A Direct-Write Approach for Carbon Nanotube Catalyst Deposition
Bibliographic record
Abstract
Nanowriting was used to directly pattern carbon nanotube (CNT) catalyst solution with a scanning probe microscope. Glass nanopipettes filled with iron-based catalyst solutions were scanned in predefined patterns using contact mode atomic force microscopy on silicon/silicon dioxide substrates to create nanoscale catalyst surface distributions. Chemical vapor deposition using methane feedstock at 900°C produced single-walled CNTs in the patterned regions. Examination of patterning and growth conditions provided insight into the catalyst nanowriting process and the associated CNT growth. Two-terminal electronic transport measurements of the nanotube samples showed a typical resistance of 1 M¿. The nanowriting technique allows precise nanoscale catalyst patterns of almost arbitrary geometry to be directly defined for CNT growth in a simple and inexpensive manner suitable for device prototyping and applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".