Water Purification and Electrochemical Oxidation: Meeting Different Targets with BDD and MMO Anodes
Bibliographic record
Abstract
The complex composition of natural organic matter (NOM) can affect drinking water treatment processes, leading to perceptible and undesired taste, color and odor, and bacterial growth. Further, current treatments tackling NOM can generate carcinogenic by-products. In contrast, promising substitutes such as electrochemical methods including electrooxidation (EO) have shown safer humic acid and algae degradation, but a formal comparison between EO methods has been lacking. In this study, we compared the Boron-doped diamond (BDD) electrode electrolysis performance for Suwannee River NOM degradation using mixed-metal oxide (MMO) anodes under different pH (6.5 and 8.5) representative of the high and low ranges for acidity and alkalinity in wastewater and applied two different current densities (10 and 20 mA cm−2). BDD anodes were combined with either BDD cathodes or stainless steel (SS) cathodes. To characterize NOM, we used (a) total organic compound (TOC), (b) chemical oxygen demand (COD), (c) specific ultraviolet absorbance (SUVA), and (d) specific energy consumption. We observed that NOM degradation differed upon operative parameters on these two electrodes. BDD electrodes performed better than MMO under stronger current density and higher pH and proved to be more cost-effective. BDD-SS electrodes showed the lowest energy consumption at 4.4 × 103 kWh kg COD−1. while obtaining a TOC removal of 40.2%, COD of 75.4% and SUVA of 3.4 at higher pH and current. On the contrary, MMO produced lower TOC, COD and SUVA at the lower pH. BDD electrodes can be used in surface water as a pre-treatment in combination with some other purification technologies to remove organic contaminants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".