Effect of Low Cadmium Concentration on the Removal Efficiency and Mechanisms in Microbial Electrolysis Cells
Bibliographic record
Abstract
Abstract Microbial electrolysis cells (MECs) can be used to remove cadmium (Cd 2+ ) via three removal mechanisms: electrodeposition; chemical precipitation; and biosorption. Here, we investigated how cadmium concentration affects its removal mechanisms and efficiency in lab‐scale MECs. For 10, 50, and 100 μg‐Cd/L, cadmium was removed by electrodeposition and biosorption without chemical precipitation. The total amount of cadmium removed by electrodeposition increased from 0.96 to 7.7 μg with the increasing cadmium concentration while its fractional contribution was stationary at 8–10 %. The fractional contribution of biosorption dropped from 59 % to 4 % with the increasing cadmium concentration, but the mass removed by biosorption (3‐6 μg) was relatively unaffected by the initial cadmium concentration. For the low concentrations, the cadmium removal was not sufficiently high, varying 13 to 69 %. However, at 2.5 mg‐Cd/L, effective removal of cadmium (93 % removal in 7 days) was observed and electrodeposition made the largest contribution to cadmium removal (68 %) while chemical precipitation (18 %) and biosorption (14 %) was relatively minor. These findings showed that cadmium concentration governs the removal mechanisms as well as the removal efficiency in MECs.
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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 | 0.001 |
| 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 | 0.001 | 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".