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Record W1985487540 · doi:10.1021/ed500341p

Template Effect and Ligand Substitution Methods for the Synthesis of Iron Catalysts: A Two-Part Experiment for Inorganic Chemistry

2014· article· en· W1985487540 on OpenAlexafffund
Peter E. Sues, Kuihua Cai, Douglas F. McIntosh, Robert H. Morris

Bibliographic record

VenueJournal of Chemical Education · 2014
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsChemistryCatalysisGreen chemistryLigand (biochemistry)Platinum groupCombinatorial chemistryOrganic chemistryNanotechnologyPlatinumReaction mechanismMaterials science

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Asymmetric transfer hydrogenation is an important transformation for the production of fine chemicals. Traditionally, platinum group metals are used to catalyze this reaction, but recent pressure for greener practices has driven the development of base-metal catalysts. Due to the growing interest in this area of research, the underlying concepts for this type of chemistry are suitable for an undergraduate laboratory. A two-part experiment was adapted from original research and tested for use in an upper-division undergraduate inorganic chemistry laboratory. The first part of the experiment explores the template synthesis of a trans -bis-acetonitrile iron complex with a tetradentate ligand. The product from the first laboratory period is used as the starting material in the second laboratory period that focuses on the synthesis of a green catalyst mimic. Students learn about multi-step synthesis, specifically the synthesis of a catalyst for green chemistry, and key inorganic chemistry concepts to help reinforce topics introduced during lectures.

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)0.002
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 score0.065
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0000.000

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 head0.009
GPT teacher head0.323
Teacher spread0.314 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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