Development of an electrochemical cell composed of biochar coated ACF as an anode for the simultaneous electrocatalytic oxidation of manganese and hardness precipitation in groundwater
Bibliographic record
Abstract
Manganese is one of the most occurring heavy metals in the source and drinking water of rural and remote communities in Canada. The removal of manganese from groundwater was explored by designing a biochar-assisted electrochemical technology as a small-scale and chemical-free method. Electrocatalytic oxidation of manganese was acquired while utilizing wood residue biochar as a coating layer on the surface of activated carbon felt anode. The biochar-coated anode showed enhanced conductivity and surface area, consequently facilitating the electron exchange and manganese removal efficiency. Manganese initial concentration, current intensity, pH, and time were considered the main variables and their effect on the removal efficiency of manganese from groundwater was investigated. Current had the most influence on removal efficiency, as evidenced by the fact that no manganese removal occurred at zero current and the removal efficiency was increased by enhancing current from 25 to 75 mA. With the Mn initial concentration of 2 mg/L, current of 75 mA, pH 9, 97.5 % manganese removal efficiency was acquired, which helped to reduce the contaminant concentration under the maximum acceptable concentration. The results demonstrated a well-fitted pseudo-first-order model with a rate constant of 0.0411 min-1 for electrocatalytic oxidation. Real groundwater of Lheidli T'enneh community in Northern BC was employed to evaluate the impacts of co-existing ions on the manganese removal performance. The results confirmed that not only did the use of real groundwater have no detrimental impact on the system’s efficiency, but also the system was able to decrease the high hardness of 268.2 mg CaCO3/L in the groundwater to 72.2 mg CaCO3/L suitable for drinking purposes. Investigation of the manganese removal mechanisms indicated that various species, such as hydroxide ions, sulfate, and hydroxide radicals can play key roles in transferring or removing manganese from groundwater by oxidation and precipitation pathways. The formation of a black powder precipitated on the anode surface and in the cell after treatment proved that oxidation of manganese takes place in the system. Furthermore, the evolution of pH after 90 minutes of reaction further confirmed the presence of hydroxide ions in the system. Overall, the designed system achieved a significant performance in removing manganese and hardness from groundwater while utilizing biochar as a waste and cost-effective material.,
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".