uniformity of freestanding CNT arrays and different levels of FE current contributions from each emitter in the arrays are recorded and visualized. FEM patterns on the PMMA thin film contain the details of the CNT emitter tip shape and whether multiple CNT emitters occur at an emission site. Observation of real-time FE performance and the CNT emitter failure process in freestanding CNT arrays are successfully achieved using a microscopic camera. High emission currents through CNT emitters causes Joule heating and light emission followed by an explosion of the CNTs. The proposed approach is capable of resolving the major challenge of building the relationship between FE performance and CNT morphologies, which can significantly facilitate the study of FE non-uniformity, the emitter failure mechanism and the development of stable and reliable FE devices in practical applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".