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Related Concept Videos

Reaction Mechanisms03:06

Reaction Mechanisms

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Chemical reactions often occur in a stepwise fashion, involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs.
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The kinetic studies of SN2 reactions suggest an essential feature of its mechanism: it is a single-step process without intermediates. Here, both the nucleophile and the substrate participate in the rate-determining step.
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Kinetic studies of ionization of a tertiary halide in a protic solvent suggest that only the substrate participates in the rate-determining step (slow step). The nucleophile is involved only after the slowest step. The SN1 reaction takes place in a multiple-step mechanism. 
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Acids, Bases and Neutralization Reactions03:26

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An acid-base reaction is one in which a hydrogen ion, H+, is transferred from one chemical species to another. Such reactions are of central importance to numerous natural and technological processes, ranging from the chemical transformations within cells or lakes and oceans to the industrial-scale production of fertilizers, pharmaceuticals, and other substances essential to the society.
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E2 Reaction: Kinetics and Mechanism02:45

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SN2 substitutions and E2 eliminations of alkyl halides proceed via a concerted pathway. While the nucleophile attacks the alpha carbon in SN2 reactions, it functions as a strong base and abstracts a beta hydrogen in the E2 mechanism. The rate-limiting transition state in E2 elimination reactions is characterized by partially broken carbon–hydrogen and carbon–halogen bonds and a partially formed pi bond between the alpha and beta carbons. The beta hydrogen and halide are eliminated...
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Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

Jinao Zhang1, Yongmin Zhong2, Chengfan Gu2

  • 1School of Engineering, RMIT University, Bundoora, VIC, 3083, Australia. Jinao.zhang@rmit.edu.au.

Medical & Biological Engineering & Computing
|May 31, 2018
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Summary
This summary is machine-generated.

This study introduces a novel method for soft tissue deformation modeling using reaction-diffusion mechanics and neural dynamics. This approach enables real-time simulation and haptic feedback for surgical applications.

Keywords:
Cellular neural networksHaptic feedbackReaction-diffusion mechanicsReal-time performanceSoft tissue deformation

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Area of Science:

  • Computational mechanics
  • Biophysics
  • Neural networks

Background:

  • Soft tissue deformation modeling is crucial for surgical simulation, planning, and robotic surgery.
  • Existing methods may face challenges in achieving real-time performance and accurately capturing nonlinear behaviors.

Purpose of the Study:

  • To present a new methodology for modeling soft tissue deformation and dynamics.
  • To achieve real-time computational performance for interactive simulations with force feedback.

Main Methods:

  • Modeling soft tissue deformation using reaction-diffusion mechanics and neural dynamics.
  • Equating stored potential energy to transmembrane potential energy for reaction-diffusion propagation.
  • Formulating mechanics as cellular neural network dynamics for real-time computation.
  • Implementing the methodology with a haptic device for interactive deformation and force feedback.

Main Results:

  • The proposed methodology accurately models the nonlinear force-displacement relationship in soft tissues.
  • It demonstrates the capability to model homogeneous, anisotropic, and heterogeneous soft tissue properties.
  • Successful implementation with a haptic device provided interactive deformation and force feedback.

Conclusions:

  • The developed methodology offers a robust framework for realistic soft tissue deformation modeling.
  • It enables real-time, interactive simulations with haptic feedback, advancing surgical simulation and planning.
  • The approach effectively captures complex material properties of soft tissues.