Infrared Study of Charge Carrier Confinement in Doped (6,5) Carbon Nanotubes
Bibliographic record
Abstract
Electronic degrees of freedom and their coupling to lattice vibrations in semiconductors can be strongly modified by doping. Accordingly, the addition of surplus charge carriers to chirality-mixed carbon nanotube samples has previously been found to give rise to a Drude-type plasmon feature as well as Fano-type antiresonances in the far- to mid-infrared spectral range (FIR/MIR). Here we investigate the FIR/MIR response of redox chemically doped semiconducting (6,5) carbon nanotubes (s-SWNTs). We find that, contrary to expectations, the Drude-type plasmon shifts to lower wavenumbers with increasing doping level. By means of Monte Carlo simulations of the optical response, we attribute this behavior to the confinement of excess charge carriers at low doping levels and their progressive delocalization when approaching degenerate doping. The coupling of vibrational modes to intraband excitations in the doped s-SWNTs can be probed via a double-resonance process similar to that responsible for the Raman D-band. The resulting Fano antiresonances shed new light onto the character and coupling of electronic and vibrational degrees of freedom in these one-dimensional semiconductors.
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 | 0.000 |
| 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".