Strong Influence of Amine Grafting on MIL-101 (Cr) Metal–Organic Framework with Exceptional CO<sub>2</sub>/N<sub>2</sub> Selectivity
Bibliographic record
Abstract
Matérial Institut Lavoisier (MIL)-101, one of the metal–organic frameworks containing numerous coordinatively unsaturated sites, as well as high specific surface area, was synthetized under different protocols for CO2 adsorption and separation from N2. To improve its CO2 adsorption capacity, different amounts [10, 25, and 40 wt % of tetraethylenepentamine (TEPA)] were grafted to the parental MIL-101. Results revealed that TEPA-MIL-101 (40 wt %) showed one of the highest CO2 adsorption capacities (i.e., 3.76 mmol g–1 at 298 K at 1 bar) among existing MIL-101 and its modified moieties. This amount was 1.56 times greater than parental MIL-101 (in spite of having superior textural properties), which adsorbed 2.41 mmol g–1 CO2 at the same operational conditions. This can be attributed to the presence of polar functional groups in the porous structure of MIL-101 that enhance the interaction between CO2 active sites on the adsorbent surface. Isosteric heat of adsorption of CO2 and N2 on TEPA-MIL-101 (40 wt %) were 35 and 18 kJ mol–1, respectively, based on the temperature-dependent form of the Freundlich model in the Clausius–Clapeyron equation. Ideal adsorption solution theory (IAST) was used for determination of CO2/N2 selectivity for a binary gas mixture, including 15% CO2 and 85% N2 at 298 K and at 1 bar. TEPA-MIL-101 (40 wt %) showed an exceptional CO2/N2 selectivity of 220. Adsorption kinetics study demonstrated that the Avrami model was the best model for fitting the experimental data, which denotes that more than one pathway exists in CO2 and N2 adsorption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 teacher head, 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".