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Metal‐Organic Frameworks: <scp>NMR</scp> Studies of Quadrupolar Nuclei

2014· other· en· W1851939856 on OpenAlexafffund
Yining Huang, Jun Xu, Farhana Gul‐E‐Noor, Peng He

Bibliographic record

VenueEncyclopedia of Inorganic and Bioinorganic Chemistry · 2014
Typeother
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSpectroscopyNuclear magnetic resonance spectroscopyChemistryQuadrupoleSolid-state nuclear magnetic resonanceNuclear magnetic resonance crystallographyCrystallographyChemical physicsNuclear magnetic resonanceFluorine-19 NMRPhysicsAtomic physicsStereochemistry

Abstract

fetched live from OpenAlex

Abstract Metal‐organic frameworks (MOFs) are a novel type of porous materials with many current and potential applications. Solid‐state NMR (SSNMR) spectroscopy is an excellent technique for MOF characterization. It can provide nuclide‐specific information on structure and dynamics, which is complementary to that obtained from X‐ray diffraction. The NMR‐active isotopes of vast majority of metal centers including 27 Al, 71 Ga, 45 Sc, 67 Zn, 25 Mg, and 115 In as well as some framework elements such as 17 O are quadrupolar nuclei. Compared to spin ‐1/2 nuclei such as 13 C, SSNMR spectroscopy of quadrupolar nuclei has traditionally considered very challenging because of the quadrupolar interaction that makes spectral acquisition and interpretation difficult. This is particularly true for those so‐called unreceptive quadrupolar nuclei (i.e., the nuclei with low gyromagnetic ratios, low natural abundances, and large quadrupole moments). However, in recent years, significant progresses have been made in developing SSNMR spectroscopy of quadrupolar nuclei. This chapter begins with a brief introduction of NMR spectroscopy of quadrupolar nuclei; followed by discussion of several very recent examples to illustrate that SSNMR spectroscopy of quadrupolar nuclei can be used as an effective tool to characterize the nuclear environments in MOF‐based materials. It is hoped that this chapter will encourage more researchers to use SSNMR spectroscopy for investigation of MOF properties by interrogating quadrupolar nuclei.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.307
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations3
Published2014
Admission routes2
Has abstractyes

Explore more

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