Ionization suppression effects with condensed phase membrane introduction mass spectrometry: methods to increase the linear dynamic range and sensitivity
Bibliographic record
Abstract
Condensed phase membrane introduction mass spectrometry (CP-MIMS) is an online analytical method that allows for the direct, trace level measurement of a wide range of analytes in complex samples. The technique employs a semi-permeable membrane that transfers analytes from a sample into a flowing acceptor solvent, which is directly infused to an atmospheric pressure ionization source, such as electrospray or atmospheric pressure chemical ionization. While CP-MIMS and variants of the technique have been in the literature for nearly a decade, much of the work has focused on instrument development. Few studies have thoroughly addressed quantitative methods related to detection limits, ionization suppression, or linear dynamic range. We examine ionization suppression in the direct rapid quantitation of analytes by CP-MIMS and introduce several analytical strategies to mitigate these effects, including the novel implementation of a continuously infused internal standard in the acceptor phase solvent, and modulation of acceptor phase flow rate. Several representative analytes were used to evaluate this approach with spiked, complex sample matrices, including primary wastewater effluent and artificial urine. Also reported are improved measured detection limits in the low part-per-trillion range, using a 'stopped-flow' acceptor mode.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".