A Multifaceted Study of Methane Adsorption in Metal–Organic Frameworks by Using Three Complementary Techniques
Bibliographic record
Abstract
Abstract Methane is a promising clean and inexpensive energy alternative to traditional fossil fuels, however, its low volumetric energy density at ambient conditions has made devising viable, efficient methane storage systems very challenging. Metal–organic frameworks (MOFs) are promising candidates for methane storage. In order to improve the methane storage capacity of MOFs, a better understanding of the methane adsorption, mobility, and host–guest interactions within MOFs must be realized. In this study, methane adsorption within α‐Mg3(HCO2)6, α‐Zn3(HCO2)6, SIFSIX‐3‐Zn, and M‐MOF‐74 (M=Mg, Zn, Ni, Co) has been comprehensively examined. Single‐crystal X‐ray diffraction (SCXRD) experiments and DFT calculations of the methane adsorption locations were performed for α‐Mg3(HCO2)6, α‐Zn3(HCO2)6, and SIFSIX‐3‐Zn. The SCXRD thermal ellipsoids indicate that methane possesses significant mobility at the adsorption sites in each system. 2H solid‐state NMR (SSNMR) experiments targeting deuterated CH3D guests in α‐Mg3(HCO2)6, α‐Zn3(HCO2)6, SIFSIX‐3‐Zn, and MOF‐74 yield an interesting finding: the 2H SSNMR spectra of methane adsorbed in these MOFs are significantly influenced by the chemical shielding anisotropy in addition to the quadrupolar interaction. The chemical shielding anisotropy contribution is likely due mainly to the nuclear independent chemical shift effect on the MOF surfaces. In addition, the 2H SSNMR results and DFT calculations strongly indicate that the methane adsorption strength is linked to the MOF pore size and that dispersive forces are responsible for the methane adsorption in these systems. This work lays a very promising foundation for future studies of methane adsorption locations and dynamics within adsorbent MOF materials.
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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".