Electrochemical Characterization and Application of Graphene Oxide Materials Obtained By Electrochemical Exfoliation of Graphite
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graphene oxide (GO) materials possess oxygen functional groups located at the edges and at the surface of the graphitic layers, confering them dispersibility in water, biocompatibility, and high affinity for specific molecules. These properties are highly appealing for their use in electrochemical sensors. However, the density and type of functional groups and defects in these materials also influence the heterogeneous electron transfer rate of redox processes occurring at the GO-based electrodes 1,2 . In this respect, it is necessary to investigate the physicochemical and electrochemical properties of GO materials to choose the most suitable one for a target application. Here, we report a simple and facile platform for the electrochemical characterization of GO materials 3 . The GO sheets are self-assembled on a glassy carbon electrode (GCE) through an aminophenyl-film linker (AP) through electrostatic interaction and pi-pi stacking, Figure 1a. Then, the modified electrodes are characterized by cyclic voltammetry with 1 mM [Fe(CN) 6 ] 3-/4- redox couple to determine the electrochemical surface area (ESA) through the Randles-Sevcik equation and to calculate the standard rate constant of electron transfer (k 0 ) by the Nicolson method. In this work, series of GO materials were obtained by electrochemical exfoliation of graphite in 0.1 M H 2 SO 4 . The electrochemical exfoliation of graphite in aqueous solution is an easy and “green” method that allows the synthesis of large amounts (in the order of grams) of materials (EGO: electrochemically exfoliated graphene oxide) with tunable composition in a short time (few hours). The applied voltage and the distance between the graphite and the counter electrodes were varied, Figure 1b, to obtain EGOs with different physicochemical properties such as the number of layers, structural defects, type and content of oxygenated groups. Transmission electron microscopy, Raman spectroscopy and X-ray photoelectron spectroscopy analysis were used to investigate the physicochemical properties of the EGOs. As shown in Figure 1c, the measured ESA and k 0 scale with each other and are sensitive to the physicochemical properties of EGOs. This confirms the suitability of the proposed platform to characterize the EGO materials 3 . Finally, selected EGO materials were used to fabricate electrochemical aptasensors for the detection of cocaine. The influence of the EGO physicochemical properties on the performance of the aptasensors will be presented and discussed. References (1) Kampouris, D. K.; Banks, C. E. Chemical Communications 2010 , 46 , 8986-8988. (2) Ambrosi, A.; Bonanni, A.; Sofer, Z.; Cross, J. S.; Pumera, M. Chemistry – A European Journal 2011 , 17 , 10763-10770. (3) Lei, Y.; Ossonon, B. D.; Chen, J.; Perreault, J.; Tavares, A. C. Journal of Electroanalytical Chemistry 2021 , 887 , 115084. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it