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Record W3144339028 · doi:10.1021/acs.joc.1c00165

Unveiling KuQuinone Redox Species: An Electrochemical and Computational Cross Study

2021· article· en· W3144339028 on OpenAlexaff
Francesca Valentini, Federica Sabuzi, Valeria Conte, Victor N. Nemykin, Pierluca Galloni

Bibliographic record

VenueThe Journal of Organic Chemistry · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicBioactive Compounds and Antitumor Agents
Canadian institutionsUniversity of Manitoba
FundersUniversità degli Studi di Roma Tor Vergata
KeywordsChemistryRedoxElectrochemistryMoleculeHydrogen bondComputational chemistryElectrolyteSolventSupporting electrolytePhotochemistryCombinatorial chemistryInorganic chemistryOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

The study of the electrochemical properties of variegated quinones is a fascinating topic in chemistry. In fact, redox reactions occurring with quinoid scaffolds are essential for most of their applications in biological systems, in photoelectrochemical devices, and in many other fields. In this paper, a detailed investigation of KuQuinones' redox behavior is presented. The distinctiveness of such molecules is the presence in the structure of two condensed naphthoquinone units, which implies the possibility to undergo multiple one-electron reduction processes. Solvent, supporting electrolyte, and hydrogen bond donor species effects have been elucidated. Changing the experimental parameters provoked significant shift of the redox potential for each reduction process. In particular, additions of 2,2,2-trifluoroethanol as a hydrogen bond donor in solution as well as Lewis acid coordination were crucial to obtain important shifts of the redox potentials toward more favorable values. UV-vis-NIR spectroelectrochemical experiments and DFT calculations are also presented to clarify the nature of the reduced species in solution.

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)0.001
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 score0.014
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.075
GPT teacher head0.413
Teacher spread0.339 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

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