A passive membrane system for on-line mass spectrometry reagent addition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 0.001 |
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 it