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Record W2018431962 · doi:10.1063/1.1514573

Multiple-quantum nuclear magnetic resonance spin dynamics in disordered rigid chains and rings

2002· article· en· W2018431962 on OpenAlexafffund
S. I. Doronin, É. B. Fel’dman, Serge Lacelle

Bibliographic record

VenueThe Journal of Chemical Physics · 2002
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaRussian Academy of SciencesRussian Foundation for Basic Research
KeywordsHamiltonian (control theory)SpinsCoupling constantChemistryPhysicsDipoleMagnetic dipole–dipole interactionQuantumNuclear magnetic resonance spectroscopySpin (aerodynamics)Molecular physicsCondensed matter physicsNuclear magnetic resonanceQuantum mechanics

Abstract

fetched live from OpenAlex

Multiple-quantum (MQ) nuclear magnetic resonance (NMR) spin dynamics are investigated in rigid linear chains and rings with nearest neighbor dipole–dipole interactions with different coupling constants due to spatial disorder. It is shown that MQ NMR spectra, for such one-dimensional systems initially at thermal equilibrium followed by evolution under a 2-quantum/2-spin average dipolar Hamiltonian, only consist of 0- and 2-quantum coherences. A new constant of motion for the systems under consideration is found and used in the numerical analysis of MQ NMR spin dynamics to factorize the Hamiltonian into distinct blocks corresponding to different eigenvalues of the constant of motion. Only one of these blocks of dimension N×N (where N is the number of spins) completely determines the MQ NMR spin dynamics. Supercomputer calculations of MQ NMR spin dynamics in rigid linear chains containing up to 1000 spins are presented. The possibility to obtain structural information from the time evolution of MQ coherences is also discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.239
Teacher spread0.228 · 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

Citations11
Published2002
Admission routes2
Has abstractyes

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