An Integrated Electrochemical System for Synergistic Cathodic Nitrate Reduction and Anodic Sulfite Oxidation
Bibliographic record
Abstract
Electrochemical reduction of nitrate has broad application prospects. However, in traditional electrochemical reduction of nitrate, the low value of oxygen produced by the anodic oxygen evolution reaction and the high overpotential limit its application. Seeking a more valuable and faster anodic reaction to form a cathode–anode integrated system with nitrate reaction can effectively accelerate the reaction rate of the cathode and anode, and improve the utilization of electrical energy. Sulfite, as a pollutant after wet desulfurization, has faster reaction kinetics in its oxidation reaction compared to the oxygen evolution reaction. Therefore, this study proposes an integrated cathodic nitrate reduction and anodic sulfite oxidation system. The effect of operating parameters (cathode potential, initial NO3−–N concentration, and initial SO32−–S concentration) on the integrated system was studied. Under the optimal operating parameters, the nitrate reduction rate in the integrated system reached 93.26% within 1 h, and the sulfite oxidation rate reached 94.64%. Compared with the nitrate reduction rate (91.26%) and sulfite oxidation rate (53.33%) in the separate system, the integrated system had a significant synergistic effect. This work provides a reference for solving nitrate and sulfite pollution, and promotes the application and development of electrochemical cathode–anode integrated technology.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".