Expanded Liquid Phases in Catalysis: Gas‐expanded Liquids and Liquid–Supercritical Fluid Biphasic Systems
Bibliographic record
Abstract
Abstract The sections in this article are A Practical Classification of Biphasic Systems Consisting of Liquids and Compressed Gases for Multiphase Catalysis Physical Properties of Expanded Liquid Phases Volumetric Expansion Density Viscosity Melting Point Interfacial Tension Diffusivity Polarity Gas Solubility Chemisorption of Gases in Liquids and their Use for Synthesis and Catalysis In SituGeneration of Acids and Temporary Protection Strategies Switchable Solvents and Catalyst Systems Using Gas‐expanded Liquids for Catalysis Motivation and Potential Benefits Sequential Reaction–Separation Processes Tunable Precipitation and Crystallization Tunable Phase Separations Tunable Miscibility Hydrogenation Reactions Carbonylation Reactions Oxidation Reactions Miscellaneous Why Perform Liquid–SCFBiphasic Reactions? By Necessity (Unintentional Immiscibility) To Facilitate Post‐Reaction Separation To Facilitate Product/Catalyst Separation in Continuous Flow Systems To Stabilize a Catalyst To Remove a Kinetic Product To Control the Concentration of Reagent or Product in the Reacting Phase To Permit Emulsion Polymerization To Create Templated Materials Biphasic Liquid–SCFSystems Solvent Selection Aqueous–SCFBiphasic Systems Ionic Liquid–SCFBiphasic Systems Polymer–SCFBiphasic Systems Liquid Product–SCFBiphasic Systems Biphasic Reactions in Emulsions Water‐in‐SCFInverse Emulsions SCF‐in‐Water Emulsions Ionic Liquid‐in‐SCFEmulsions Applications of Emulsions
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".