Quantification of free, dissolved combined, particulate, and total amino acid enantiomers using simple sample preparation and more robust chromatographic procedures
Bibliographic record
Abstract
The goal of this work was to develop amino acid (AA) quantification protocols using simple sample preparation steps and new chromatographic conditions optimized for a hybrid (organo‐silica) C18 column resistant to alkaline samples in borate buffer. This resistance allows for larger injection volumes, and thus stronger detected signals (~3 times stronger), higher precision, and lower analysis costs compared with previous methods. Enantiomeric and non‐enantiomeric separations of AA were possible in less than 0.5 mL of AA‐poor water samples. Results showed that a high salt content and the sample treatment do not lead to AA loss or separation interference. The accuracy and the precision of AA quantification in a natural sample strongly depend on the blank correction. The limits of detection, measured based on the variability of these blanks, were 0.007–3.6 nM depending on the AA. The protocols developed here allow for the quantification of all forms of AA (i.e., free, dissolved combined, particulate, and total) in most natural waters, including deep ocean waters. It was even possible to quantify many AA in ultrapure water after blank correction. Despite the injection of a relatively high volume (100 µL) of alkaline borate solutions, the hybrid C18 column showed no sign of performance degradation after more than 700 injections. This durability reduces the cost of AA analysis and provides a more consistent separation. The analysis time can be greatly reduced by the use of stationary phases with a nonporous core and extended pH stability as they become available.
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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 | 0.001 | 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) | 0.000 | 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".