Desorption of harmful hydrocarbon compounds in soil using micron-sized magnetic particles and high-frequency magnetic fields
Bibliographic record
Abstract
This research looks at the use of high frequency (HF) magnetic fields to desorb/reduce harmful chemical compounds within gasoline and diesel that commonly leak out of underground storage tanks. Using a multi-strand coil design, measured magnetic fields of over 3 kilo-amperes per meter are generated at an optimal frequency of 117 kHz without skin-depth losses, and without the use of expensive super-conductors or liquid-cooled mechanisms. This high frequency magnetic field is successfully used in non-contact-based magnetic heating and desorption of Gasoline and Diesel mixed with sand, water and easily-dispersible, benign micrometer-sized iron filings, used as a magnetic absorber. Gas chromatography (GC) tests done on magnetically-heated Gasoline-soil and Diesel-soil mixtures show desorption/reduction of gasoline and diesel by 44% and 51% respectively, but desorption/reduction of harmful BTEX compounds and other chemical irritants within Gasoline and Diesel by 28-66% after only 80 minutes of magnetic heating. Review of remediation/desorption methods show magnetic fields fare favorably in comparison to other methods that require longer treatment period or the use of secondary pollutants at reducing hydrocarbon and BTEX compounds in them.
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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) | 0.000 | 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) | 0.001 | 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".