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Record W2594699198 · doi:10.1021/acs.iecr.7b00192

Polyurea Structure Characterization by HR-MAS NMR Spectroscopy

2017· article· en· W2594699198 on OpenAlexafffund
Xubao Jiang, Xiaoli Zhu, Alexandre A. Arnold, Xiang Zheng Kong, Jérôme P. Claverie

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Montréal
FundersFonds de recherche du Québec – Nature et technologiesNational Natural Science Foundation of China
KeywordsBiuret testChemistryHeteronuclear single quantum coherence spectroscopyChemical shiftMagic angle spinningCharacterization (materials science)Nuclear magnetic resonance spectroscopyCarbon-13 NMRTransverse relaxation-optimized spectroscopySolid-state nuclear magnetic resonanceChemical structureFluorine-19 NMRAnalytical Chemistry (journal)Physical chemistryNuclear magnetic resonanceMaterials scienceOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

Owing to the presence of abundant interchain interactions such as hydrogen bonds, polyureas (PU) are only swellable or soluble in a limited number of highly protonic solvents, and the viscosity of the solutions obtained is very high, making their chemical structure characterization hard or even impossible by standard NMR. Accurate structure analysis is also hard by solid-state NMR due to low spectral resolution. The presence of a side reaction in their synthesis generating biuret cross-links is often invoked to explain their insolubility. Here we demonstrate that High Resolution Magic Angle Spinning (HR-MAS) NMR is an efficient tool for the chemical structure analysis even for cross-linked PU. With 1 H, 13 C, and 1 H– 15 N HSQC combined, a variety of linear and cross-linked PU is analyzed by HR-MAS NMR, and conclusive information on their chemical structure is obtained, which reveals for the first time that the biuret group is absent in all PU.

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)0.001
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 score0.010
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.323
Teacher spread0.274 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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