Electrochemical Characterization of Graphene-like Materials Obtained By Electrochemical Exfoliation of Graphite
Bibliographic record
Abstract
Graphene and its derivatives have been widely used in electrochemical sensors due to their unique properties, including high electron conductivity and large surface area [ 1 ] , which are expected to significantly improve the sensitivity of sensors. With oxygen functional groups located at the edge and at the surface of the graphene sheets, graphene oxides exhibit better dispersion in water, biocompatibility, and higher affinity for specific molecules than graphene. [ 2 - 3 ] The electrochemical exfoliation of graphite in aqueous solution is a fast and easy method to obtain large amounts (1-2 g) of graphene oxide materials with tunable composition [ 4 ] . In this respect, it is necessary to investigate the physicochemical and electrochemical properties of electrochemically exfoliated graphene oxide (EGO) in order to choose the most suitable material to be used in electrochemical sensors. Here, we report a simple and facile method for the electrochemical characterization of a series of EGOs produced by electrochemical exfoliation of graphite [ 4 ] under different experimental conditions (electrolyte, applied potential difference, and thermal treatment). First, the EGOs were sonicated in water for three hours to obtain a uniform dispersion. Then, the EGO sheets were successfully assembled on a glassy carbon electrode (GCE) through an aminophenyl-film linker (AP) by the electrostatic attraction between positively charged amine groups and the negatively charged EGO sheets, and through the pi-pi stacking as well, Figure 1. [ 5 ] The electrodes modified with the EGOs (EGO/AP/GCE) were electrochemically characterized by cyclic voltammetry with 1 mM [Fe(CN) 6 ] 3-/4- redox couple, Figure 1, to determine the electrochemical surface area (ESA) through the Randles-Sevcik equation and to calculate the standard rate constant of electron transfer (k°) by the Nicolson method [ 6 ] . As a result, the synthesis conditions leading to the EGO with the highest electrochemical surface area and the highest standard rate constant were identified. Finally, the electrochemical response of the EGO materials was correlated to their chemical composition and structural defects determined by X-ray photoelectron (at%Oxygenated-C/at%C) and Raman (I D /I G ) spectroscopies, respectively. Both of the ESA and k° show a Vulcano dependence with the potential difference applied during the electrochemical exfoliation of graphite. The origin behind these trends will be presented and discussed. References (1) Novoselov, K. S.; Geim, A. K.; Morozov, S. V.; Jiang, D.; Zhang, Y.; Dubonos, S. V.; Grigorieva, I. V.; Firsov, A. A. Science 2004 , 306 , 666-669. (2) Deng, X.; Tang, H.; Jiang, J. Analytical and Bioanalytical Chemistry 2014 , 406 , 6903-6916. (3) Lee, J.; Kim, J.; Kim, S.; Min, D.-H. Advanced Drug Delivery Reviews 2016 , 105 , 275-287. (4) Ossonon, B. D.; Bélanger, D. Carbon 2017 , 111 , 83-93. (5) Elshafey, R.; Siaj, M.; Tavares, A. C. Analyst 2016 , 141 , 2733-2740. (6) Nicholson, R. S. Analytical chemistry 1965 , 37 , 1351-1355. Figure 1
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".