Synthesis of Novel Graphene Composite Adsorbent for Water Treatment By Adsorption and Electrochemical Regeneration
Bibliographic record
Abstract
One of the efficient and cost effective ways to remove organic contaminants from water is adsorption coupled with electrochemical oxidation. Graphite intercalated compounds (GIC) and activated carbons (AC) are among the common adsorbents for the adsorption - electrochemical regeneration process; nevertheless, they have significant shortcomings, i.e. GIC has low adsorptive capacity, whereas AC requires long regeneration time and high energy consumption associated with low current efficiency and high cell voltages. Owing to their huge surface area and high electrical conductivity, graphene-based materials are promising candidates as adsorbents suitable for electrochemical regeneration. This results in higher adsorptive capacity and current efficiency compared to AC and GIC. In this study, we prepared different types of graphene-based materials, namely graphene foam and magnetite reduced graphene oxide (rGO). The synthesized materials were characterized and tested for adsorption and electrochemical regeneration. Our findings revealed that, in addition to high current efficiency (greater than 80 %), these materials had a good adsorptive capacity compared to GIC. However, during the regeneration, graphene was oxidized and corroded after only a few cycles, contaminating the treated water. In the case of graphene foam, the integrity of the foam was lost after electrochemical regeneration. In order to overcome this corrosion problem and increase the current efficiency, a new composite was developed that is resistant to oxidation. The composite was characterized using scanning electron microscopy, transmission electron microscopy, X-ray diffraction and Raman spectroscopy. Methylene blue was employed as a model adsorbate in synthetic wastewater samples. Regeneration was carried out in an electrolytic cell operated at a constant current. In spite of the slight reduction in adsorptive capacity compared to the pure graphene, the composite facilitated the oxidation of the organics to an extent in which a current efficiency of around 100 % was achieved. TEM images indicated that the composite was not significantly corroded during the electrochemical treatment. Even after ten cycles of adsorption and regeneration, no reduction in the adsorptive capacity and no contamination of the treated water was observed. In brief, the results of the current study introduce graphene composites as efficient materials for the adsorption - electrochemical regeneration process.
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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".