A Molecular-level Investigation of the Interactions between Organofluorine Compounds and Soil Organic Matter using Nuclear Magnetic Resonance Spectroscopy
Bibliographic record
Abstract
In this dissertation, the intermolecular interactions between soil organic matter (SOM) and organofluorine compounds have been studied at the molecular-level using Nuclear Magnetic Resonance (NMR) spectroscopy. NMR probes the local magnetic environment surrounding atomic nuclei, and is uniquely capable as an analytical tool to probe molecular environments in complex disordered materials, such as soils. Several NMR techniques were employed in this work, including Pulse Field Gradient (PFG)-NMR based diffusion measurements, solid-state cross-polarization (CP), saturation transfer difference (STD) spectroscopy, and reverse-heteronuclear saturation transfer difference (RHSTD) spectroscopy. Using organofluorine compounds as molecular probes, xenobiotic interactions with SOM were studied. Using 1H{19F} RHSTD, the interaction sites in humic acid for organofluorine compounds were identified by direct molecular-level methods. Protein and lignin were identified as major binding sites, with different preferences exhibited for these sites by dissimilar organofluorine compounds: aromatic organofluorine compounds display varied preference for aromatic humic acid sites while perfluorooctanoic acid exhibits near total selectivity for protein-derived binding sites. The mechanisms underlying these preferences were probed in the solution state. Using crucial knowledge from the humic acid studies, a detailed molecular-level investigation of xenobiotic interactions in an intact and unmodified whole soil was made possible. A direct and in situ elucidation of the components in soil organic matter that interact with small organofluorine xenobiotic molecules has been presented, allowing, for the first time, resolution of multiple interactions occurring for xenobiotics simultaneously at different sites within a whole soil.
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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".