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Record W2308514777 · doi:10.1002/ejic.201501242

Multiple Modes of Motion: Realizing the Dynamics of CO Adsorbed in M‐MOF‐74 (M = Mg, Zn) by Using Solid‐State NMR Spectroscopy

2016· article· en· W2308514777 on OpenAlexaff
Bryan E. G. Lucier, Hendrick Chan, Yue Zhang, Yining Huang

Bibliographic record

VenueEuropean Journal of Inorganic Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsChemistryAdsorptionMetal-organic frameworkMoleculeChemical physicsSpectroscopyMetalSolid-state nuclear magnetic resonanceMolecular dynamicsPorous mediumSolid-statePorosityTransition metalPhysical chemistryCrystallographyAnalytical Chemistry (journal)Computational chemistryNuclear magnetic resonanceOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Metal–organic frameworks (MOFs) often exhibit high porosities and surface areas, making them ideal media for gas storage and carbon capture. MOF‐74 is an intriguing porous MOF featuring one‐dimensional honeycomb‐shaped channels and open metal sites, and is able to adsorb poisonous CO. Variable‐temperature (VT) 13 C solid‐state NMR (SSNMR) experiments focusing on 13 CO adsorbed within M‐MOF‐74 (M = Mg, Zn) are a sensitive probe of guest motion, revealing valuable details regarding the dynamics of adsorbed CO within the MOF channels. 13 C SSNMR experiments recorded at temperatures ranging from 153 to 373 K, along with accompanying simulations, unambiguously indicate that two types of dynamic CO motion are present in MOF‐74: a localized wobbling of CO on the open metal site, and a non‐localized hopping of CO molecules between adjacent open metal sites. The fine details of these motions, including the motional angles and rates, are revealed and discussed. The CO dynamics in MOF‐74 are then compared and contrasted with those of CO 2 , illustrating the similarities and differences in motion between the two types of guest molecules across the experimental temperature range.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations35
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Inorganic ChemistrySame topicMetal-Organic Frameworks: Synthesis and ApplicationsFrench-language works237,207