Online High-Performance Size Exclusion Chromatography−Nuclear Magnetic Resonance for the Characterization of Dissolved Organic Matter
Bibliographic record
Abstract
The substantial heterogeneity of dissolved organic matter (DOM) inhibits detailed chromatographic analysis with conventional detectors as little structural information can be obtained in the presence of extensive coelution. Here we examine the direct hyphenation of high-performance size exclusion chromatography (HPSEC) with nuclear magnetic resonance (NMR) spectroscopy to determine how size-distinguished fractions differ in composition. The results support the applicability of using HPSEC to generate more homogeneous fractions of DOM prior to NMR analysis and demonstrate that structure is significantly altered with size. The largest fractions are enriched in carbohydrate- and aromatic-type structures. The midsized material is substantial and is representative of carboxyl-rich alicyclic molecules (CRAMs). The smallest material has strong signatures of material derived from linear terpenoids (MDLT). Both CRAMs and MDLT have been recently hypothesized as major components of DOM, and detection by HPSEC-NMR confirms their existence as unique and separable entities. This preliminary work focuses on NMR hyphenation to HPSEC due to widespread use of HPSEC to characterize DOM. Online hyphenation is useful not only for time-efficient analysis of DOM but also for that of other highly complex samples such as those found in many environmental analyses.
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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 | 0.001 |
| 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".