Digital microfluidics and nuclear magnetic resonance spectroscopy for <i>in situ</i> diffusion measurements and reaction monitoring
Bibliographic record
Abstract
In recent years microcoils and related structures have been developed to increase the mass sensitivity of nuclear magnetic resonance spectroscopy, allowing this extremely powerful analytical technique to be extended to small sample volumes (<5 μl). In general, microchannels have been used to deliver the samples of interest to these microcoils; however, these systems tend to have large dead volumes and require more complex fluidic connections. Here, we introduce a two-plate digital microfluidic (DMF) strategy to interface small-volume samples with NMR microcoils. In this system, a planar microcoil is surrounded by a copper plane that serves as the counter-electrode for the digital microfluidic device, allowing for precise control of droplet position and shape. This feature allows for the user-determination of the orientation of droplets relative to the main axes of the shim stack, permitting improved shimming and a more homogeneous magnetic field inside the droplet below the microcoil, which leads to improved spectral lineshape. This, along with high-fidelity droplet actuation, allows for rapid shimming strategies (developed over decades for vertically oriented NMR tubes) to be employed, permitting the determination of reaction-product diffusion coefficients as well as quantitative monitoring of reactive intermediates. We propose that this system paves the way for new and exciting applications for in situ analysis of small samples by NMR spectroscopy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".