Performance of an oscillatory electrode reactor for electrochemical phosphate removal
Bibliographic record
Abstract
ABSTRACT Efficient orthophosphate recovery from wastewater is essential for sustainable nutrient recycling. This study evaluated the effectiveness of an oscillatory electrode reactor (OER) employing sacrificial magnesium electrodes for enhanced phosphate removal, improved struvite recovery, and reduced electrode passivation. Baseline experiments in a batch electrochemical reactor (BER) identified optimal operating conditions and subsequently applied them to OER experiments by systematically varying oscillation frequency (0-8 Hz), amplitude (0–10 mm), and initial solution pH (range 4.0-10.0). Results indicated that electrode oscillation significantly improved phosphate removal efficiency, achieving approximately 86% removal compared to 48% without oscillations. Oscillation frequency had a greater influence on phosphate removal than amplitude, with maximum phosphate removal observed at 8 Hz, 10 mm amplitude, and at initial pH ~9.0. Oscillations also significantly reduced electrode passivation, maintaining stable electrode performance. A simplified model correlating phosphate removal enhancement with oscillatory shear rate was developed, achieving excellent agreement (R² = 1) with experimental data. The OER system demonstrates substantial potential as a chemical-free, sustainable, and efficient technology for orthophosphate recovery, supporting wastewater treatment strategies and circular economy objectives.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".