Thermoelectric and electronic properties of B-doped graphene nanoribbon
Bibliographic record
Abstract
The effect of concentration and position of boron atoms as impurities in graphene nanoribbons have been studied through density functional theory (DFT) and the Landauer approach. For this purpose, we designed a graphene nanoribbon doped with three different concentrations of boron atoms and calculated the density of states (DOS), electronic current, and thermopower. The comparison between the DOS curve of the pure graphene and that of the boron-doped graphene shows that the presence of boron atoms as impurities in graphene has caused an energy gap near the Fermi energy. Moreover, the study of the I–V characteristics shows that not only the current quantization is established, but also the reduction in conductivity caused by doping with boron atoms can be observed; however, this reduction is not sensitive to the concentration of boron atoms. In addition, the changes of the Seebeck coefficient shows that in both pure and boron-doped graphene, the curve has a minimum value at the temperature of 10 K, which decreases to a lower value by increasing the concentration of boron atoms. Generally, the result of calculations shows that by increasing the boron concentration, not only the energy gap of graphene is changed, but also several changes appear in its thermoelectric properties that can be attributed to the impurity potential distribution. The results of this study can be effectively used for designing semiconductor electronic devices based on the graphene nanoribbon.
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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 | 0.000 |
| 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 | 0.001 | 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".