Electrochemical Removal of Zinc and Nickel Ions from Wastewater Using Flat Plate Electrodes
Bibliographic record
Abstract
Simulated wastewater containing 20ppm of Zn++, 20ppm of Ni++ was treated using an electrochemical technique. This synthetic wastewater was used to simulate the wastewater from metal finishing industries. A rectangular bath integrated with an electrochemical cell consisting of flat plate electrodes (the stainless steel anode and aluminum cathode) was used in the treatment. Potassium sulfate was used as a supporting electrolyte to enhance the removal of Zn++ and Ni++. The effects of volumetric liquid flux, pH and electrode surface area on Zn++ and Ni++ removal were investigated. All experiments were performed at 25ºC and at an applied voltage of 4V. When volumetric flux was raised from 0.0092 to 0.0277m³.m-².s-¹, an increasing trend of the Zn++ and Ni++ removal was observed. The maximum metal removal was observed at a volumeteric liquid flux of 0.0231m³.m-².s-¹. Zn++ and Ni++ were removed by 80% and 34%, respectively, after 48 hours of electrochemical treatment. Moreover, an increase in the removal of Zn++ and Ni++ was observed when the pH was varied from 3.5 to 6.5. The maximum removal of Zn++ and Ni++, 97% and 62%, respectively, occurred at a volumetric liquid flux of 0.0231m³.m-².s-¹ and a pH of 6.5. The experimental values showed a similar increasing trend in the removal of Zn++ and Ni++, when the electrode surface area was increased from 0.024m² to 0.048m²; the removal of Zn++ and Ni++ improved by 14% and 12%, respectively. However, there was no major change in the removal of Zn++ and Ni++ between flat plate and corrugated plate electrodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".