Effect of polyaniline-coated galvanized steel electrodes on electrokinetic sedimentation of dredged mud slurries
Bibliographic record
Abstract
An experimental study on electrokinetic improvement of dredged marine sediments to accelerate their sedimentation for land reclamation purposes is presented. Electrokinetic stabilization is currently used to improve soils; however, its use on soils with marine sediments with low permeability is still questionable due to the deterioration of anodes caused by an electrolysis reaction. A number of traditional methods are employed in literature to reduce the corrosion degradation of metals, such as painting, galvanizing, and conversion coating. Conducting polymers, e.g., polyaniline, are of engineering interest due to their properties such as ease of preparation and their high environmental stability in protecting metals from corrosion. For this purpose, the anodes used in the electrokinetic testing cell herein were coated with polyaniline to investigate the effect on electrokinetic stabilization of the dredged mud. Two series of experiments were performed using a polyaniline-coated galvanized steel anode, and two series of experiments with noncoated galvanized steel anodes were also carried out as a control. Depending on the applied voltage, the settlement and electroosmotic permeability of the dredged mud varied during the process. Polyaniline coating increased the power consumption during the electrokinetic stabilization compared to the case where the same electric potential was applied using the uncoated electrodes. However, when 5 V electric potential was applied to the soil through the polyaniline coated anode, its settlement and electroosmotic permeability were equivalent to what was observed with the 30 V electric potential applied through the noncoated anode, with 3 times less energy consumption.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".