Electrophoretic deposition of carbon nanotubes on semi-conducting and non-conducting substrates
Bibliographic record
Abstract
Electrophoretic deposition (EPD) is useful for conductive substrates, where a requisite electrical path can be formed. In order to make EPD more broadly applicable for semi-/non-conducting substrates, e.g. EPD of carbon nanotubes (CNTs) on silicon and rubber, we proposed and demonstrated a simple modified EPD set-up. The substrate was directly attached to a conductive electrode at the top end, while a porous separator was placed between the lower parts of the substrate and the electrode which submerged in the CNTs suspension. The separator allowed the suspension moving through its micro-pores to reach the steel to form the requisite conductive path but hindered most CNTs from moving and attaching to the steel. Therefore, CNTs were successfully deposited on the semi-/non- conducting silicon/rubber in a simple single-step process by using the modified EPD set-up. We believe this EPD set-up can be applied to the deposition of versatile particles on various semi-/non-conducting substrates.
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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 | 0.001 |
| 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".