Corrosion Resistance of Electrochemically Exfoliated Doped-Graphene Nanostructures
Bibliographic record
Abstract
Graphene with its high conductivity and accessible surface area makes it an ideal material applicable as an adsorbent that can be electrochemically regenerated and reused over several cycles. However, it has been reported that corrosion causes degradation, weight loss and lifetime reduction of these adsorbents [1]. Most studies on corrosion mechanism in carbon materials has focused on highly porous and disordered materials used as catalyst supports for fuel cell electrodes. However, little work has been done to identify the corrosion mechanism in doped-graphene materials. The current study analyzes the corrosion resistance by using an accelerated corrosion protocol on electrochemically synthesized graphene samples with different doped heteroatoms including nitrogen and phosphorus. The accelerated corrosion protocol consisted of a potential steps whereby graphene was exposed to high potential for a few seconds for carbon corrosion to occur [2]. The corrosion mechanism and the degree of oxidation of the doped-graphene materials were characterized using cyclic voltammetry (CV), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). Electroactive groups, changes in the double layer capacitance, and morphological changes were identified using these techniques. The stability of samples was determined for different samples using the accelerated corrosion test. Interestingly, the peaks associated with electroactive oxygen-containing surface groups were present and showed the extent of corrosion for both graphene samples before and after the test. A higher degree of corrosion was observed from the phosphorus doped-graphene as compared to the nitrogen-doped graphene. This was further confirmed from the corrosion current and percentage change in gravimetric capacitance. The implication of these findings suggest that the large distortion caused by the additional P-doping in the carbon lattice allows open edge sites and produces wrinkles [3]; thus providing more defective sites and facilitating corrosion. Further materials analyses are ongoing to fully elucidate this process in detail and support the results obtained from the electrochemical tests. References [1] F. Sharif, L. R. Gagnon, S. Mulmi, and E. P. L. Roberts, “Electrochemical regeneration of a reduced graphene oxide / magnetite composite adsorbent loaded with methylene blue,” Water Res., vol. 114, pp. 237–245, 2017. [2] F. Forouzandeh, X. Li, D. W. Banham, F. Feng, S. Ye, and V. Birss, “Understanding the Corrosion Resistance of Meso- and Micro-Porous Carbons for Application in PEM Fuel Cells,” J. Electrochem. Soc., vol. 165, no. 6, pp. F3230–F3240, 2018. [3] C. H. Choi, S. H. Park, and S. I. Woo, “Binary and ternary doping of nitrogen, boron, and phosphorus into carbon for enhancing electrochemical oxygen reduction activity,” ACS Nano, vol. 6, no. 8, pp. 7084–7091, 2012. Figure 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 | 0.001 |
| 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 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".