Improving Oxidation Resistance of CO2-Adsorbents Modified with Dual Amino Silanes in the Presence of Air
Bibliographic record
Abstract
Amine-containing materials have been explored as potential CO2 capture adsorbents, but their low oxidative stability has been the most significant barrier to their practical implementation. In this work, adsorbents were prepared by two types of amino silanes, one contained tertiary amine and hydroxy groups (BHAPS), whereas the other had primary amine (APTMS), at different ratios ranging from 10% to 50% through 2-steps and single-step procedures. The combination of these amino silanes resulted in synergy, with the best oxidation stability obtained at a ratio of 20%BHAPS for 2-step and 50%BHAPS for single-step synthesis. The resulting sorbents exhibit a loss of CO2 capacity of around 70% after 190 h and 240 h aging in the air at 110 oC for 20%BHAPS (2-step) and 50%BHAPS (single-step), respectively. The results showed a slower deactivation rate than a typical APTMS/silica, which exhibits a total loss of 90% and 98% of CO2 uptake capability following the same treatment of 190 and 240 h. The stability of silica-grafted amino silanes showed that the oxidative stability of sorbents depended on the amino silane loading and oxidation duration. The results suggested that the hydroxyl groups of BHAPS help protect APTMS from oxidation. The surface characteristics of sorbents have a significant influence on O2 diffusion, regulating the oxidation rate. Attenuated total reflection-Fourier transform infrared spectroscopy, solid and liquid carbon and proton NMR, and thermogravimetric analysis were applied to characterize adsorbent properties.
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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 | 0.001 | 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".