Study of Graphene FOAM Characteristics: Adsorption and Electrochemical Regeneration
Bibliographic record
Abstract
During extraction of bitumen at high temperatures, release of constituents of oil and bitumen into produced water occurs. Naphthenic acids (NAs) are of those constituents which contaminate the produced water and end up in accumulation in tailing ponds. NAs removal form produced water is vital due to their toxicity for living beings as well as their corrosive nature. Pipeline and separation system in oil and gas industry can be damaged by NAs effect. One of the processes which contribute to NAs removal from produced waters is adsorption [1, 2]. Some adsorbents have been recently developed to address environmental challenges associated with removal of dissolved organics like NAs from contaminated water. New adsorbents should feature important properties such as high surface area, high electrical conductivity, homogeneous dispersion, ability to remove various contaminants, and easy separation from treated water. Graphene is a two-dimensional pure carbon with one-atom-thick honeycomb arrangement, and possesses a perfect sp2 hybrid carbon nanostructure. Most of above mentioned properties can be attributed to graphene, which turn it to an important nanomaterial. As a nonmaterial, graphene cannot be used only as adsorbent, but it can be also employed as nanoelectronics, nanocomposites, nanosensors, and nanodevice [1]. However, graphene naturally tends to agglomerate in the liquid, and thus, turn into graphite. It leads to tremendous reduction in graphene surface area that decreases its adsorptive capacity. Besides, utilized graphene cannot be separated from wastewater readily. Nowadays, prevention from graphene aggregation is a big challenge in its utilization as adsorbent. Several methods have been proposed to overcome this problem; one of them is to assemble the graphene sheets in the form of foam [3]. Application of adsorption process using graphene foam as adsorbent is beneficial as it can be regenerated and reused. Different techniques can be implemented for regeneration purpose, including thermal, solvent, microbial and electrochemical methods. Owing to high carrier mobility of graphene at room temperature, application of electrochemical method can be the best choice. While electrochemical regeneration of some adsorbents such as activated carbon [4] and Nyex [5] has been the subject of many reports, electrochemical regeneration of graphene foam has not been studied, to the best of our knowledge. In this study, removal of NAs by adsorption on graphene foam and electrochemical regeneration of adsorbed naphthenic acid were investigated. Graphene foam was synthesized by two different methods. Resulted foams were characterized by Raman spectroscopy, Fourier Transformed Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM). Formation of Graphene foam was proven by data obtained by Raman and FTIR tests. SEM images confirmed porous structure of synthesized graphene foam. Electrical resistivity of 0.5 Ω.cm was measured for synthesized graphene foam by Low Resistivity meter, alluding its high conductivity. Adsorption experiments were carried out in 50 ml beakers. Constant initial concentrations of NAs were maintained during the experiment, which was corresponded to COD of 350 ppm. Solutions were well mixed with known mass of graphene foam by magnet stirrer. NAs residual concentrations were measured by COD test. Electrochemical regeneration was accomplished inside a 50ml cell with NaCl solution (1.0M) as electrolyte, stainless steel plate as cathode, and graphite plate as anode. 150 mA dc current was applied for regeneration. Adsorption capacity of regenerated graphene foam was confirmed through another set of experiment with the same solution. Thus, regeneration efficiency of 81% was achieved for graphene foam. References 1. Liu, F., et al., Three-Dimensional Graphene Oxide Nanostructure for Fast and Efficient Water-Soluble Dye Removal. ACS Applied Materials & Interfaces, 2011. 4(2): p. 922-927. 2. Deriszadeh, A., T.G. Harding, and M.M. Husein, Improved MEUF removal of naphthenic acids from produced water. Journal of Membrane Science, 2009. 326(1): p. 161-167. 3. Xu, Y., et al., Self-Assembled Graphene Hydrogel via a One-Step Hydrothermal Process. ACS Nano, 2010. 4(7): p. 4324-4330. 4. Karimi-Jashni, A. and R. Narbaitz, Electrochemical Reactivation of Granular Activated Carbon: Effect of Electrolyte Mixing. Journal of Environmental Engineering, 2005. 131(3): p. 443-449. 5. Brown, N.W. and E.P.L. Roberts, Electrochemical pre-treatment of effluents containing chlorinated compounds using an adsorbent. Journal of Applied Electrochemistry, 2007. 37(11): p. 1329-1335.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".