Tailored Design of Mesoporous Aminated Imidazolium Poly(ionic liquid)s: Practical Chemisorption Metric and Detailed Insights into CO <sub>2</sub> Sorption Behavior
Bibliographic record
Abstract
Mitigation against climate change requires efficient and selective CO 2 capture technologies, especially under low-concentration conditions. Herein, we report a reproducible and scalable synthesis of mesoporous aminated imidazolium-based porous poly(ionic liquid)s (PILs) via solvothermal radical polymerization. Despite moderate surface areas, these materials exhibit high CO 2 uptake (up to 2.5 mmol g –1 ) and CO 2 /N 2 selectivity at low CO 2 pressures, driven by the chemical interaction between CO 2 and the amine-functionalized imidazolium matrix. A practical screening metric, the Low-Pressure Uptake Efficiency (LPUE), was introduced to distinguish sorbents operating via physisorption from those favoring chemisorption. Our aminated porous organic polymers (POPs) displayed relatively high LPUE values (58–66%), indicative of sorption primarily driven by chemisorption. We systematically evaluated the influence of counteranion, cross-linker, amine substitution, and humidity. Solid-state nuclear magnetic resonance (ssNMR) experiments on 13 CO 2 -labeled samples revealed the formation of carbamate–ammonium and carbamic acid species under dry conditions, while water favored carbamate and allowed bicarbonate formation. Breakthrough experiments demonstrated enhanced CO 2 uptake under humid conditions, which is particularly relevant for direct air capture (DAC). Compared to the hydrophobic cross-linker divinylbenzene (DVB), the hydrophilic cross-linker exhibited excessive water uptake, limiting the performance under humid conditions. This dilemma was mitigated by introducing hydrophobic anions such as bis(trifluoromethanesulfonyl)imide (TFSI – ). These findings underscore the versatility of aminated imidazolium PILs for tailored CO 2 capture and highlight their potential for integration into sorption–desorption cycles.
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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) | 0.001 | 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".