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Solid‐State<scp>NMR</scp>of the Light Main Group Metals

2015· other· en· W2922732793 on OpenAlexaff
Robert W. Schurko, Michael J. Jaroszewicz

Bibliographic record

VenueEncyclopedia of Inorganic and Bioinorganic Chemistry · 2015
Typeother
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNuclideSolid-state nuclear magnetic resonanceChemistryCrystallographyNuclear magnetic resonanceNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Abstract This review article discusses solid‐state nuclear magenetic resonance (SSNMR) spectroscopy of NMR‐active isotopes of the light main group metals, including lithium ( 6 Li and 7 Li), sodium ( 23 Na), aluminum ( 27 Al), potassium ( 39 K), magnesium ( 25 Mg), calcium ( 43 Ca), and beryllium ( 9 Be). All of these nuclides are quadrupolar, meaning that they have nuclear spins greater than 1/2. As a result, specialized techniques are needed to effectively acquire their spectra, which can then be analyzed to provide information on molecular‐level structure and dynamics. Work on SSNMR of 7 Li, 23 Na, and 27 Al, which are all receptive nuclides, has been essential in the development of a wide range of pulse sequences and techniques for acquiring good quality, high‐resolution SSNMR spectra. Applications of 7 Li, 23 Na, and 27 Al SSNMR are widespread, owing to the ubiquity of these elements in both natural and manmade materials. 25 Mg, 39 K, 43 Ca, and 9 Be are relatively unreceptive nuclides by comparison (the former three are low‐γ nuclides); however, much work has gone into both development of spectral acquisition methods and application of these methods to study a wide range of systems. This review article, which is directed at non‐NMR experts and/or students, provides a basic background to SSNMR of quadrupolar nuclides, and then discusses key reviews and papers which are essential for understanding the scope and potential of SSNMR of metal nuclides.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.215
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.236
Teacher spread0.231 · 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
GenreOther

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

Citations8
Published2015
Admission routes1
Has abstractyes

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