Effect of co-doping on dielectric function spectra and static refractive indices of single-walled carbon nanotubes: A first principles study
Bibliographic record
Abstract
This paper details a density functional theory (DFT) based ab initio study on the effect of co-doping on the dielectric function spectra and refractive indices of single-walled carbon nanotubes (SWCNTs). Dielectric function spectra of a pristine (8,0) SWCNT; (8,0) SWCNT co-doped with aluminum (Al) and phosphorus (P); (8,0) SWCNT co-doped with Al, P, and nitrogen (N); (9,0) SWCNT doped with Al; and (9,0) SWCNT co-doped with Al and boron (B) have been calculated using DFT-based Cambridge sequential total energy package (CASTEP) code. Polarized and unpolarized light as well as light through polycrystalline media have been considered. Analysis involves calculation and comparison of static refractive indices of the pristine and co-doped SWCNTs. Co-doping with Al and P results in a substantial increase in the value of the static refractive index while co-doping of Al, N, and P results in a reduction in the value of static refractive index though it does not fall lower than that of the pristine SWCNT. Thus, it can be concluded that co-doping with atoms of different combinations of elements can be evolved as a novel and effective tool for tuning the dielectric function and static refractive index values of SWCNTs. It will prove to be highly significant for effective designing of various sensitive optical devices using SWCNTs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".