Experimental and modeling evidence of hydroxyl radical production in iron electrocoagulation as a new mechanism for contaminant transformation in bicarbonate electrolyte
Bibliographic record
Abstract
Iron electrocoagulation is designed for sustainable high-efficiency and high-flexibility water purification applications. Recent advances reported that hydroxyl radicals (•OH)-based oxidative transformation of organic contaminants can occur in iron electrocoagulation. However, there is still a lack of mechanistic understanding the production of •OH in bicarbonate electrolyte, which presents a critical knowledge gap in the optimization of iron electrocoagulation technology towards practical application. Combined with contaminant degradation, radical quenching experiments, and spectroscopic techniques, we found that •OH was produced at rate of 16.1 μM∙h − 1 during 30-mA iron electrocoagulation in bicarbonate electrolyte through activation of O2 by Fe(II) under pH-neutral conditions. High yield of •OH occurred at pH 8.5, likely due to high adsorbed Fe(II) that can activate O2 to enhance •OH production. Mössbauer and X-ray photoelectron spectroscopy measurements substantiated that Fe(II)-adsorbed lepidocrocite was the dominant solid Fe(II) species at pH 8.5. A process-based kinetic modeling was developed to describe the dynamic of •OH production, Fe(II) oxidation, and contaminant degradation processes in iron electrocoagulation. Findings of this study extend the functionality of electrocoagulation from phase separation to •OH-based advanced oxidation process, which provides a new perspective for the development of electrocoagulation-based next generation sustainable water purification 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) | 0.000 | 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 | 0.000 |
| 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".