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Record W2136376960 · doi:10.1039/c000353k

Natural abundance high field 43Ca solid state NMR in cement science

2010· article· en· W2136376960 on OpenAlexaff
Igor Moudrakovski, Rouhollah Alizadeh, J.J. Beaudoin

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsCementAbundance (ecology)Field (mathematics)Solid-state nuclear magnetic resonanceMaterials scienceEnvironmental scienceChemistryPhysicsNuclear magnetic resonanceComposite materialEcologyBiologyMathematics

Abstract

fetched live from OpenAlex

This work is a systematic attempt to determine the possibilities and the limitations of the (43)Ca high field solid state NMR in the study of cement-based materials. The low natural abundance (0.135%) and small gyromagnetic ratio of (43)Ca present a serious challenge even in a high magnetic field. The NMR spectra of a number of cement compounds of known structure and composition are examined. The spectra of several phases important in cement science, e.g., anhydrous beta di-calcium silicate (beta-C(2)S) and tri-calcium (C(3)S) silicate were obtained for the first time and the relation of spectroscopic and structural parameters is discussed. The method was also applied to the hydrated C(3)S and synthetic calcium silicate hydrates (C-S-H) of different composition in order to understand the state of calcium and transformations in the structure during hydrolysis. The spectra of hydrated C(3)S reveals a calcium environment similar to that of the C-S-H samples and 11 A Tobermorite. These observations support the validity of using layered crystalline C-S-H systems as structural models for the C-S-H that forms in the hydration of Portland cement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.288
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2010
Admission routes1
Has abstractyes

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