Detecting and quantifying organic contaminants in sediments with nuclear magnetic resonance
Bibliographic record
Abstract
ABSTRACT We have conducted proton nuclear magnetic resonance (NMR) measurements of relaxation times T1 and T2 as well as the diffusion coefficient D to detect and quantify gasoline, diesel, crude oil, and trichloroethylene (TCE) in sediment samples containing water. The sediment samples were coarse sand, fine sand, and a sand-clay mixture. We found that water, gasoline, diesel, and crude oil all exhibited similar signal amplitudes per unit volume, whereas TCE exhibited one-tenth the signal. The ability to use T2 measurements to distinguish the contaminant signal from the water signal depended on the bulk-fluid properties as well as the sediment texture and grain size. In the T2 distributions for samples containing equal volumes of contaminant and water, the contaminant signal could be resolved for crude oil in sand and for gasoline and TCE in the sand-clay mixture. Adding the diffusion measurement, using either pulsed or static gradients, made it possible to distinguish diesel and crude oil in all of the samples due to the large differences between the D of the contaminants and water. From the diffusion measurements, we were able to accurately quantify diesel and crude oil volumes ranging from 1% to 17% of the total sample volume. These methods could be applied in the field using NMR logging tools to quantify and monitor subsurface contamination.
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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 | 0.001 |
| 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 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".