Investigation of heavy metal removal from salty wastewater and voltage production using <i>Shewanella oneidensis</i><scp>MR</scp>‐1 nanowires in a dual‐chamber microbial fuel cell
Bibliographic record
Abstract
Abstract Heavy metal removal and simultaneous energy production were studied using a dual chambered Microbial Fuel Cell inoculated with Shewanella oneidensis MR‐1 in the anode. Synthetic wastewater was prepared with Cu (II), Mg (II), Mn (II), Zn (II), Na, and Phenol based on desalter effluent from refinery processes at different metal concentrations. In this study, a maximum open‐circuit voltage of 517.6 mV was reached at Conc. 5 with wastewater in the anode chamber, and 127.7 mV at Conc. 3 was produced with synthetic wastewater in the cathode chamber. Moreover, μ at Conc. 5 was 0.1133 h −1 , demonstrating bacterial growth under metal and phenol concentrations. The highest metal removal in the anode for Cu (II), Mg (II), Mn (II), Zn (II), and Na was 93%, 85%, 93%, 88%, and 36%, respectively. In the cathode chamber the removal of Cu (II), Mg (II), Mn (II), Zn (II), and Na was 98%, 49%, 57%, 59%, and 36%, respectively. During the operation in the anode, SEM images showed that the bacterial nanowires are formed in response to toxic and anaerobic environments which contribute to the bacterial growth. These nanowires increased the metal removal and the voltage production as a consequence of a higher electron rate from the anode to the cathode due to the higher extracellular membrane surface area. S. oneidensis is a bacterium with metal‐reducing characteristics, and it is suitable for metal removal and electron transport from carbon sources, demonstrated in voltage production with microbial fuel cells.
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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) | 0.000 | 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".