Electrochemical Water Treatment Using Graphene, Graphene Foam and Graphene / Metal Oxide Composites
Bibliographic record
Abstract
Combining adsorption with electrochemical oxidation has been shown to be effective for the removal and oxidation of dissolved and dispersed organic contaminants from water, using a graphite flake adsorbent. In order to increase the adsorption capacity, we have investigated the use of graphene based adsorbents, which have high specific surface area combined with electrochemical activity. We have prepared and tested reduced graphene oxide (RGO), graphene foam and graphene metal oxide composite materials. RGO offer significantly higher adsorptive capacity than graphite flake. Similarly graphene foam was found to have a high adsorption capacity, although the adsorption kinetics were poor due to the slow diffusion of conatminants into the foam pores. A graphene foam would be more readily separated from the treated water and further work is planned to determine whether a flow through treatment process can be used to overcome the slow adsorption kinetics. However, during electrochemical oxidation, the structural integrity of the foam was compromised after only 1 or 2 cycles, presumably due to oxidation of the graphene. A graphene / iron oxide composite was preapred with ferromagnetic characteristics to enable separation from the treated water. The adsorption capcity was slightly reduced, but the electrochemcial regeneration performance was good. Oxidation of graphene remains a challenge, and work is ongoing to explore the use of graphene / titanium dioxide composites. The TiO2 will catalyse the organic oxidation and may also protect the graphene from oxidation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".