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Record W3131115518 · doi:10.26434/chemrxiv.7763213.v1

Electro-Inductive Effect: Using an Electrode as a Functional Group to Control the Reactivity of Molecules with Voltage

2019· article· en· W3131115518 on OpenAlexaff
Mu‐Hyun Baik, Joon Heo, Sang Woo Han

Bibliographic record

VenueChemRxiv · 2019
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMoleculeElectrodeReactivity (psychology)Substrate (aquarium)ChemistryMonolayerCombinatorial chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

A new general method of controlling the chemical reactivity of molecules is proposed. Instead of tuning the electronic properties of reactive molecules by employing functional groups that impose different inductive effects to a molecule, we propose to immobilize the parent molecule onto an electrode, for example by installing a thiol group to the molecule and forming a self-assembled monolayer on a gold surface. By applying a voltage the electronic property of the immobilized molecule can be tuned, as is commonly done by decorating the molecule with electron-donating and electron-withdrawing groups. As a proof of principle, it is shown that the base-catalyzed saponification of benzoic ester can be shut down completely by applying a negative voltage, while it can be accelerated by using a positive voltage. Furthermore it is shown that the Suzuki-Miyaura cross-coupling reaction can be affected by the voltage when the arylhalide substrate of the reaction is immobilized on a gold electrode.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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