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Record W2582865645 · doi:10.1039/c7cp00199a

Sizable dynamics in small pores: CO<sub>2</sub>location and motion in the α-Mg formate metal–organic framework

2017· article· en· W2582865645 on OpenAlexafffund
Yuanjun Lu, Bryan E. G. Lucier, Yue Zhang, Pengju Ren, Anmin Zheng, Yining Huang

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFormateAdsorptionMetal-organic frameworkMolecular dynamicsChemical physicsChemistryAtom (system on chip)Solid-state nuclear magnetic resonanceLinkerMaterials scienceComputational chemistryPhysical chemistryOrganic chemistryNuclear magnetic resonanceCatalysisPhysics

Abstract

fetched live from OpenAlex

Metal–organic frameworks (MOFs) are promising materials for carbon dioxide (CO2) adsorption and storage; however, many details regarding CO2 dynamics and specific adsorption site locations within MOFs remain unknown, restricting the practical uses of MOFs for CO2 capture. The intriguing α-magnesium formate (α-Mg3(HCOO)6) MOF can adsorb CO2 and features a small pore size. Using an intertwined approach of 13C solid-state NMR (SSNMR) spectroscopy, 1H–13C cross-polarization SSNMR, and computational molecular dynamics (MD) simulations, new physical insights and a rich variety of information have been uncovered regarding CO2 adsorption in this MOF, including the surprising suggestion that CO2 motion is restricted at elevated temperatures. Guest CO2 molecules undergo a combined localized rotational wobbling and non-localized twofold jumping between adsorption sites. MD simulations and SSNMR experiments accurately locate the CO2 adsorption sites; the mechanism behind CO2 adsorption is the distant interaction between the hydrogen atom of the MOF formate linker and a guest CO2 oxygen atom, which are ca. 3.2 Å apart.

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)
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 score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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
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

Citations45
Published2017
Admission routes2
Has abstractyes

Explore more

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