Graphene Oxide-Based Electrodes with Constant Magnetic Field-Assisted Electrodeposition System to Raising the Efficient of Cu2+ Removal
Bibliographic record
Abstract
In this work higher Cu 2+ removal efficiency was achieved by graphene oxide GO-based electrode with the constant magnetic field (CMF)-assisted electrodeposition system.The GO was initially synthesized by the electrochemical exfoliation method in the electrolyte containing custom-made surfactant.The as-prepared GO solution was then spray coated on the pre-heated stainless steel-cathode before annealed at 400°C for 1 hour.GO-based electrode with CMF-assisted electrodeposition system was successful removed 89.1% of Cu 2+ from aqueous solutions as compared to the electrode without GO of only 75.5% of Cu 2+ removal within a fixed 3 hours' time.The efficient of Cu 2+ removal by GO-based electrode was believed due to the high specific surface area of GO that increases the accommodation of the active species (Cu 2+ ) at the electrode surfaces.In the meantime, it was found that the presence of CMF of 23 Gauss parallel to cathode surfaces enhances the convection and mass transport of Cu 2+ charged species to the electrode as compared to the absence of the CMF on an electrodeposition system.This phenomenon was due to the Magneto-Hydrodynamics (MHD) effect of CMF which is based on the Lorentz force that increases the Cu 2+ deposition efficiency by reducing the diffusion layer thus increasing the ions transfer into the electrical double layer.The electrodes before and after Cu 2+ deposition was examined by electron microscopy, energy dispersive x-ray, and Raman spectroscopy, and the concentration of remaining Cu 2+ in the solution was analyzed using atomic absorption spectroscopy.From the analysis done shows that the GO-based electrode with CMF-assisted electrodeposition system pave a new and pragmatic route for efficient removal of heavy metal ions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".