Constant pressure‐assisted electrokinetic injection for on‐line enhanced detection of monophthalates in capillary electrophoresis‐mass spectrometry with application to human urine
Bibliographic record
Abstract
Electrophoresis characteristics of several monophthalates in the sample zone and EOF variation in a fused-silica capillary column during a constant pressure-assisted electrokinetic injection (PAEKI) in an on-line CE-MS were studied in an effort to reconcile the mobility theory and field amplification with the enhancement achieved in present work. Influences of capillary length on the amount injected using PAEKI were investigated in detail and except for the injection time, the amount injected was found to increase linearly with capillary column length. A longer capillary provides a longer linear increase time range with PAEKI injection. The results show that smaller m/z analytes generate a large enhancement power using PAEKI, which is in agreement with the mobility theory. ACN was used as an example to investigate influences of organic additives on the amount injected and was found to decrease the amount injected with PAEKI injection, which is in agreement with an increase of resistivity in running buffer by organic additives. The peak width obtained with PAEKI injection proved to be independent of the amount injected. The band size of the sample zone was estimated by comparison with conventional hydrodynamic injection. A 240 s PAEKI injection achieved the same size of sample zone as a 2 s of hydrodynamic injection. Existance of two ion layers around the boundary of the buffer and sample solutions in sample zone was hypothesized to contribute the narrow sample zone with a long time of PAEKI injection. With a 240 s on-line PAEKI injection in CZE-MS, five monophthalates were enriched several hundred times without compromise in their separation efficiency and peak shape. With appropriate sample cleanup, PAEKI was applied to the analysis of monophthalates in urine samples, achieving detection limits ranging between 0.53 and 1.3 ng/mL.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".