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Record W4296089572 · doi:10.26434/chemrxiv-2022-qh9rq

Solid-state NMR spectra of protons and quadrupolar nuclei at 28.2 T: resolv-ing signatures of surface sites with fast magic angle spinning

2022· preprint· en· W4296089572 on OpenAlexaff
Zachariah J. Berkson, Snædís Björgvinsdóttir, Alexander V. Yakimov, Domenico Gioffré, Maciej Korzyński, Alexander B. Barnes, Christophe Copéret

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSolid-state nuclear magnetic resonanceMagic angle spinningMagic angleNMR spectra databaseSpectral lineSpinningSolid-stateZeoliteCatalysisChemistryHigh resolutionNuclear magnetic resonance spectroscopyMagnetic fieldMaterials scienceNuclear magnetic resonanceAnalytical Chemistry (journal)Physical chemistryPhysicsOrganic chemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Advances in solid-state nuclear magnetic resonance (NMR) methods and hardware offer expanding op-portunities for analysis of materials, interfaces, and surfaces. Here, we demonstrate the application of a very high magnetic field strength of 28.2 T and fast magic-angle-spinning rates (MAS, >40 kHz) to sur-face species relevant to catalysis. Specifically, we present as case studies the 1D and 2D solid-state NMR spectra of important catalyst and support materials, ranging from a well-defined silica-supported organometallic catalyst to dehydroxylated γ-alumina and zeolite solid acids. The high field and fast-MAS measurement conditions substantially improve spectral resolution and narrow NMR signals, which is particularly beneficial for solid-state 1D and 2D NMR analysis of 1H and quadrupolar nuclei such as 27Al at surfaces.

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)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.293
Teacher spread0.277 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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