MétaCan
Menu
Back to cohort
Record W3094792527 · doi:10.1021/acs.jpca.0c08198

Solid-State <sup>17</sup>O NMR Studies of Sulfonate Jump Dynamics in Crystalline Sulfonic Acids: Insights into the Hydrogen Bonding Effect

2020· article· en· W3094792527 on OpenAlexafffund
Yizhe Dai, Gang Wu

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2020
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSulfonic acidHydrogen bondChemistrySulfonateCrystal structureNMR spectra databaseLattice energySolid-state nuclear magnetic resonanceCrystallographyActivation energyProton NMRPhysical chemistrySpectral lineNuclear magnetic resonanceStereochemistryOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

We report variable-temperature (VT) 17O solid-state nuclear magnetic resonance (NMR) spectra for three crystalline sulfonic acids: l-cysteic acid monohydrate (CA), 3-pyridinesulfonic acid (PSA), and p-toluenesulfonic acid monohydrate (TSA). We were able to analyze the experimental VT 17O NMR spectra to obtain the activation barriers for SO3– jumps in these systems. Using the density functional-based tight-binding (DFTB) method, we performed potential energy surface scans for SO3– jumps in the crystal lattice of CA, PSA, and TSA, as well as for three related crystalline sulfonic acids (taurine, homotaurine, and 4-aminobutane-1-sulfonic acid) for which relevant 17O solid-state NMR data are available in the literature. The calculated activation barriers are in reasonable agreement with the experimental values. On the basis of the DFTB results, we hypothesized that activation barriers for SO3– jumps in the crystal lattice depend largely on the hydrogen bonding energy difference between the ground state and the transition state.

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 score0.045
Threshold uncertainty score0.611

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 science0.0010.000
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 head0.014
GPT teacher head0.308
Teacher spread0.294 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry ASame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207