<sup>13</sup>C chemical shift tensors in MOF <i>α</i>‐Mg<sub>3</sub>(HCOO)<sub>6</sub>: Which component is more sensitive to host‐guest interaction?
Bibliographic record
Abstract
Abstract Metal–organic frameworks (MOFs) are a class of important porous materials with many current and potential applications. Their applications almost always involve the interaction between host framework and guest species. Therefore, understanding of host–guest interaction in MOF systems is fundamentally important. Solid‐state NMR spectroscopy is an excellent technique for investigating host–guest interaction as it provides information complementary to that obtained from X‐ray diffraction. In this work, using MOF α‐Mg3(HCOO)6 as an example, we demonstrated that 13C chemical shift tensor of organic linker can be utilized to probe the host–guest interaction in MOFs. Obtaining 13C chemical shift tensor components (δ11, δ22, and δ33, where δ11 ≥ δ22 ≥ δ33) in this MOF is particularly challenging as there are six coordinatively equivalent but crystallographically non‐equivalent carbons in the unit cell with very similar local coordination environment. Two‐dimensional magic‐angle‐turning experiments were employed to measure the 13C chemical shift tensors of each individual crystallographically non‐equivalent carbon in three microporous α‐Mg3(HCOO)6 samples with different guest species. The results indicate that the δ22 component (with its direction approximately being co‐planar with the formate anion and perpendicular to the C−H bond) is more sensitive to the adsorbate molecules inside the MOF channel due to the weak C−H···O hydrogen bonding or the ring current effect of benzene. The 13C isotropic chemical shift, on the other hand, seems much less sensitive to the subtle changes in the local environment around formate linker induced by adsorption. The approach described in this study may be used in future studies on host–guest interaction within MOFs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".