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 CO 2 adsorption and separation from N 2 . To improve its CO 2 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 CO 2 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 CO 2 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 CO 2 active sites on the adsorbent surface. Isosteric heat of adsorption of CO 2 and N 2 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 CO 2 /N 2 selectivity for a binary gas mixture, including 15% CO 2 and 85% N 2 at 298 K and at 1 bar. TEPA-MIL-101 (40 wt %) showed an exceptional CO 2 /N 2 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 CO 2 and N 2 adsorption.
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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 | 0.000 |
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".