(Invited) Dynamics of Nitrogen Functionalization of Graphene
Bibliographic record
Abstract
For graphene to be used efficiently in scientific and technological applications, its electronic properties need to be tailored to suit precise demands. The formation of a forbidden energy gap or the modification of the Fermi energy is achievable with functionalization techniques that simultaneously leave most of the atomic structure of graphene unchanged. Nitrogen functionalization is one of the most promising and studied types of treatment, in large part due to the proximity between carbon and nitrogen in the periodic table. It has already produced promising experimental results in the fields of high-sensibility biodetectors, field-effect transistors, solar cells, and supercapacitors, among others. To further understand this functionalization, we have performed an ab initio electronic structure study of different doping configurations and functionalization dynamics of nitrogen treatments of graphene. In particular, we have study the effect of simulation cell size used to carry our these calculations. We found that the inclusion of the special point K of the first Brillouin Zone in the momentum space sampling lowers formation energy results and should not be overlooked. Nudged Elastic Band calculations were also completed to study different incorporation mechanisms from an adsorbed state to in-plane doping. In the presence of native defects, low barriers of 0.55 eV and 0.46 eV were obtained. When no defects are present, much larger barriers between 3.70 eV to 4.38 eV were found, which suggests an external source of energy is required to complete the incorporation. In the second part of this presentation, we will report our result of using machine learning models to predict higher-level quantities. The goal of this work is to study the dependence of the Raman response of nitrogen functionalization of graphene. To simulate such a response, very large simulation cells need to be considered that would be out of reach for ab initio techniques, hence the use of machine learning methods to assist in the evaluation of the properties. Machine learning methods are now used more and more as a substitute for density functional theory calculations due to their low computational costs. However, in more advanced cases, relevant datasets are not always available, and the effort that would be necessary to generate the needed data suppress the advantages of using machine learning to speed up the calculations. Furthermore, the process of training a reliable model is not trivial and can also be expensive which should encourage the usage and diffusion of portable trained models. Figure 1
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".