CVD grown nitrogen doped graphene is an exceptional visible-light driven photocatalyst for surface catalytic reactions
Bibliographic record
Abstract
Abstract The photocatalytic potential of large area CVD grown nitrogen doped graphene (NGr) has been explored though the chemical transformation of 4-nitrobenzene thiol into p,p′-dimercaptoazobenzene. Decoration of NGr with Ag nanocubes with rounded edges to form NGr/Ag nanohybrids resulted in a slight increase in the work-function and a decrease in the n-type character of NGr due to ground state transfer of negative charge from NGr to Ag. The Ag nanocubes exhibited a localized surface plasmon resonance (LSPR) at ~425 nm. When the NGr/Ag nanohybrids were illuminated with visible light of wavelength close to the LSPR peak, Kelvin probe force microscopy (KPFM) indicated a dramatic change in surface potential of −225 mV and Raman spectra detected electron accumulation in NGr, which are attributed to a high local field enhancement-mediated hot electron injection into NGr and the formation of long-lived charge separated states. Pristine nitrogen doped graphene and its coupled system with plasmonic Ag nanoparticles showed superior photocatalytic performance compared to bare plasmonic Ag catalyst. While standalone Ag NPs were unable to complete the transformation of 4-NBT into DMAB even at a laser power of 10 mW, NGr/Ag nanohybrids completed this transformation at a laser power of 1 mW, pointing to the high photoreduction strength of NGr/Ag. Density functional theory (DFT) based computational modeling was used to examine the electronic structure of graphene doped with graphitic, pyridinic and pyrrolic nitrogen dopant atoms. DFT results indicated an enhanced chemical reactivity of NGr due to stronger localization of charge at the dopant sites and a pronounced difference in the projected density of states (PDOS) for carbon atoms in proximity to, and distant from, the nitrogen dopant sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".