Enhanced Cu (II) Removal by Iron-Graphite Electrocoagulation: Kinetics, Efficiency and Hydrogen Co-Production
Bibliographic record
Abstract
Electrocoagulation (EC) process is an effective electrochemical method of heavy metal removal in wastewater.The removal of Cu (II) in aqueous solutions using Fe-Gr electrodes was studied in the present study in both batch and continuous modes.This was to determine the efficiency of removal of Cu (II) under different operating conditions, such as current density, pH, and treatment time.The originality of the work is that it uses Fe-Gr electrodes, which have the coagulant effect of iron and the high adsorption capacity of graphite.The electrode performance is better, and the removal efficiency is higher for Cu (II) and hydrogen production.The specific surface area (BET) of graphite electrode is 2.6 (m 2 /g).The experimental results showed that the pseudo-second-order model was the best fit to the Cu (II) removal kinetics, where R 2 = 0.98, which was higher than that of the first-order model, R 2 = 0.86, indicating a strong correlation.Graphite offers high electrical conductivity and chemical stability, which facilitates faster electron movement and improved electrode kinetics.During batch experiments, an applied voltage of 50 V and an inter-electrode distance of 3 cm led to almost total removal of Cu (II) in 25 minutes.A 90% percent removal efficiency with H2 produced was attained with continuous EC experiments at a flow rate of 0.3 L/min and the weight of sludge 12 g.H2 gas generation (0.152, 0.244, and 0.366 L/min) respectively.This study emphasizes the prospect of Fe-Gr electrodes in enhancing the electrocoagulation process of wastewater treatment, which is more economical and environmentally friendly in controlling the contamination of heavy metals.
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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".