Linnett double quartet theory in organic chemistry: structure, reactivity and resonance

1980 ◽  
Vol 58 (8) ◽  
pp. 780-785 ◽  
Author(s):  
Richard Francis Langler ◽  
June Ellen Trenholm ◽  
John Spencer Wasson
Keyword(s):  
Organic Chemistry ◽  
Chemical Reactivity ◽  
Orbital Theory ◽  
Resonance Theory

A reexamination of Linnett's nonpairing spatial orbital theory, as it is qualitatively employed, has led to a number of conclusions. Firstly, in our view the theory has been inconsistently applied to the prediction of molecular geometries and may be ambiguous in dealing with saturated systems. Secondly, it does not provide a guide to chemical reactivity, in contrast to claims made for it. Finally, NPSO theory does provide a useful extension of resonance theory as it is qualitatively employed in organic chemistry, contrary to an existing claim that NPSO theory makes resonance theory obsolete.

The correspondence between the resonance and molecular orbital theories

1952 ◽  
Vol 214 (1119) ◽  
pp. 482-493 ◽  
Keyword(s):  
Organic Chemistry ◽  
Molecular Orbital ◽  
Valence Bond ◽  
Quantum Mechanical ◽  
Orbital Method ◽  
Molecular Orbital Method ◽  
Resonance Theory ◽  
Bond Method ◽  
Additional Assumptions

The resonance theory of organic chemistry is critically examined from a theoretical view­point. It is stressed that this theory is not rigorously founded on the quantum-mechanical valence bond method, but involves additional assumptions which cannot be defended. The practical success of the resonance theory must therefore be explained in some other way. It is here shown that a remarkable correspondence exists between the resonance theory and the molecular orbital method; and it is suggested that the resonance theory owes its success more to this correspondence than to the validity of its own premises.

scholarly journals Ten Essential Delocalization Learning Outcomes: How Well Are They Achieved?

2020 ◽  
Author(s):  
Myriam S. Carle ◽  
Romeo Junior El Issa ◽  
Nicolas Pilote ◽  
Alison Flynn
Keyword(s):  
Organic Chemistry ◽  
Physical Properties ◽  
Learning Outcomes ◽  
Causal Reasoning ◽  
Scientific Reasoning ◽  
Chemical Reactivity ◽  
Student Reasoning ◽  
Resonance Structures ◽  
Research And Practice ◽  
Resonance Hybrid

<p>Delocalization (resonance) is a concept in organic chemistry that influences the chemical reactivity, structure, and physical properties of molecules. However, the concept has proven challenging for students and the related learning outcomes had previously been only vaguely defined. We recently defined ten essential learning outcomes about delocalization that a student should be able to demonstrate by the end of a two-course organic chemistry sequence. The goal of the present study was to investigate to what extent the ten LOs were achieved by students, as well as the connections between the LOs. We analyzed three exam questions related to seven of the ten LOs for the degree of achievement, common errors, and scientific reasoning. We found that students sometimes struggled to identify when delocalization could occur, that some of the LOs built on one another, and that students were more successful in drawing resonance structures when explicitly asked, but less successful when the requirement was implicit or embedded within a mechanism. Our analysis of student reasoning showed that the dominant modes of reasoning were aligned with the related expectations and explanations in the course. When asked to justify the contribution of resonance structures to the resonance hybrid, most answers used a descriptive mode of reasoning; when asked to explain why a given proton was more acidic than another, most answers contained relational and linear causal reasoning. Implications for research and practice are discussed.</p>

scholarly journals About usefulness of developing anyone of quantum chemical programs for pupils of 9-11 special classes

2019 ◽  
Vol 5 (3) ◽  
pp. 439-451
Author(s):  
A. Sikachina
Keyword(s):  
Organic Chemistry ◽  
Numerical Experiment ◽  
Quantum Chemical ◽  
Core Level ◽  
Organic Reactions ◽  
Resonance Theory ◽  
The Core ◽  
Educational Literature ◽  
Special Classes

Despite the fact that quantum chemical programs (and even superficial ones) are considered only at chemical faculties of universities, even students of science-oriented school classes could help to visualize many of the most important properties of molecules, complementing the theoretical and practical laboratory fundamentals of chemistry, and as a result inspire more interest in such. The author of the famous textbook of chemistry, O. Gabrielyan in the author's course of chemistry in the 2003 edition introduced aspects of higher organic chemistry (mechanisms of organic reactions, the concept of electrophilicity and nucleophilicity, partial δ− and δ+ charges, resonance theory). The author of this article examines possible types of convenient explanations that can be used when introducing into practice the core level of educational literature, following the example of O. Gabrielyan, small workshops on numerical experiment.

PREDICTION OF THE CONDITIONS OF CHEMICAL ORGANIC REACTIONS USING THE EDGE ATTENTION GRAPH CONVOLUTION NETWORK

2020 ◽  
Author(s):  
Владимир Борисович Москалев
Keyword(s):  
Organic Chemistry ◽  
Chemical Reactivity ◽  
Point Of View ◽  
Organic Reactions ◽  
Metabolic Processes ◽  
Reaction Conditions

Моделирование структуры химической реактивности с точки зрения структуры участвующих веществ имеет важные последствия во всех областях химии и биохимии, от синтеза до понимания метаболических процессов. Для предсказания условий реакций органической химии на основе графов была взята модель графовой сверточной нейронной сети с механизмом внимания на ребрах Edge Attention Graph Convolution Network. Modeling the structure of chemical reactivity from the point of view of the structure of the substances involved has important consequences in all areas of chemistry and biochemistry, from synthesis to understanding metabolic processes. To predict the reaction conditions of organic chemistry based on graphs, we used the model Edge Attention Graph Convolution Network.

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