Enhancing CO<sub>2</sub> Adsorption in MIL-53(Al) through Pressure–Temperature Modulation: Insights from Guest–Host Interactions
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) have garnered significant attention for their exceptional CO 2 adsorption capabilities. Among them, MIL-53(Al) is uniquely known for its “breathing effect”, a reversible phase transition between large-pore and narrow-pore phases. While previous studies have explored the structural changes in MIL-53(Al) under varying conditions, this work represents an innovative investigation into the simultaneous effects of high pressure and high temperature on the CO 2 adsorption performance of MIL-53(Al). Utilizing a diamond anvil cell as the high-pressure device, we employed in situ Fourier transform infrared spectroscopy to examine the structural changes in activated MIL-53(Al) under compression and its CO 2 adsorption performance under specific simultaneous high-pressure and high-temperature conditions. Our findings reveal that heating serves as an effective strategy to augment CO 2 adsorption by enhancing the mobility of the CO 2 molecules under high pressure. Remarkably, the CO 2 adsorption capacity of MIL-53(Al) surged when subjected to pressures from 0.20 to 1.24 GPa and temperatures up to the melting point of CO 2 . Detailed spectral analysis further elucidated the chemisorptive nature of host–guest interactions between the framework and CO 2 . These findings significantly advance our understanding of MOFs’ potential for carbon capture across a broad pressure–temperature spectrum.
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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".