Determination of Oil and Grease in Water and Soil by Liquid Extraction on a Centrifugal Microfluidic Device
Bibliographic record
Abstract
There is a need for field portable analyzers to rapidly test both liquids and solids for a variety of contaminants.Among these con-taminants are oil and grease(OG)in water and soil samples.These compounds cause environmental degradation and are public health risks.Microfluidic devices have the potential for revolutionizing analytical methods due to their low consumption of reagents,low production costs,short analysis time and minimal power and space requirements,making them very attractive economically and environmentally.While the growing analytical capabilities of centrifugal microfluidic systems are becoming well known,few prepare and analyze environmental samples on the same integrated device.Liquid-liquid extraction and solid-liquid extraction were achieved in our experiments using a magnetically driven extraction with mobile magnets to efficiently agitate samples to enhance the extraction process.This magnetically driven sample preparation unit was integrated on the centrifugal device with a sedimentation/filtration unit in the case of solid analysis as well as with an in-tegrated detection cell for both liquid and solid analysis.The extraction of OG from water and soil samples on the centrifugal microfluidic de-vices was presented.Tetrachloroethylene was used as the extractant as a Freon 113 substitute.OG was detected by IR spectrophotography,and a detection limit of 11 μg.mL-1 was obtained.
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 | 0.000 | 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) | 0.000 | 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".