GISAXS study of the alignment of oriented carbon nanotubes grown on plain SiO<sub>2</sub>/Si(100) substrates by a catalytically enhanced CVD process
Bibliographic record
Abstract
Abstract We report a quantitative Grazing Incidence Small Angle X‐Ray Scattering (GISAXS) study of the alignment of mutually oriented carbon nanotubes (CNTs) grown by a catalytically‐activated, a plasma direct current and hot filaments‐assisted (DC HF CCVD) process. Metallic catalytic (Co) islands were dispersed on plain SiO 2 (5 nm thickness)/Si(100) substrates prior the growth of CNTs which can be considered as highly anisotropic 1D nanostructures. The GISAXS pattern analysis in the framework of the Distorted‐Wave Born Approximation (DWBA) has been expanded to multilayered non‐correlated surface science systems (non‐correlated carbon nanotubes) and is based on the determination of carbon nanotubes density, characteristic lengths, atomic Co dispersion throughout the CNTs and roughnesses of the uncorrelated particles. Even dominated by envelope features of disordered objects, they provide noticeable information about CNTs films: both structural (orientation, size and length distribution) and correlation (density, mutual alignment) information. The results stand in rather good agreement with the Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) observations. Moreover, the GISAXS patterns could only be satisfactorily reproduced by adding a mixed C–Co contribution between the pure carbon and the metallic cobalt let on top of the CNT, inferring that cobalt continuously fills the nanotube in the course of the growth and that the CNTs experience a large tendency toward mutual alignment. The effect of variable X‐ray incidence angles has been investigated. With a weak addition of ammonia (from 1% to 3% of the gas mixture) the density has been found to decrease by more than one order of magnitude. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".