Accelerated robotic discovery of type II porous liquids
Bibliographic record
Abstract
Porous liquids are an emerging class of materials and to date little is known about how to best design their properties. For example, bulky solvents are required that are size-excluded from the pores in the liquid, along with high concentrations of the porous component, but both of these factors may also contribute to higher viscosities, which are undesirable. Hence, the inherent multivariate nature of porous liquids makes them amenable to high-throughput optimisation strategies. Here we develop a high-throughput robotic workflow, encompassing the synthesis, characterisation and property testing of highly-soluble, vertex-disordered porous organic cages dissolved in a range of cavity-excluded solvents. As a result, we identified 29 cage-solvent combinations that combine both higher cage-cavity concentrations and more acceptable carrier solvents than the best previous examples. The most soluble materials gave three times the pore concentration of the best previously reported scrambled cage porous liquid, as demonstrated by increased gas uptake. We were also able to explore alternative methods for gas capture and release, including liberation of the gas by increasing the temperature. We also found that porous liquids can form gels at higher concentrations, trapping the gas in the pores, which could have potential applications in gas storage and transportation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".