Isolation and Quantification of Dissolved Lignin from Natural Waters Using Solid-Phase Extraction and GC/MS
Bibliographic record
Abstract
Solid-phase extraction (SPE) was tested for the isolation of dissolved lignin from diverse natural waters (fresh, estuarine, and marine) in preparation for CuO oxidation. Capillary GC coupled to selected-ion monitoring mass spectrometry (SIM-MS) of CuO oxidation products provides the high sensitivity and precision required for the identification and quantification of trace levels of lignin in seawater. The low blanks and quick cleanup of C18 cartridges support SPE for processing such samples. Comparison of SPE with other isolation procedures (direct dry-down and ultrafiltration) has shown that this method quantitatively recovers dissolved lignin and preserves its compositional parameters. The concentration and nature of dissolved organic matter appear to be primary factors that constrain the amount of water that should be processed to obtain quantitative and reproducible recoveries of dissolved lignin using SPE. Highest recoveries of dissolved lignin were obtained at low pH (1.5-4.0) with substantial decreases at pH > 4. Extraction efficiencies were independent of flow rate within a range of five to fifteen bed volumes per minute (50-150 mL min(-1)), and both refrigeration and freezing were appropriate long-term storage methods for processed cartridges prior to elution of retained dissolved lignin.
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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 | 0.001 | 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) | 0.000 | 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 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".