Microfluidic supercritical CO2 applications: Solvent extraction, nanoparticle synthesis, and chemical reaction
Bibliographic record
Abstract
Supercritical CO2, known for its non-toxic, non-flammable and abundant properties, is well-perceived as a green alternative to hazardous organic solvents. It has attracted considerable interest in food, pharmaceuticals, chromatography, and catalysis fields. When supercritical CO2 is integrated into microfluidic systems, it offers several advantages compared to conventional macro-scale supercritical reactors. These include optical transparency, small volume, rapid reaction, and precise manipulation of fluids, making microfluidics a versatile tool for process optimization and fundamental studies of extraction and reaction kinetics in supercritical CO2 applications. Moreover, the small length scale of microfluidics allows for the production of uniform nanoparticles with reduced particle size, beneficial for nanomaterial synthesis. In this perspective, we review microfluidic investigations involving supercritical CO2, with a particular focus on three primary applications, namely, solvent extraction, nanoparticle synthesis, and chemical reactions. We provide a summary of the experimental innovations, key mechanisms, and principle findings from these microfluidic studies, aiming to spark further interest. Finally, we conclude this review with some discussion on the future perspectives in this field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".