A microdevice assisted approach for the preparation, characterization and selection of continuous aqueous two-phase systems: from micro to bench-scale
Bibliographic record
Abstract
Aqueous two-phase systems (ATPS) have emerged as an alternative strategy for the recovery and purification of a wide variety of biological products. Typical process development requires a large screening of experimental conditions towards industrial adoption where continuous processes are preferred. In this work, it was proved that under certain flow conditions, ATPS could be formed continuously inside a microchannel, starting from stocks of phase components. Staggered herringbone chaotic micromixers included within the device sequentially and rapidly prepare two-phase systems across an entire range of useful phase compositions. Two-phase diagrams (binodal curves) were easily plotted using the cloud-point method for systems of different components and compared with previously reported curves for each system, proving that phase formation inside the device correlated with the previously reported diagrams. A proof of concept for sample partitioning in such a microdevice was performed with two different experimental models: BSA and red blood cells. Finally, the microdevice was employed to obtain information about the recovery and partition coefficient of invertase from a real complex mixture of proteins (yeast extract) to design a process for the recovery of the enzyme selecting a suitable system and composition to perform the process at bench-scale.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".