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Record W3003608952 · doi:10.1002/cctc.201901838

Probing Interactions of γ‐Alumina with Water via Multinuclear Solid‐State NMR Spectroscopy

2020· article· en· W3003608952 on OpenAlexaff
Li Shen, Yang Wang, Jia‐Huan Du, Kuizhi Chen, Zhiye Lin, Yujie Wen, Ivan Hung, Zhehong Gan, Luming Peng

Bibliographic record

VenueChemCatChem · 2020
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsMinistry of Education and Child Care
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Science Foundation
KeywordsNanorodNuclear magnetic resonance spectroscopySolid-state nuclear magnetic resonanceCatalysisMoleculeChemistrySpectroscopyNMR spectra databaseReactivity (psychology)Inorganic chemistryPhysical chemistryChemical engineeringMaterials scienceSpectral lineNanotechnologyNuclear magnetic resonanceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Interactions of γ‐alumina with water are important in controlling its structure and catalytic properties. We apply solid‐state multinuclear NMR spectroscopy to investigate these interactions by monitoring 1 H and 17 O spectra in real‐time. Surface‐selective detection is made possible by adsorbing 17 O‐enriched water on γ‐alumina nanorods. Structural evolution on the surface was selectively probed by 1 H/ 17 O double resonance NMR and 27 Al NMR at ultrahigh 35.2 T magnetic field. Formation of hydroxyl species on the surface of nanorods is rapid upon the exposure of water, which involves low coordinated aluminum ions with doubly bridging and isolated hydroxyl species being generated first. Fast exchange occurs between oxygen atoms in the water molecules and bare surface sites, indicating high reactivity of these oxygen species. These results provide new insights into the structure and dynamics on the surface of γ‐alumina and the methods applied here can be extended to study the interaction of other oxides with water.

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 score0.013
Threshold uncertainty score0.554

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)0.0010.000

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 head0.014
GPT teacher head0.270
Teacher spread0.257 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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