Dispersion of the carbon nonotubes in a vacuum arc system and synthesis of copper-carbon nanotubes composites
Bibliographic record
Abstract
Electric arcs operating in the thermo-field mode are used in many industrial processes. They are used in electricity transport and distribution, in metal production industry, and in plasma processing applications like in plasma spraying and as the plasma source in physical vapour deposition (PVD) processes. The electric arc-based systems have the common problem of material degradation at the attachment point of the electric arc on the electrode, mainly the cathode surface. The cathode erosion is particularly severe in higher power devices such as plasma torches and circuit breakers. The cathode erodes while producing the ions needed for the electric discharge to occur. Cathode erosion is one big limitation to the use of electric arcs in industry. Carbon nanotubes (CNTs) are 1-D structures with a diameter in the nanoscale range giving them very good field emission properties. These properties make them possible candidates to form metal-CNT composites that can be used as electrodes. The CNTs presence at the electrode surface may help in providing an enhanced electron emission and reduced erosion. The long term objective of this project is to form a new class of electrode materials, namely copper-CNT nanocomposites. One major problem of using these CNTs in composites is their agglomeration and inability to disperse easily. In this project, a pulsed arc discharge system is used to study the capability to ablate, disperse, transport, and deposit the CNTs onto a substrate. The project involves studying the ability to form a nanocomposite made of copper and CNTs from a target and deposition of the copper-CNT mixture on a substrate.
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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".