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Record W2039686187 · doi:10.1039/b507602a

Hyperpolarized 129Xe NMR spectroscopic investigation of potentially porous shape-persistent macrocyclic materials

2005· article· en· W2039686187 on OpenAlexafffund
Kristopher J. Ooms, Katie Campbell, Rik R. Tykwinski, Roderick E. Wasylishen

Bibliographic record

VenueJournal of Materials Chemistry · 2005
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsXenonNuclear magnetic resonance spectroscopyNMR spectra databaseChemistryChemical shiftIsotopes of xenonSpectroscopyMolecular dynamicsSpectral lineCarbon-13 NMR satelliteChemical physicsCrystallographyNuclear magnetic resonanceFluorine-19 NMRPhysical chemistryComputational chemistryStereochemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The application of continuous-flow hyperpolarized 129Xe NMR spectroscopy to investigate the pores of three shape-persistent organic macrocycles is described. Peaks with xenon chemical shifts between 160 and 200 ppm are assigned to xenon atoms trapped in highly confined pores while NMR peaks with chemical shifts of 100 to 140 ppm are assigned to xenon present in channels that exist through the centre of the stacked macrocycles. With the aid of molecular dynamics simulations, connections between the 129Xe NMR spectra and the X-ray diffraction structure of one of the solvated macrocycles, 1, have been made. The similarities in the 129Xe NMR spectra of the three compounds containing hyperpolarized xenon suggest that all three hosts possess comparable pore structures. The 129Xe NMR data provide information about the porous nature of the two compounds for which X-ray crystallographic analysis was not possible. Data obtained from two-dimensional 129Xe NMR exchange spectroscopy experiments, variable-temperature 129Xe NMR and molecular dynamics simulations suggest a mechanism whereby the xenon gains access to the highly confined sites via the channels.

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 categoriesInsufficient 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 score0.017
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0070.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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

Citations14
Published2005
Admission routes2
Has abstractyes

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