Electrochemical Regeneration of Various Graphitic Adsorbents in an Air Agitated Sequential Batch Reactor
Bibliographic record
Abstract
With the aim to address the issues related to the regeneration of activated carbon used for wastewater treatment, a novel and state of the art water treatment technology (Arvia) was introduced at the University of Manchester, UK. This technology employs the adsorption of dissolved toxic pollutants present in water onto the surface of graphitic adsorbents followed by their quick and cheap electrochemical regeneration in a simple electrochemical reactor. The main mechanism of regeneration is based on the anodic oxidation of adsorbed species at the adsorbent surface. The adsorbed species are thought to be completely mineralized. However, some of the adsorbed organic contaminants may lead to the generation of toxic intermediate breakdown species owing to incomplete mineralization and/or indirect oxidation of organics present in solution. The former phenomenon may also depend upon the adsorption capacity of the adsorbent to be used. In this paper, a range of graphitic adsorbents including graphite intercalation compound-bisulphate (GIC-bisulpahte), recycled vein graphite (RVG) and exfoliated graphite (EG) were selected so as to investigate the formation of breakdown products during their electrochemical regeneration. Relatively fewer quantities of breakdown products in terms of p-benzoquinone and 4-chlorophenol were observed for EG. However, higher concentrations of oxalic acid were found for EG in comparison to GIC-bisulphate and RVG leading to conclude that the electrochemical degradation of phenol at the surface of exfoliated graphite could be through the direct oxidation of phenol into carboxylic acids.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".