Simultaneous removal of metal ions and oxidation of linear alkylbenzene sulfonate by combined electrochemical and photocatalytic processes
Bibliographic record
Abstract
Simultaneous electrochemical removal of Zn⁺₂ and Ni⁺₂ ions and photooxidation of linear alkylbenzene sulfonate (LAS) over TiO₂ particles were investigated. To achieve this objective, first, the effect of different variables such as current density, pH and flow rate on sole electrochemical reduction of metal ions was studied. Both controlling pH in the range of 5.5-6 and increasing the liquid volumetric flux effectively improved the rate of Zn⁺₂ ion reduction, but they did not have any significant effect on the rate of Ni⁺₂ ion reduction. Under optimum operating conditions ... and using total electrolyte volume of 6 L, zinc and nickel were reduced by 86% and 53% respectively, over a 7-hour treatment period. Sole photocatalytic treatment of LAS and controlling pH between 5-5.5 resulted in 60% LAS degradation. However, temperature and flow rate did not have any considerable effect on the rate of LAS degradation compared to the photocatalytic system alone. LAS was degraded in the combined system by 76% compared to 60% in the sole photocatalytic system. However, using the combined system, zinc and nickel were reduced by 81% and 47% respectively, which were slightly less than those obtained in the electrochemical system alone.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".