Density Functional Theory Study of 1,2-, 1,3- and 1,4-Dinitrogen Doping in Graphene
Bibliographic record
Abstract
The prompt development of carbon-based nanostructured materials has received a wide-spread interest due to their novel physico-chemical properties and applications [1].Modification of 2D graphene with heteroatom substitution is one of the efficient approaches to tuning its electronic and chemical properties.Nitrogen-doped graphene has stimulated lots of interest in manipulating the properties of graphene for applications in diverse research areas [2].Experimental and theoretical studies revealed that doping configuration of nitrogen dopants in graphene can significantly influence the material properties [3], [4], [5], [6].In this study, the finite-sized graphene model, C186H36, was considered to investigate double nitrogen doping within six-membered rings of graphene.Graphitic nitrogen atoms were employed due to their efficiency in fine-tuning the performance of oxygen reduction reaction (ORR) activity and opening of band gap.Three possibilities of dinitrogen doping are 1,2-, 1,3-and 1,4-substitutions, which may consider correspondingly as ortho-, meta-, and para-like substitutions.This comprehensive computational study presents the results of positional preference of double nitrogen doping in multiple non-equivalent six-membered rings.Our aim is to investigate the positional preference of the above-mentioned three different doping possibilities and to reveal any selectivity for doping of two nitrogen atoms within the rings.All structures were fully optimized using density functional theory (DFT) at B3LYP/6-31G(d) level.The energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) values for all the positional isomers were calculated at TPSSh/6-31G(d) level using B3LYP/6-31G(d) optimized geometries.Relative stabilities of these isomers generally follow the trend: 1,4-> 1,3-> 1,2substitution.Interestingly, the location of the ring plays a role in determining the relative stabilities of double nitrogen dopants.Our computational study predicts that the most stable isomers have two nitrogen atoms doped close to the zigzag edge.However, the nitrogen doping at the armchair edge provides the structures with high relative energy.The effect of nitrogen dopants in graphene on HOMO and LUMO energies and HOMO-LUMO energy gaps was analyzed.Our study indicates that the band gap can be opened by controlled nitrogen doping in the graphene model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".