MétaCan
Menu
Back to cohort
Record W2587116785 · doi:10.1107/s2053229616019550

Structure determination of a partially ordered layered silicate material with an NMR crystallography approach

2017· article· en· W2587116785 on OpenAlexaff
Darren H. Brouwer, Sylvian Cadars, Kathryn Hotke, Jared Van Huizen, Nicholas Van Huizen

Bibliographic record

VenueActa Crystallographica Section C Structural Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsRedeemer University
Fundersnot available
KeywordsCrystallographySilicateMaterials scienceChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Structure determination of layered materials can present challenges for conventional diffraction methods due to the fact that such materials often lack full three-dimensional periodicity since adjacent layers may not stack in an orderly and regular fashion. In such cases, NMR crystallography strategies involving a combination of solid-state NMR spectroscopy, powder X-ray diffraction, and computational chemistry methods can often reveal structural details that cannot be acquired from diffraction alone. We present here the structure determination of a surfactant-templated layered silicate material that lacks full three-dimensional crystallinity using such an NMR crystallography approach. Through a combination of powder X-ray diffraction and advanced 29 Si solid-state NMR spectroscopy, it is revealed that the structure of the silicate layer of this layered silicate material templated with cetyltrimethylammonium surfactant cations is isostructural with the silicate layer of a previously reported material referred to as ilerite, octosilicate, or RUB-18. High-field 1 H NMR spectroscopy reveals differences between the materials in terms of the ordering of silanol groups on the surfaces of the layers, as well as the contents of the inter-layer space.

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 score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueActa Crystallographica Section C Structural ChemistrySame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207