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Double Resonance NMR Echo Spectroscopy of Aluminosilicates

2000· article· en· W2038607072 on OpenAlexaff
Yong Ba, C. I. Ratcliffe, John A. Ripmeester

Bibliographic record

VenueAdvanced Materials · 2000
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsAluminosilicateNanoclustersMaterials scienceCharacterization (materials science)Nuclear magnetic resonance spectroscopySpectroscopyFluorine-19 NMRResonance (particle physics)SiliconNuclear magnetic resonanceNanotechnologyOrganic chemistryCatalysisChemistryOptoelectronicsAtomic physics

Abstract

fetched live from OpenAlex

NMR spectroscopy is a powerful method for the characterization of materials. Here the development of double resonance NMR experiments is summarized and their application to materials science highlighted using the synthesis of silicon nanoclusters within aluminosilicate molecular sieves (see Figure) as an example. Not only can the structure of the product be elucidated but also that of the reaction intermediates.

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 score0.094
Threshold uncertainty score0.992

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)0.0090.000

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 head0.011
GPT teacher head0.286
Teacher spread0.276 · 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
Published2000
Admission routes1
Has abstractyes

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