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Record W2792886567 · doi:10.1002/poc.3805

<sup>1</sup>H NMR‐based method for the determination of complexation equilibrium parameters and chemical shifts in a hydrogen‐bonded system with dynamic composition

2018· article· en· W2792886567 on OpenAlexafffund
Iamnica J. Linares Mendez, Jeffrey S. Pleizier, Hongbo Wang, James A. Wisner

Bibliographic record

VenueJournal of Physical Organic Chemistry · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsChemistryDimerPhotoisomerizationHydrogen bondEquilibrium constantChemical shiftCis–trans isomerismIsomerizationHydrogenComputational chemistryStereochemistryPhysical chemistryMoleculeOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract A photoswitchable self‐complementary hydrogen bond system with an azoheteroaromatic backbone is presented. The trans‐isomer (T) is the most stable form designed to dimerize through 6 hydrogen bond interactions. The dimerization constant (KT·T) and the dimer structure in the solid state have been obtained by conventional methods. When the cis‐isomer (C) is generated through photoisomerization, the dynamics of the complex structures in solution change. The less stable cis‐isomer can engage in dimerization, and a trans‐cis complex may form with remaining trans‐isomer (whose concentration increases as cis‐trans thermal reversion takes place). A mathematical approach to calculate the trans‐cis complexation constant (KT·C) along with an estimation of the cis‐isomer dimerization constant (KC·C) from phenyl analogues allows a description of the species distribution in solution to be generated.

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)0.000
metaresearch head score (Gemma)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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
GenreMethods

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
Published2018
Admission routes2
Has abstractyes

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