Vanadium-Doped Ti<sub>4</sub>O<sub>7</sub> Porous Transport Layers for Efficient Electrochemical Oxidation of Industrial Wastewater Contaminants
Bibliographic record
Abstract
Inexpensive electrode materials and effective cell designs are needed to advance electrochemical technologies for the oxidative treatment of wastewater. Novel vanadium-doped Ti4O7 porous transport layers (PTLs) used in a compact wastewater electrolyzer are developed and characterized and their performance for the electrochemical oxidation of synthetic wastewater is evaluated. An original analytical model predicting performance with the apparent mass transfer coefficient and cell potential is developed. The influence of operating parameters such as volumetric flow, current density, and PTL composition on performance is investigated. Decolorization and chemical oxygen demand (COD) removal of 100 mg L−1 of methyl orange (MO), an azo dye, in 1,500 mgNaCl L−1 is rapid with mass transfer coefficients as great as 377 ± 24 m s−1 for MO at 15 mA cm−2. After 2.5 Ah L−1 at 10 mA cm−2, >99 decolorization and >98% COD removal are achieved with a current efficiency of 19.2% and with specific and volumetric energy consumption of 120 and 84.1 kWh kg−1 for MO and COD, respectively, and 1.34 ± 0.09 and 6.45 ± 0.97 kWh m−3 order−1, respectively. A more energy-efficient electrochemical cell design for industrial wastewater treatment using less expensive high oxidation power (HOP) electrode materials is demonstrated with these results.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".