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Record W2108530855 · doi:10.1139/v07-106

Characterization of metallurgical-grade aluminas and their precursors by <sup>27</sup>Al NMR and XRD

2007· article· en· W2108530855 on OpenAlexvenueno aff
Linus Perander, Zoran Ẑujović, Tania Groutso, Mark E. Smith, Luke A. O’Dell, James B. Metson

Bibliographic record

VenueCanadian Journal of Chemistry · 2007
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilMacDiarmid Institute for Advanced Materials and Nanotechnology
KeywordsMagic angle spinningChemistryCharacterization (materials science)Solid-state nuclear magnetic resonanceXANESExtended X-ray absorption fine structureNuclear magnetic resonance spectroscopyCrystallographyMetallurgyMaterials scienceNuclear magnetic resonanceSpectroscopyNanotechnologyStereochemistryAbsorption spectroscopy

Abstract

fetched live from OpenAlex

The structure of metallurgical- or smelter-grade aluminas (MGAs) is complex and poorly understood. Ultra-high-field solid-state 27 Al NMR results on industrial as well as on laboratory-prepared aluminas are discussed in relation to XRD results. It is demonstrated that high-field NMR can effectively be used to quantify the proportion of the thermodynamically stable alpha-alumina phase in these materials. The results demonstrate that 27 Al NMR is a vital adjunct to XRD methods to quantify the transition alumina phases that invariably dominate the MGAs. The nature of the disorder in these materials, determined by 27 Al NMR, is also compared with literature data, such as XANES and EXAFS studies, on typical laboratory-prepared materials. The utility of 27 Al NMR studies to provide new insight into the structural complexity of metallurgical aluminas is shown.Key words: solid-state magic-angle-spinning NMR, metallurgical-grade alumina, transition alumina, gamma-alumina, coordination number.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.536

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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