Ab initio Study of Nucleophilic Aromatic Substitution of Polyfluorobenzene

1999 ◽  
pp. 528-529 ◽  
Author(s):  
Kiyoshi Tanaka ◽  
Makoto Deguchi ◽  
Satoru Iwata
Keyword(s):  
Ab Initio ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Ab Initio Study

scholarly journals Ab initio Study of Nucleophilic Aromatic Substitution of Polyfluorobenzene

1999 ◽  
Vol 23 (9) ◽  
pp. 528-529
Author(s):  
Kiyoshi Tanaka ◽  
Makoto Deguchi ◽  
Satoru Iwata
Keyword(s):  
Transition State ◽  
Ab Initio ◽  
Transition States ◽  
Nucleophilic Aromatic Substitution ◽  
Bond Breaking ◽  
Aromatic Substitution ◽  
Ab Initio Study ◽  
Rate Determining Step ◽  
Elimination Mechanism ◽  
The Stability

Calculations at ab initio levels of theory of the nucleophilic aromatic substitution of pentafluoronitrobenzene with amines demonstrate an addition–elimination mechanism (SNAr), with the rate-determining step at the second transition state involving C–F bond breaking, and support the ortho-selectivity of the reactions based on the stability of the second transition states.

How Does Nucleophilic Aromatic Substitution in Nitroarenes Really Proceed: General Mechanism

Synthesis ◽  
2017 ◽  
Vol 49 (15) ◽  
pp. 3247-3254 ◽  
Author(s):  
Mieczysław Mąkosza
Keyword(s):  
Ab Initio Calculations ◽  
Ab Initio ◽  
Nucleophilic Substitution ◽  
Experimental Studies ◽  
Secondary Reaction ◽  
Nucleophilic Aromatic Substitution ◽  
General Mechanism ◽  
Primary Process ◽  
Nucleophilic Substitution Of Hydrogen ◽  
Aromatic Substitution

On the basis of previously published experimental studies and ab initio calculations, a general corrected mechanism of nucleophilic aromatic substitution was formulated. It was shown that conventional nucleophilic substitution of halogens is a slow secondary reaction whereas nucleophilic substitution of hydrogen is the fast primary process. The general mechanism embraces both of these alternative and complementary reactions.

An ab initio study of electrophilic aromatic substitution

1999 ◽  
Vol 102 (1-6) ◽  
pp. 78-86 ◽  
Author(s):  
Daniel Zerong Wang ◽  
Andrew Streitwieser
Keyword(s):  
Ab Initio ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Ab Initio Study

An ab initio study of Se-reacted GaAs(0 0 1) surfaces

1998 ◽  
Vol 184-185 (1-2) ◽  
pp. 80-84 ◽  
Author(s):  
W Faschinger
Keyword(s):  
Ab Initio ◽  
Ab Initio Study

Carrier Transport in 2-D Materials: an Ab Initio Study

2019 ◽  
Author(s):  
Mathieu Luisier ◽  
Aron Szabo ◽  
Cedric Klinkert ◽  
Christian Stieger ◽  
Martin Rau ◽  
...  
Keyword(s):  
Ab Initio ◽  
Carrier Transport ◽  
Ab Initio Study

Ion solvation by dipolar aprotic solvents. An ab initio study

1987 ◽  
Vol 52 (1) ◽  
pp. 6-13 ◽  
Author(s):  
Petr Kyselka ◽  
Zdeněk Havlas ◽  
Ivo Sláma
Keyword(s):  
Ab Initio ◽  
Geometry Optimization ◽  
Molecular Structures ◽  
Dimethyl Sulphoxide ◽  
Ion Solvation ◽  
Interaction Energies ◽  
Ab Initio Study ◽  
Solvation Energies ◽  
Dipolar Aprotic Solvents ◽  
Complete Geometry Optimization

The paper deals with the solvation of Li+, Be2+, Na+, Mg2+, and Al3+ ions in dimethyl sulphoxide, dimethylformamide, acetonitrile, and water. The ab initio quantum chemical method was used to calculate the solvation energies, molecular structures, and charge distributions for the complexes water···ion, acetonitrile···ion, dimethyl sulphoxide···ion, and dimethylformamide···ion. The interaction energies were corrected for the superposition error. Complete geometry optimization was performed for the complex water···ion. Some generalizations are made on the basis of the results obtained.

scholarly journals Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

2021 ◽  
Author(s):  
Kjell Jorner ◽  
Tore Brinck ◽  
Per-Ola Norrby ◽  
David Buttar
Keyword(s):  
Machine Learning ◽  
Activation Energies ◽  
Accurate Prediction ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Mechanistic Modelling

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

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