Compositional Differences between Size Classes of Dissolved Organic Matter from Freshwater and Seawater Revealed by an HPLC-FTIR System
Bibliographic record
Abstract
The molecular complexity of dissolved organic matter (DOM) hinders its characterization. New approaches are thus needed for a better understanding of DOM reactivity and fate in aquatic systems. In this study, high-performance liquid chromatography (HPLC), using size-exclusion separation, was coupled with Fourier transform infrared spectroscopy (FTIR). A solvent-elimination interface was used to deposit DOM fractions onto a germanium disk that were then analyzed by FTIR. Samples included ultrafiltered DOM (UDOM) and fulvic acids from the St. Lawrence Estuary and its tributaries. Results showed significant compositional changes with molecular size and origin, especially in UDOM. Larger fractions of UDOM contained more carbohydrates, amides, aromatics/alkenes and aliphatics, while smaller fractions contained more carboxylate and OH groups. Small marine molecules (500-900 Da) were also enriched in sulfate groups that appeared bound to UDOM. Large marine molecules were the most amide-rich fractions. Fulvic acids were enriched in carboxylate and OH groups, showed little changes in composition, and appeared similar to small terrigenous (riverine) UDOM even in marine water. This work shows that an HPLC-FTIR system is a powerful and complementary tool in the characterization of DOM. The compositional changes observed may explain the reported contrasting reactivity and fate of DOM having different size and origin.
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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".