A Novel Technique for Rapid Extraction of Volatile Organohalogen Compounds from Low Permeability Media
Bibliographic record
Abstract
A new method for rapid extraction of Volatile Organic Compounds (VOC) from low permeability, unconsolidated geologic media such as clayey soil has been developed and tested using trichloroethylene (TCE) as the model compound. The technique is based on a combination of sonication and mechanical agitation of the samples. The sample vials, mounted in a special holder attached to an orbital shaker, were immersed in an ultrasonic bath during extraction. The extracts were analyzed by gas chromatography with electron capture detection (GC-ECD). The method was validated using clay samples from a TCE-contaminated industrial site by comparing TCE recoveries to those obtained by the standard methanol extraction. In all cases, the recoveries obtained with the new method were the same or better than the recoveries obtained with the reference method. The extraction time was shortened from 5 days with the standard method to less than 2 h with the new method. The new technique makes it possible to analyze a large number of samples in a short time, without the need for sample preservation and prolonged storage. It has good potential for on-site analysis to facilitate decisions while field investigations are in progress.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".