Design and preparation of a simplified microdroplet generation device for nanoliter volume collection and measurement with liquid microjunction–surface sampling probe–mass spectrometry
Bibliographic record
Abstract
Abstract Given recent interest in laboratory automation and miniaturization, the microdroplet research space has expanded across research disciplines and sectors. In turn, the microdroplet field is continually evolving and seeking new methods to generate microdroplets, especially in ways that can be integrated into diverse (microfluidic) workflows. Herein, we present a convenient, low‐cost, and re‐usable microdroplet generation device, termed as the “NanoWand,” which enables microdroplet formation in the nanoliter volume range through modulated surface energy and roughness, that is, an open surface energy trap (oSET), using commercially available and readily assembled coating and substrate materials. A wand‐like shape is excised from a microscope glass cover slip via laser‐micromachining and rendered hydrophobic; a circle is then cut‐out from the hydrophobically modified wand's tip using laser‐micromachining to create the oSET. By adjusting the size of the oSET with laser‐micromachining, the volume of the microdroplet can be similarly controlled. Using liquid microjunction–surface sampling probe–mass spectrometry (LMJ‐SSP‐MS), specific NanoWand droplet capture volumes were estimated to be 117 ± 23.6 nL, 198 ± 30.3 nL, and 277 ± 37.1 nL, corresponding to oSET diameters of 0.75, 1.00, and 1.25 mm, respectively. This simple approach provides a user‐friendly way to form and transfer microdroplets that could be integrated into different liquid handling applications, especially when combined with the LMJ‐SSP and ambient ionization MS as a powerful and rapid analytical tool.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".