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Record W2778490202 · doi:10.1021/acs.accounts.7b00357

Characterization of Metal–Organic Frameworks: Unlocking the Potential of Solid-State NMR

2017· article· en· W2778490202 on OpenAlexafffund
Bryan E. G. Lucier, Shoushun Chen, Yining Huang

Bibliographic record

VenueAccounts of Chemical Research · 2017
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharacterization (materials science)Solid-state nuclear magnetic resonanceMetal-organic frameworkSolid-stateChemistryMetalMaterials scienceNanotechnologyOrganic chemistryPhysical chemistryNuclear magnetic resonancePhysics

Abstract

fetched live from OpenAlex

Conspectus An exciting advance in materials science is the discovery of hybrid organic–inorganic solids known as metal–organic frameworks (MOFs). Although they have numerous important applications, the local structures, specific molecular-level features, and guest behaviors underpinning desirable properties and applications are often unknown. Solid-state nuclear magnetic resonance (SSNMR) is a powerful tool for MOF characterization as it provides information complementary to that from X-ray diffraction (XRD). We describe our novel pursuits in the three primary applications of SSNMR for MOF characterization: interrogating the metal center, targeting linker molecules, and probing guests. MOFs have relatively low densities, and the incorporated metals are often quadrupolar nuclei, making SSNMR detection difficult. Recently, we examined the local structures of metal centers (i.e., 25 Mg, 47/49 Ti, 63/65 Cu, 67 Zn, 69/71 Ga, 91 Zr, 115 In, 135/137 Ba, 139 La, 27 Al) in representative MOFs by SSNMR at a high magnetic field of 21.1 T, addressing several important issues: (1) resolving chemically and crystallographically nonequivalent metal sites; (2) exploring the origin of disorder around metals; (3) refining local metal geometry; (4) probing the effects of activation and adsorption on the metal local environment; and (5) monitoring in situ phase changes in MOFs. Organic linkers can be characterized by 1 H, 13 C, and 17 O SSNMR. Although the framework structure can be determined by X-ray diffraction, hydrogen atoms cannot be accurately located, and thus the local structure about hydrogen is poorly characterized. Our work demonstrates that magic-angle spinning (MAS) experiments at very high magnetic field along with ultrafast spinning rates and isotope dilution enables one to obtain ultrahigh resolution 1 H MAS spectra of MOFs, yielding structural information truly complementary to that obtained from single-crystal XRD. Oxygen is a key constituent of many important MOFs but 17 O SSNMR work on MOFs is scarce due to the low natural abundance of 17 O. 17 O enriched MOFs can now be prepared in an efficient and economically feasible manner using solvothermal approaches involving labeled H 2 17 O water; the resulting 17 O SSNMR spectra provide distinct spectral signatures of various key oxygen species in representative MOFs. MOFs are suitable for the capture of the greenhouse gas CO 2 and the storage of energy carrier gases such as H 2 and CH 4 . A better understanding of gas adsorption obtained using 13 C, 2 H, and 17 O SSNMR will enable researchers to improve performance and realize practical applications for MOFs as gas adsorbents and carriers. The combination of SSNMR with XRD allows us to determine the number of adsorption sites in the framework, identify the location of binding sites, gain physical insight into the nature and strength of host–guest interactions, and understand guest dynamics.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.341
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations155
Published2017
Admission routes2
Has abstractyes

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