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Record W4322764615 · doi:10.1021/acs.jchemed.2c01258

The Synthesis of Ruthenocene─A Methodology Appropriate for the Inorganic Undergraduate Curriculum

2023· article· en· W4322764615 on OpenAlexaff
Shane Harrypersad, John P. Canal

Bibliographic record

VenueJournal of Chemical Education · 2023
Typearticle
Languageen
FieldChemistry
TopicInorganic and Organometallic Chemistry
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRuthenoceneFerroceneSublimation (psychology)ChemistryMetalloceneRutheniumMicroscale chemistryOrganometallic chemistryCombinatorial chemistryOrganic chemistryInorganic chemistryPhysical chemistryCatalysisMathematics educationElectrochemistry

Abstract

fetched live from OpenAlex

The discovery of ferrocene, Fe(C 5 H 5 ) 2, in the 1950s sparked the development of organometallic chemistry including metallocene compounds. Shortly after the isolation of ferrocene, the analogous ruthenium and osmium compounds were discovered. The preparation of ferrocene is common in undergraduate inorganic laboratory courses, but an appropriate undergraduate synthesis of ruthenocene, Ru(C 5 H 5 ) 2, is lacking. Herein we describe a convenient synthesis of Ru(C 5 H 5 ) 2 for the undergraduate teaching laboratory. This microscale reaction incorporates elements of green chemistry and introduces students to an air sensitive reaction with sublimation as a purification method. Students explore the characterization of ruthenocene through experimental and computational approaches.

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)0.005
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 score0.016
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0000.000
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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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