Development of a Microfluidic Open Interface with Flow Isolated Desorption Volume for the Direct Coupling of SPME Devices to Mass Spectrometry
Bibliographic record
Abstract
Technologies that efficiently integrate the sampling and sample preparation steps with direct introduction to mass spectrometry (MS), providing simple and sensitive analytical workflows as well as capabilities for automation, can generate a great impact in a vast variety of fields, such as in clinical, environmental, and food-science applications. In this study, a novel approach that facilitates direct coupling of Bio-SPME devices to MS using a microfluidic design is presented. This technology, named microfluidic open interface (MOI), which operates under the concept of flow-isolated desorption volume, consists of an open-to-ambient desorption chamber ( V ≤ 7 μL) connected to an ionization source. Subsequently, compounds of interest are transported to the ionization source by means of the self-aspiration process intrinsic of these interfaces. Thus, any ionization technology that provides a reliable and constant suction, such as electrospray ionization (ESI), atmospheric-pressure chemical ionization (APCI), or inductively coupled plasma ionization (ICP), can be hyphenated to MOI. Using this setup, the desorption chamber is used to release target compounds from the coating, while the isolation of the flow enables the ionization source to be continuously fed with solvent, all without the necessity of employment of additional valves. As a proof of concept, the design was applied to an ESI-MS/MS system for experimental validation. Furthermore, numerical simulations were undertaken to provide a detailed understanding of the fluid flow pattern inside the interface, then used to optimize the system for better efficiency. The analytical workflow of the developed Bio-SPME-MOI-MS setup consists of the direct immersion of SPME fibers into the matrix to extract/enrich analytes of interest within a short period of time, followed by a rinsing step with water to remove potentially adhering proteins, salts, and/or other interfering compounds. Next, the fiber is inserted into the MOI for desorption of compounds of interest. Finally, the volume contained in the chamber is drained and moved toward the electrospray needle for ionization and direct introduction to MS. Aiming to validate the technology, the fast determination of selected immunosuppressive drugs (e.g., tacrolimus, cyclosporine, sirolimus, and everolimus) from 100 μL of whole blood was assessed. Limits of quantitation in the subppb range were obtained for all studied compounds. Good linearity ( r 2 ≥ 0.99) and excellent precision, with (8%) and without (14%) internal standard correction, were attained.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".