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Record W2153641456 · doi:10.1002/chem.201300113

Resolving Multiple Non‐equivalent Metal Sites in Magnesium‐Containing Metal–Organic Frameworks by Natural Abundance <sup>25</sup>Mg Solid‐State NMR Spectroscopy

2013· article· en· W2153641456 on OpenAlexafffund
Jun Xu, Victor V. Terskikh, Yining Huang

Bibliographic record

VenueChemistry - A European Journal · 2013
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsNational Research Council CanadaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMagic angle spinningAbundance (ecology)Nuclear magnetic resonance spectroscopyMagnesiumSpectroscopySolid-stateMetalChemistrySolid-state nuclear magnetic resonanceComputer sciencePhysicsNuclear magnetic resonancePhysical chemistryStereochemistryOrganic chemistryQuantum mechanicsEcology

Abstract

fetched live from OpenAlex

In a spin: Directly differentiating multiple Mg sites in Mg-containing MOFs by 25Mg solid-state NMR spectroscopy is very challenging at natural abundance. By performing 25Mg two-dimensional triple-quantum magic-angle spinning solid-state NMR experiments at a magnetic field of 21.1 T at natural abundance, four non-equivalent Mg sites with very similar local environments in α-Mg3(HCOO)6 were unambiguously resolved (see figure). As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.251
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
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

Citations51
Published2013
Admission routes2
Has abstractyes

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