Enhanced electrokinetic bioremediation by pH stabilisation
Bibliographic record
Abstract
A new technique to stabilise pH during electrokinetic bioremediation is investigated. The technique employs an anode and a cathode in the same compartment (anode−cathode compartment (ACC)) placed side by side at each end of the soil under treatment. Tests using ACC technique were performed along with control tests conducted using a conventional anode−cathode (CAC) configuration. The efficiency of ACC and CAC configurations in delivering nitrate inside soil was investigated. The results showed that, the pH values at the anode and the cathode compartments after CAC tests were around 2 and 11, respectively. For the ACC, the pH at the water compartments was between 7·2 and 7·8. The pore fluid pH at the end of the tests varied between 2·7 at the anode and 8·0 at the cathode in the CAC and remained close to the initial pH in the ACC. The nitrate concentration in CAC was high near the anode (2700 mg/l) but low in subsequent sections with lowest concentration near the cathode (105 mg/l). For the ACC, a relatively uniform nitrate concentration between 1300 mg/l and 800 mg/l was reported. The results showed that the novel ACC configuration is effective in stabilising the soil pH and distributing nutrients evenly across the soil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".