Study of Extraction and Recycling of Switchable Hydrophilicity Solvents in an Oscillatory Microfluidic Platform
Bibliographic record
Abstract
One of the challenges in the development of green and sustainable chemical processing is solvent removal and subsequent replacement with another solvent in various steps of multistep syntheses, extraction, or purification. A promising alternative approach is the use of “switchable” solvents that change their properties on demand. The evaluation of the performance of switchable solvents in the entire extraction-recovery process, with a capability of efficient solvent recycling, is vital for their future applications. We report an oscillatory microfluidic platform to reproduce a complete cycle of CO 2 -mediated extraction and recovery of switchable hydrophilicity solvents, with the capability of solvent recycling. The evaluation of the efficiency of solvent extraction and recovery in the entire process is achieved within 1.5 h, with <15 μL of the solvent, without the formation of double emulsions, and with the capability to obtain the temporal information for each step of the process. The new methodology enabled time-, labor-, and cost- efficient screening of the performance and recovery of different switchable solvents, important for the development of new effective formulations and their use on industrial 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".