Going with the Flow: Mechanistic Insights into Slow Mixing Mode Native Mass Spectrometry
Bibliographic record
Abstract
Slow mixing mode native mass spectrometry (SLOMO-nMS), which monitors the mixing of layered solutions within a nanoflow electrospray ionization (nanoESI) emitter, enables robust quantification of biomolecular complexes in vitro, even when absolute concentrations are unknown. The method relies on mass balance principles, assuming that the concentration of one of interacting species remains constant throughout the mixing process. While this condition is typically achieved by using identical starting concentrations in both solutions, deviations may arise due to non-uniform mass transport within the emitter. Here, we report the first quantitative investigation of the factors governing solution mixing and analyte transport in a nanoESI emitter under an applied electric field. Using a dual-emitter setup and a panel of dyes varying in size and charge, we dissected the contributions of diffusion, advection, and electrophoresis. Our results reveal that diffusion is the primary driver of mixing and, with advection, bulk transport. In contrast, electrophoretic displacement of analyte is negligible at typical nanoESI voltages. Notably, the effective flow rate associated with diffusion is comparable to the overall solution flow rates under low-voltage conditions, highlighting the importance of low-flow regimes for maintaining steady-state concentrations. These findings provide mechanistic validation for the mass balance assumptions underlying SLOMO-nMS and have broader implications for other long-duration nMS experiments that rely on stable solution-phase composition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".