Open Port Interface for Coupling Capillary Electrophoresis and Mass Spectrometry: Performance Evaluation for Capillary Isoelectric Focusing
Bibliographic record
Abstract
Capillary electrophoresis (CE) combined with mass spectrometry (MS) is a powerful analytical technique that utilizes the resolving power of CE and the mass-detection capabilities of MS. In many cases, CE is coupled to MS via a sheath-flow interface (SFI). This interface has a simple design and can be easily constructed; however, it often suffers from issues such as MS signal suppression, interference of MS and CE electrical circuits, and the inability to set an optical point of detection close to the capillary end due to the specific design of the coupling union. In this paper, we describe a novel coupling of CE and MS based upon the open port interface (OPI). The OPI differs from classical sheath flow interfaces by operating at flow rates at least 1 order of magnitude higher. In addition to the flow rate difference, the OPI provides more efficient mixing of the capillary eluates with the transport fluid and thus minimizes MS signal suppression. In this work, we compared the performance of OPI and SFI in a series of capillary isoelectric focusing (cIEF) experiments with 5 pI markers, carbonic anhydrase II and NIST antibody. The evaluation criteria for the comparison of the OPI and SFI were analytical sensitivity, reproducibility, and pI marker linearity. Given the extent of sample dilution in the OPI, we also compared the peak resolution determined using an upstream UV detector to those determined by the downstream mass spectrometer. The results suggested that the OPI configuration reduced signal suppression, with no adverse effect on peak resolution. In addition, the OPI provided better decoupling of the CE and MS potentials as well as reduced signal dependence upon the sheath liquid composition. While these results are preliminary, they suggest that the OPI is a viable approach for CE-MS coupling.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".