Modular Type III Porous Liquids Based on Porous Organic Cage Microparticles
Bibliographic record
Abstract
Abstract The dispersion of particulate porous solids in size‐excluded liquids has emerged as a method to create Type III porous liquids, mostly using insoluble microporous materials such as metal–organic frameworks and zeolites. Here, the first examples of Type III porous liquids based on porous organic cages (POCs) are presented. By exploiting the solution processability of the POCs, racemic and quasiracemic cage microparticles are formed by chiral recognition. Dispersion of these porous microparticles in a range of size‐excluded liquids, including oils and ionic liquids, forms stable POC‐based Type III porous liquids. The flexible pairing between the solid POC particles and a carrier liquid allows the formation of a range of compositions, pore sizes, and other physicochemical properties to suit different applications and operating conditions. For example, it is shown that porous liquids with relatively low viscosities or high thermal stability can be produced. A 12.5 wt% Type III porous liquid comprising racemic POC microparticles and an ionic liquid, [BPy][NTf 2 ], shows a CO 2 working capacity (104.30 µmol g L −1 ) that is significantly higher than the neat ionic liquid (37.27 µmol g L −1 ) between 25 and 100 °C. This liquid is colloidally stable and can be recycled at least ten times without loss of CO 2 capacity.
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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) | 0.001 | 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".