Electrochemical Regeneration of Reduced Graphene Oxide - Metal Oxide Composite Adsorbents
Bibliographic record
Abstract
Reduced graphene oxide (RGO) has been demonstrated to be an excellent adsorbent for many aqueous contaminants, including dissolved organics1. A challenge for practical application remains the disposal of the loaded adsorbent, with regeneration the most desirable outcome. Anodic electrochemical regeneration has successfully been applied for graphite flake adsorbent for organic contaminants2, however oxidation of the graphite has been observed3. In this study, anodic electrochemical regeneration of RGO and RGO metal oxide composites has been evaluated. Rapid corrosion of RGO was found occur during electrochemical regeneration, so that the reuse of the RGO was not possible after only a few cycles. The addition of metal oxide nanoparticles of metal oxides to the RGO surface was found to lead to more efficient oxidation adsorbed contaminants and almost no corrosion of the RGO was detected. References Hongmei Sun, Linyuan Cao, Lehui Lu (2011) Nano Research 4, pp 550-562. SN Hussain, EPL Roberts, HMA Asghar, AK Campen, NW Brown (2013) Electrochimica Acta 92, pp 20-30. K Nkrumah-Amoako, EPL Roberts, NW Brown, SM Holmes (2014) Electrochimica Acta 135, pp 568-577.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".