Application of Sodium Dodecyl Sulfate Micellar Electrokinetic Chromatography (SDS MEKC) for the Rapid Measurement of Aqueous Phase 2,4,6-Trinitrotoluene Metabolites in Anaerobic Sludge: A Comparison with LC/MS
Bibliographic record
Abstract
The present study describes the application of sodium dodecyl sulfate micellar electrokinetic chromatography (SDS MEKC) for the fast analysis of TNT biotransformation products in the aqueous phase of anaerobic sludge cultures. SDS MEKC was performed using a Hewlett-Packard HP 3D CE capillary electrophoresis system with photodiode array detection. In a single sample injection the SDS MEKC method detected 2,4,6-trinitrotoluene (TNT), 2-hydroxylamino-4,6-dinitrotoluene (2-HADNT), 4-hydroxylamino-2,6-dinitrotoluene (4-HADNT), 2-amino-4,6-dinitrotoluene (2-ADNT), 4-amino-2,6-dinitrotoluene (4-ADNT), 2,4-diaminonitrotoluene (2,4-DANT), 2,6-diaminonitrotoluene (2,6-DANT), and 2,4,6-triaminotoluene (TAT). Analyte peaks were verified using visible/ultraviolet photodiode array spectra from commercial standards and periodic analyses using electrospray ionization liquid chromatography mass spectrometry (ES-LC-MS). A time course study constructed from the SDS MEKC data provided supporting evidence for the stepwise reduction of TNT to TAT. The SDS MEKC method was judged to be of practical value for the identification of polar and moderately polar TNT biotransformants in place of HPLC and LC-MS methods, with significant reduction in material expense and analysis time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".