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Record W4392199192 · doi:10.1021/acs.organomet.3c00470

Electrochemical Properties of a Pyridine-Tunable Trisferrocenylborane

2024· article· en· W4392199192 on OpenAlexafffund
Harvey Sharma, Mitchell J. Demchuk, Suman Debnath, Brady J. H. Austen, Marcus W. Drover

Bibliographic record

VenueOrganometallics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsWestern UniversityUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaCouncil of Ontario UniversitiesWestern UniversityUniversity of Windsor
KeywordsChemistryAcetonitrileDichloromethaneFerroceneCyclic voltammetryRedoxTetrahydrofuranElectrochemistryPyridineAqueous solutionBoraneAdductSubstituentBulk electrolysisMetalloceneNucleophileSolventInorganic chemistryPhotochemistryOrganic chemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Non-aqueous redox flow batteries (NARFBs) offer promising solutions for sustainable and long-term energy storage. Herein, trisferrocenylborane (BFc 3; Fc = ferrocene, Cp 2 Fe, and Cp = C 5 H 5 – ) is examined for its potential role as a tunable anolyte for use in NARFBs. BFc 3 contains three readily oxidized ferrocene units along with a mediating borane that hosts an empty p orbital, allowing tuned access to specific redox properties as a function of nucleophile choice. The electrochemical behavior (cyclic voltammetry, charging/discharging cycles, etc.) of BFc 3 are accordingly examined in the presence of a series of 4-R-substituted pyridines (R = NMe 2, NH 2, t Bu, Et, or I), with a range of nitrogen donor strengths. These adducts produce a linear correlation that relates E 1/2 to the para -substituent constant (σ p ), showing a tunability of 200–300 mV based on donor choice alone; solvent effects using tetrahydrofuran, acetonitrile, and dichloromethane are additionally discussed.

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 score0.026
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.232
Teacher spread0.215 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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