Microfluidic origami: a new device format for in-line reaction monitoring by nanoelectrospray ionization mass spectrometry
Bibliographic record
Abstract
Microfluidics is an attractive platform for chemical synthesis because it offers fast reaction times, reduced reagent usage, and the ability to integrate multiple functions on a single device. Digital Microfluidics (DMF) is particularly well-suited for microscale chemical synthesis, as it permits discretized sample handling, allowing for total process control. However, a limitation of DMF-based synthesis is analysis, which is often performed offline. To this end, we have developed "microfluidic origami", a new device format that integrates DMF with in-line analysis by mass spectrometry (MS). This format comprises a DMF platform and a folded nanoelectrospray ionization (nanoESI) emitter formed on a single flexible polyimide film substrate. Additionally, the device contains a two-plate-to-one-plate DMF interface, which allows for straightforward coupling of micro-reaction operations and product delivery to the emitter for analysis. The integrated platform was used to perform the Morita-Baylis-Hillman (MBH) reaction using DMF followed by inline MS analysis for monitoring the reaction progress in real-time. We propose that this platform has potential as a new tool for real-time monitoring of reaction rates and reaction pathways and could be a useful addition to the synthetic organic chemistry laboratory.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".