Milli‐free flow electrophoresis: I. Fast prototyping of mFFE devices
Bibliographic record
Abstract
We coin a term of milli-free flow electrophoresis (mFFE) to describe mid-scale FFE with flow rates intermediate to macro-FFE and micro-FFE (μFFE). Introduced decades ago, mFFE did not find practical applications. We revive mFFE, as we view it as a viable purification complement to continuous synthesis in capillary reactors with product flow rates of ∼5 to 2000 μL/min, too small for macro-FFE but too large for μFFE. The development of the tandem of continuous synthesis/purification will require the production and evaluation of a large number of prototypes of mFFE devices. As the first step, we developed a fast (<24 h) and economical (∼$10) method for prototyping mFFE devices using a robotic milling machine. mFFE prototypes are constructed from two machined matching poly(methyl methacrylate) (PMMA) substrates, which are bonded in 10 min using dichloromethane to provide a strong and irreversible seal. Using the developed prototyping technology, we designed and evaluated 25 prototypes of mFFE devices. By optimizing the feed rates and rotational speeds of the drills, the depth of the electrode channels, the dimensions of the entrance and exit reservoirs, the sample flow rate, and the diameter and position of the sample input, we were able to achieve indefinitely long operation of the device with cycles of alternating 15-min electrophoresis and 0.5-min regeneration (bubble removal). The test analytes, rhodamine B and fluorescein, were baseline resolved by mFFE for flow rates ranging from 10 to 600 μL/min. These results prove that our prototyping approach is suitable for the challenging task of multi-parameter optimization of mFFE devices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".