One-Step Synthesis of Aminobenzoic Acid Functionalized Graphene Oxide by Electrochemical Exfoliation of Graphite for Oxygen Reduction to Hydrogen Peroxide and Supercapacitors
Bibliographic record
Abstract
Graphene-based materials have attracted considerable attention as promising electrocatalysts for the oxygen reduction reaction (ORR) and as electrode materials for supercapacitors. In this work, electrochemical exfoliation of graphite in the presence of 4-aminebenzoic acid (4-ABA) is used as a one-step method to prepare graphene oxide materials (EGO) functionalized with aminobenzoic acid (EGO-ABA). The EGO and EGO-ABAs materials were characterized by FT-IR spectroscopy, X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray diffraction and scanning electron microscopy. It was found that the EGO-ABA materials have smaller flake size and higher density of oxygenated functional groups compared to bare EGO. The electrochemical studies showed that the EGO-ABA catalysts have higher activity for the ORR to H2O2 in alkaline medium compared to EGO due to their higher density of oxygenated functional groups. However, bare EGO has a higher selectivity for the 2-electron process (81%) compared to the EGO-ABA (between 64 and 72%) which was related to a lower content of carbonyl groups. The specific capacitance of the EGO-ABA materials was higher than that of EGO, with an increase by a factor of 3 for the materials prepared from exfoliation in 5 mM 4-ABA/0.1 M H2SO4. This electrode material also showed a remarkable cycling capability with a loss of only 19.4% after 5000 cycles at 50 mVs−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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".