Enhanced Adsorption Efficiency through Materials Design for Direct Air Capture over Supported Polyethylenimine
Bibliographic record
Abstract
Abstract Until recently, carbon capture and sequestration (CCS) was regarded as the most promising technology to address the alarming increase in the concentration of anthropogenic CO 2 in the atmosphere. There is now an increasing interest in carbon capture and utilization (CCU). In this context, the capture of CO 2 from air is an ideal solution to supply pure CO 2 wherever it is needed. Here, we describe innovative materials for direct air capture (DAC) with unprecedented efficiency. Polyethylenimine (PEI) was supported on PME, which is an extra‐large‐pore silica (pore‐expanded MCM‐41) with its internal surfaces fully covered by a uniform layer of readily accessible C 16 chains from cetyltrimethylammonium (CTMA + ) cations. The CTMA + layer plays a key role in enhancing the amine efficiency toward dry or humid ultradilute CO 2 (400 ppm CO 2 /N 2 ) to unprecedented levels. At the same PEI content, the amine efficiency of PEI/PME was two to four times higher than that of the corresponding calcined mesoporous silica loaded with PEI or with different combinations of C 16 chains and PEI. Under humid conditions, the amine efficiency of 40 wt % PEI/PME reached 7.31 mmol /g PEI , the highest ever reported for any supported PEI in the presence of 400 ppm CO 2 . Thus, amine accessibility, which reflects both the state of PEI dispersion and the adsorption efficiency, is intimately associated with the molecular design of the adsorbent.
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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 | 0.001 |
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".