Ultrarapid Desalting of Protein Solutions for Electrospray Mass Spectrometry in a Microchannel Laminar Flow Device
Bibliographic record
Abstract
The adverse effects of nonvolatile salts on the electrospray (ESI) mass spectra of proteins and other biological analytes are a major obstacle for a wide range of applications. Numerous sample cleanup approaches have been devised to facilitate ESI-MS analyses. Recently developed microdialysis techniques can shorten desalting times down to several minutes, the bottleneck being diffusion of the contaminant through a semipermeable membrane. This work introduces an approach that allows the on-line desalting of macromolecule solutions within tens of milliseconds. The device does not employ a membrane; instead, it uses a two-layered laminar flow geometry that exploits the differential diffusion of macromolecular analytes and low molecular weight contaminants. To maximize desalting efficiency, diffusive exchange between the flow layers is permitted only for such a time as to allow full exchange of salt, while incurring minimal macromolecule exchange. Computer simulations and optical studies show that the device can reduce the salt concentration by roughly 1 order of magnitude, while retaining approximately 70% of the original protein concentration. Application of this approach to the on-line purification of salt-contaminated protein solutions in ESI-MS results in dramatic improvements of both the signal-to-noise ratio and the absolute signal intensity. However, efficient desalting requires the diffusion coefficients of salt and analyte to differ by roughly 1 order of magnitude or more. This technique has potential to facilitate high-throughput analyses of biological macromolecules directly from complex matrixes. In addition, it may become a valuable tool for process monitoring and for on-line kinetic studies on biological systems.
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 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".