Post-Growth Planarization of Vertically Aligned Carbon Nanotube Forests for Electron-Emission Devices
Bibliographic record
Abstract
This paper reports a microplasma-based planar process for as-grown carbon nanotube (CNT) forests to produce macroscopically flat top surfaces over different scales. This noncontact process is based on microscale removal of CNTs driven by pulsed electrical discharge generated at the interface between the forest surface and a stainless-steel planar electrode, achieving controlled subtractive planarization of the forest structure while maintaining the CNTs’ alignment. Forest samples with large surface areas of up to 26 mm 2 with the largest height variations of ∼1 mm are successfully processed to demonstrate improvements by up to ∼60× in the height uniformity of the forests and ∼30× in the parallelism of their top surfaces with the substrate planes. Elemental analyses suggest that the contamination from the electrode material is minor, or negligibly small when the discharge process does not experience short-circuit events that can lead to damaging irregular arcs. The promising results obtained in this work are expected to pave the way for studying the properties of the CNT forest further and promote its application in areas where height uniformity is of prime interest, such as in electron emission devices.
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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) | 0.001 | 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".