Reverse of Mixing Process with a Two-Dimensional Electro-Fluid-Dynamic Device
Bibliographic record
Abstract
Mixing of two solutions into one is a spontaneous process with a net increase in entropy. However, the reverse of the mixing process is usually not possible unless certain conditions are met. A continuous solution stream containing a mixture of two compounds can be separated into two channels, each containing a pure compound, thus reversing the mixing process using a two-dimensional microfluidic electro-fluid-dynamic (EFD) device. When the electric field is strategically applied in the interconnecting channels of an EFD device, the pressure required to direct an analyte into a certain channel can be calculated by using the solutions of electric field and fluid dynamics in the mass balance equation. If the pressure and electric potential at various inlets and outlets satisfy these predetermined conditions, the reverse of a mixing process is observed. Conventional microfluidic devices have been used to introduce samples from interconnecting channels or efficiently mix different solutions into a single channel. The EFD devices expand the spatial separation of analytes from one dimension to two using both the differential migration behavior of analytes and the velocity field distribution in different channel geometries. The devices designed according to these basic physicochemical principles can be used for complete processing of minute samples and to obtain pure chemical species from complex mixtures.
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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) | 0.000 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".