Microbial desalination cell for concurrent hydrogen peroxide production and desalination
Bibliographic record
Abstract
Microbial desalination cells (MDCs) are novel desalination approaches because of using biocatalyst, with the ability to convert organic waste to bioenergy in various forms, to desalinate salty water. If MDCs were applied to the production of hydrogen peroxide, which is an important green oxidant, less energy-intensive and eco-friendly hydrogen peroxide production can be achieved during desalination. Here, we examined MDCs for concurrent hydrogen peroxide and desalination by varying an applied voltage, initial sodium chloride concentration, electrode materials, oxygen concentration, catholyte pH and catholyte concentration. The hydrogen peroxide production rate and salt removal rate (SR) increased by 0·033 kg (H2O2/m3)/h and 197·5 mg/(L·h) with increasing applied voltage. Carbon felt showed the highest hydrogen peroxide production (0·197 kg H2O2/m3) but the lowest SR (119·0 mg/(L·h)). Compared with air supply, pure oxygen supply dramatically improved hydrogen peroxide production (100%), but the SR only slightly improved (10%). With increasing sodium chloride concentration from 5 to 30 g/L, the hydrogen peroxide production and the SR increased, reaching 0·261 kg H2O2/m3 at 30 g/L and 236 mg/(L·h) at 20 g/L, respectively. The highest hydrogen peroxide production was achieved at catholyte pH 4, but the highest SR was achieved at pH 7. With increasing catholyte concentration from 5 to 100 mM, the hydrogen peroxide production and the SR increased.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".