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Aza-Michael Addition in Explicit Solvent: A Relative Energy Gradient-Interacting Quantum Atoms Study
Aël Cador1, Vincent Tognetti1, Laurent Joubert1
1Normandy Univ., COBRA UMR 6014 & FR 3038, Université de Rouen, INSA Rouen, CNRS, 1 rue Tesnière, 76821, Mont St, Aignan Cedex, France.
This study uses the REG-IQA computational method to analyze aza-Michael additions, revealing solvent effects on reaction rates. The findings establish a novel link between theoretical analysis and experimental data for predicting electrophilicity.
Area of Science:
- Organic Chemistry
- Computational Chemistry
- Physical Chemistry
Background:
- Aza-Michael additions are fundamental transformations in organic synthesis.
- Understanding solvent effects is crucial for controlling reaction outcomes and rates.
- Predicting electrophilicity is essential for designing efficient synthetic strategies.
Purpose of the Study:
- To investigate aza-Michael additions using theoretical and computational methods.
- To elucidate the role of dimethylsulfoxide as an explicit solvent in these reactions.
- To establish a connection between computational analysis and experimental measurements of electrophilicity.
Main Methods:
- Utilized the Relative Energy Gradient-Interacting Quantum Atoms (REG-IQA) method.
- Performed quantum topological energy decomposition analysis.
- Focused on the rate-limiting addition step of aza-Michael reactions.
Main Results:
- Detailed analysis of the rate-limiting step in aza-Michael additions with varying electrophiles.
- Quantified the influence of dimethylsulfoxide (DMSO) as an explicit solvent on reaction mechanisms.
- Established the first correlation between REG-IQA analysis and experimental data for predicting Mayr electrophilicity.
Conclusions:
- The REG-IQA method provides valuable insights into the mechanistic details of aza-Michael additions.
- Explicit solvent effects play a significant role in modulating reaction pathways and rates.
- This study bridges theoretical calculations with experimental observations, enhancing predictive capabilities in organic synthesis.
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