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Record W2807173802 · doi:10.1002/anie.201806097

Formazanate Complexes of Hypervalent Group 14 Elements as Precursors to Electronically Stabilized Radicals

2018· article· en· W2807173802 on OpenAlexafffund
Ryan R. Maar, Sara D. Catingan, Viktor N. Staroverov, Joe B. Gilroy

Bibliographic record

VenueAngewandte Chemie International Edition · 2018
Typearticle
Languageen
FieldChemistry
TopicSynthesis and characterization of novel inorganic/organometallic compounds
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHypervalent moleculeChemistryRadicalHeteroatomDelocalized electronCrystallographyCarbon groupMain group elementConjugated systemAtom (system on chip)Ligand (biochemistry)Density functional theoryElectron paramagnetic resonanceGroup (periodic table)PhotochemistryComputational chemistryRing (chemistry)Transition metalOrganic chemistry

Abstract

fetched live from OpenAlex

The stability of molecular radicals containing main-group elements usually hinges on the presence of bulky substituents that shield the reactive radical center. We describe a family of Group 14 formazanate complexes whose chemical reduction allows access to radicals that are stabilized instead by geometric and electron-delocalization effects, specifically by the square-pyramidal coordination geometry adopted by the Group 14 atom (Si, Ge, Sn) within the framework of the heteroatom-rich formazanate ligands. The reduction potentials of the Si, Ge, and Sn complexes as determined by cyclic voltammetry become more negative in that order. Examination of the solid-state structures of these complexes suggested that their electron-accepting ability decreases with increasing size of the Group 14 atom because a larger central atom increases the nonplanarity of the ligand-based conjugated π-electron system of the complex. The experimental findings were supported by density-functional calculations on the parent complexes and the corresponding radical anions.

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 categoriesInsufficient payload (model declined to judge)
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 score0.104
Threshold uncertainty score0.973

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0280.000

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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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

Citations35
Published2018
Admission routes2
Has abstractyes

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