Electrochemical reactivation of granular activated carbon: pH dependence
Bibliographic record
Abstract
The objectives of this paper were to verify and to quantify the dependence of granular activated carbon (GAC) electrochemical reactivation efficiency on the extreme pH values that occur at the electrodes. Phenol-loaded GAC was reactivated using a bench-scale electrochemical reactor. The pH values in the cathode and anode compartments during reactivation were 12 and 2, respectively. The pH of the electrolyte controlled the reactivation efficiency, and cathodic reactivation efficiencies were about 30% higher at pH 12 than at pH 2. Cathodic reactivation was about 20% more efficient than the anodic reactivation. The cathode, the reducing electrode, generates OH – ions, which increase the local pH at the cathode. Reduced phenol adsorbability at high pH promotes desorption from previously loaded GAC, thus increasing the reactivation efficiency. Greater phenol desorption was observed at pH 12 when the GAC was subjected to a 50-mA current than in the absence of a current. Thus, electrochemical reactivation is more efficient than chemical reactivation. Key words: granular activated carbon, pH, electrochemical, regeneration, reactivation, adsorption, desorption, phenol, F-400.
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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".