High-Pressure Studies of Flexible Metal-Organic Frameworks and Their Performance for CO2 Adsorption using Infrared Spectroscopy
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are crystalline porous materials comprising metal ions/clusters and organic linkers. MOFs feature very large surface area and broad tunability, which distinguish them from traditional CO2 adsorbents. High external pressure can significantly modify the framework structures and CO2 adsorption properties of MOFs. MIL-53(Al) and NH2-MIL-53(Al) exhibit excellent CO2 affinity by forming hydrogen bonds between bridging OH groups and adsorbed CO2. We used in situ infrared spectroscopy to investigate the high-pressure performance of their framework structures and CO2 adsorption capacities. Diamond anvil cell was employed to apply high pressures in gigapascal level.\nFor as-made MIL-53(Al), pressures-induced inter-framework hydrogen bonds between OH groups and free H2BDC molecules were observed. The IR spectra of activated MIL-53(Al) upon compression provided direct evidence of its extraordinary stability compared to as-made and CO2-loaded MIL-53(Al). Pressure-induced intra-framework hydrogen bonding interactions between OH groups and octahedral [AlO6] were observed in activated MIL-53(Al). Moreover, structural modifications of as-made and activated MIL-53(Al) were irreversible upon complete decompression. Pressure-enhanced CO2 adsorption in MIL-53(Al) was demonstrated. Upon complete decompression, considerable CO2 molecules remained in the framework. Activated NH2-MIL-53(Al) exhibited reversible pressure-enhanced intra-framework interactions via two types of hydrogen bonding: one was between -NH2 groups and octahedral [AlO6], the other was between OH groups and octahedral [AlO6]. For CO2-loaded NH2-MIL-53(Al), there were four different high-pressure adsorption sites co-existing upon compression: dimeric adsorption, large-pore adsorption, narrow-pore adsorption, and amino adsorption. We demonstrated that high pressures made the narrow-pore adsorption highly favored over the other three.
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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".