An Organic Solvent-Free Route for Preparing Silica-Alkoxylated Polyethylenimine Adsorbents for CO<sub>2</sub> Capture
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mesoporous silica-supported polyethylenimine (PEI) and, more recently, alkoxylated PEI (APEI) are promising adsorbents for CO 2 capture, displaying high adsorption capacity and selectivity. Wet impregnation is the established synthesis procedure for preparing silica-PEI. However, excessive quantities of organic solvents, particularly methanol, have invariably been used for both PEI alkoxylation and polymer mixing with silica. This study demonstrates an organic solvent-free synthesis method for 1) mesoporous silica preparation from sodium silicate solution, 2) PEI alkoxylation, and 3) the subsequent impregnation of silica-PEI using minimal water, typically with a water-to-silica mass ratio not exceeding 1.0. For large scale samples (up to 5 kg) preparation, controlled drying is essential to retain approximately 5 Wt.% moisture, preserving CO 2 capture performance. APEIs can be tailored for direct air capture (30 °C) and industrial processes (50 °C) by controlling the alkoxylation chemistry and degree. Silica-APEI exhibits enhanced oxidative stability and reduced moisture coadsorption, which extend operational lifespan and lower regeneration energy consumption. This water-based synthesis eliminates the need for excess organic solvents, such as methanol, preventing volatile organic compound (VOC) emissions, reducing drying energy consumption, and enhancing sustainability, making large-scale production commercially viable.
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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) | 0.002 | 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".