Storing liquid chromatographic separations on surface energy traps: decoupling the LC and the mass spectrometer
Bibliographic record
Abstract
We report a micro-fractionation device for high performance liquid chromatography-mass spectrometry to archive chromatographic separations on an array of optimized surface energy traps (SETs). The method has the potential to significantly alter nanoflow LC-MS workflow, decoupling separation and analysis. The wetting characteristics of the SETs cause the HPLC eluent stream to spontaneously split into droplet microfractions. The droplet mirofractions are then dried down to enable facile storage and transport of the archived separation. Discontinuously dewetting array parameters were explored to maximize array volume and resolution using a combination of SET design, shape, size, and spacing. Mass spectrometry analysis is performed utilizing a liquid micro-junction surface sampling probe to extract dried analytes from the surface of the SETs followed by electrospray ionisation. A reverse phase separation of pharmaceutical compounds is "recorded" using the micro-fractionation device followed by "reading" the chromatographic trace with a mass spectrometer 24 hours after the separation was performed/archived, demonstrating a true decoupling of LC, and MS. Additionally, we demonstrate the ability to collect microfractions with sub-one-second integration time, approaching the scan time of a mass spectrometer or UV-Vis detector. With further improvements to the device, sub-1-second micro-fractionation may enable seamless reconstruction of archived chromatograms indistinguishable from online LC-MS data, while also providing the benefits of easy storage and transport of archived separations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".