A passive membrane system for on‐line mass spectrometry reagent addition
Bibliographic record
Abstract
RATIONALE: Post-separation addition of chemical modifiers in liquid chromatography-mass spectrometry is widely used for improving ionization sensitivity and selectivity. This is typically accomplished using a post-column T-junction, which can result in sample dilution and imperfect mixing. We present a passive semi-permeable hollow fiber membrane approach for the addition of chemical modifiers that avoids these issues. METHODS: Model compounds were directly infused by flow injection to an electrospray ionization triple quadrupole mass spectrometer after passing through a polydimethylsiloxane hollow fiber membrane. Ionization enhancement reagents were introduced into the flowing stream by membrane permeation from aqueous solutions. Ionization enhancement from volatile acids and bases in positive and negative electrospray ionization was evaluated to assess the feasibility of this approach. RESULTS: The membrane-based apparatus resulted in relative ionization enhancement factors of up to 14×, depending upon the analyte, reagent, and ionization mode used. Ionization enhancement signal stability is reasonable (relative standard deviation of 5-7%) for extended periods from the same reagent solution, and minimal analyte dilution is observed. A proof-of-concept demonstration of the chromatographic "trifluoroacetic acid fix" strategy is presented. CONCLUSIONS: An on-line mass spectrometry ionization reagent addition method with potential post-chromatography reagent addition applications was developed using a hollow fiber polydimethylsiloxane membrane. This approach offers a promising alternative to traditional methods requiring additional hardware such as pumps and T-junctions that can result in sample dilution and imperfect reagent mixing.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".