Practical application of <sup>1</sup>H benchtop <scp>NMR</scp> spectroscopy for the characterization of a nonaqueous phase liquid from a contaminated environment
Bibliographic record
Abstract
Abstract Nonaqueous phase liquids (NAPLs) located at the surface of the water table and/or below the water table are often a significant source for groundwater contamination near current or former commercial/industrial facilities. Due to the complex and long history of many industrial sites, these NAPLs often contain a complex mixture of contaminants and as such can be difficult to fully characterize using conventional analytical methods. Remediation and risk assessment activities at sites containing NAPLs may, subsequently, be hindered as the contamination profile may not be fully understood. This paper demonstrates the application of bench‐scale 1 H nuclear magnetic resonance (NMR) spectroscopy as a practical tool to assist with the characterization of complex NAPLs. Here, a NAPL collected from a contaminated site situated near a former chemical manufacturing facility was analyzed using a combination of one‐dimensional (1D) 1 H NMR spectroscopy and two‐dimensional (2D) 1 H J‐resolved spectroscopy (JRES). It is shown that 1D NMR experiments are useful in the rapid identification of the classes of compounds present, whereas 2D JRES NMR experiments are useful in identifying specific compounds. The use of benchtop NMR spectroscopy as a simple and cost effective tool to assist in the analysis of contaminated sites may help improve the practical characterization of many heavily contaminated sites and facilitate improved risk assessments and remedial strategies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".