Noncovalent interactions between aromatic compounds and dissolved humic acid examined by nuclear magnetic resonance spectroscopy
Bibliographic record
Abstract
We examined the molecular-level interactions of aromatic compounds with a humic acid that was extensively characterized with one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy. Interactions of target compounds were evaluated by monitoring their NMR relaxation properties. Solid-state NMR revealed that the humic sample contains paraffinic carbon (31%), substituted aliphatic carbon (30%), and aromatic carbon (28%). The liquid-state experiments further identified amino acids, a range of carbohydrates, methoxylated aromatics (likely derived from lignin), and a series of aliphatic chains. The 13C spin-lattice relaxation time (T1) of site-specifically labeled naphthalene, 1-naphthol, and quinoline was found to decrease with increasing additions of humic acid. The 1H T1 values also were measured and demonstrated that the association with the humic acid was not specific because all the protons acquired the T1 value of the humic acid at the same rate. The lack of a chemical shift change and an increase in signal line broadening indicates that the interaction between these compounds and humic acid is noncovalent. These interactions were detected at low humic concentrations (5 mg C/L) and suggest that low concentrations of humic material, which are prevalent in both aquatic and terrestrial systems, will significantly affect the fate and transport of contaminants in the environment.
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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) | 0.001 | 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".