Simulation and microwave measurement of the conductivity of carbon nanotubes
Bibliographic record
Abstract
Recently, excellent properties have been realised from structures formed by carbon nanotubes. This propelled their use as nanoscale electronic devices in the information technology industry. The discovery of carbon nanotubes has stimulated interest in carbonbased electronics. Metal-Oxide-Semiconductor systems (MOS) are used to model charge transport within these carbon structures. Schrodinger‟s equation is solved self-consistently with Poisson‟s equation. The Poisson equation, which defines the potential distribution on the surface of the nanotube, is computed using a two-dimensional finite difference algorithm exploiting the azimuthal symmetry. A solution to the Schrodinger‟s equation is required to obtain the wavefunctions within the nanotube model. This is implemented with the scattering matrix method. The resulting wavefunctions defined on the nanotube surface are normalised to the flux computed by the Landauer equation. A novel implementation of the Schrodinger- Poisson solver for providing a solution to a three dimensional nanoscale system is described. To avoid convergence problems, an adaptive Simpson‟s method is employed in the model devices. Another main contribution to this field is the highlighting of the differences in the output characteristics of carbon nanotube- and graphene-based devices. In addition, the source and drain contacts that give an optimum device performance are identified. The limitation of this model is that quantised conductance appears on making contact to the nanotube ends. Electron transport in carbon nanotubes can be studied using non-contacting means. A new approach is to induce current in the nanotubes using microwave energy. A resonator-based measurement method is used to examine the electrical properties of the nanotubes. Remarkably, the nanotubes appear to have the smallest sheet resistance of any non-superconducting material. The possibility of a ferromagnetic carbon nanotube is investigated due to the remarkable screening properties observed. Measurements of the magnetisation as a function of the applied magnetic field are conducted using a vector vibrating sample magnetometer. The morphology and microstructure of the nanotubes are observed using scanning electron microscopy (SEM) and transmission electron microscopy (TEM), respectively. Carbon nanotubes can be contaminated with metal particulates during growth. These impurities can modify charge transport in these carbon structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".