Mitigation of soil contaminated with diesel fuel using bioelectrokinetics
Bibliographic record
Abstract
This study investigated the effectiveness of bioelectrokinetics in rehabilitating a silty clayey sand contaminated with diesel fuel using three novel bacterial strains; Acinetobacter calcoaceticus, Sphingobacterium multivorum, and Sinorhizobium, isolated form agriculture land. Three electrokinetic bioremediation cells were used to conduct the tests and a novel electrode configuration technique was used to stabilize pH and water content in the soil specimen. Solar photovoltaic panels were used to generate sustainable energy for the process. The tests were carried out in outdoors for 55 days. Applied voltage, current passing through the electrokinetic cell, and the temperature of the soil specimen were recorded periodically during the test. The pH, water content, and diesel concentration were determined at the end of the tests. Over the test period, the voltage typically increased from zero before sunrise, remained relatively stabilized for about 4 h, and then started to decrease and dropped to zero by sunset. The temperatures in the cells were found to be 5-7 °C higher than the ambient temperature. The innovative electrode configuration succeeded in keeping the pH of soil to remain the same and thereby prevented the development of a pH gradient in the soil, an important development for survival of the bacteria. The diesel degradation in the soil after bioelectrokinetics were 20-30%, compared to 10-12% in the control test. The study was successful in developing environmentally friendly technology employing novel bacterial strains to degrade diesel fuel and utilizing solar panel to produce renewable energy for bioelectrokinetic during the winter season.
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 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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".