Adsorption of gases in acetate functionalized silica: Experimental and Monte Carlo molecular simulation study
Bibliographic record
Abstract
Adsorption of carbon dioxide and other gases on a novel acetate functionalized silica adsorbent under various conditions is investigated experimentally and theoretically by grand-canonical Monte Carlo (GCMC) simulations. The acetate functional group has the capability to interact with the carbon atoms in CO 2 molecules due to the electron-donating properties of the carbonyl and ether groups. However, the acetate functional group has not yet been examined for CO 2 adsorption on silica. Adsorption of CO 2 , CH 4 , N 2, and H 2 was measured experimentally in the temperature range of 253–373 K and pressure range of 0–100 kPa. CO 2 showed significantly higher adsorption compared to other gases with maximum adsorption of ca . 32 cc/gr at standard condition (STP) at a pressure of 100 kPa and a temperature of 253 K. The recorded adsorption data could be fitted by Freundlich isotherms, indicating heterogeneous adsorption sites. To better understand the heterogeneous adsorption sites, GCMC simulations were used to examine the effects of pore size, temperature, pressure, concentration of functional groups in the silica matrix, and competitive adsorption. The GCMC data was in good agreement with the experimental data and suggested the oxygen-containing moieties (i.e., carbonyl and ether groups) on the acetate group as the adsorption sites. These sites displayed high Lewis acid-base interaction with the CO 2 molecules. The GCMC data indicated selective adsorption of CO 2 over N 2 and a CO 2 /N 2 binary gas mixture selectivity of 20 for a 10/90 CO 2 /N 2 feed. To the best of our knowledge, this is the first report on the experimental adsorption of CO 2 over acetate functionalized silica adsorbent coupled with an investigation of the adsorption sites through GCMC simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".