Rapid Chemical Reaction Monitoring by Digital Microfluidics‐NMR: Proof of Principle Towards an Automated Synthetic Discovery Platform
Bibliographic record
Abstract
Microcoil nuclear magnetic resonance (NMR) has been interfaced with digital microfluidics (DMF) and is applied to monitor organic reactions in organic solvents as a proof of concept. DMF permits droplets to be moved and mixed inside the NMR spectrometer to initiate reactions while using sub-microliter volumes of reagent, opening up the potential to follow the reactions of scarce or expensive reagents. By setting up the spectrometer shims on a reagent droplet, data acquisition can be started immediately upon droplet mixing and is only limited by the rate at which NMR data can be collected, allowing the monitoring of fast reactions. Here we report a cyclohexene carbonate hydrolysis in dimethylformamide and a Knoevenagel condensation in methanol/water. This is to our knowledge the first time rapid organic reactions in organic solvents have been monitored by high field DMF-NMR. The study represents a key first step towards larger DMF-NMR arrays that could in future serve as discovery platforms, where computer controlled DMF automates mixing/titration of chemical libraries and NMR is used to study the structures formed and kinetics in real time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".