Nitrogen-Doped Graphene Based Nanostructures for Energy & Catalytic Applications
Bibliographic record
Abstract
Carbon nanotubes (CNTs) and graphene attract enormous research attention for their outstanding material properties along with molecular scale dimension. Optimized utilization of the graphene based materials in various application fields inevitably requires the subtle controllability of their properties according to a specified target application. In this presentation, our recent research works associated to nitrogen-doped graphene based nanomaterials will be presented. Substitutional doping of CNTs and graphene with nitrogen (N) heteroelement could be achieved via pre- or post-synthetic treatment. The resultant N-doped CNTs and graphene demonstrate tunable workfunction, modulated charge carrier density and remarkably enhanced surface activity, including catalytic behavior, which could be employed for many different graphene based functional nanostructure or heterostructure formation. N-doped CNTs could be hybridized with metallic nanoparticles to accommodate plasmonic properties with charge selective carrier transport, which can be utilized for the effective enhancement of device efficiency of organic and perovskite solar cells. Various catalytic oxides or other ceramics, such as amorphous molybdenum sulfides, can be directly deposited at the surface of N-doped graphene based materials without any intermediate adhesive layer for high performance hybrid photocatalysts or electrocatalysts for oxygen reduction or hydrogen evolution reaction. N-dopant sites can initiate damage-free unzipping of graphene plane to greatly enlarge the surface area of N-doped CNT array, whose facile carrier transport along the highly crystalline unzipped nanoribbon structure can be utilized for ultrahigh power supercapacitors and so on.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".