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Computational Modelling and Simulations for Designing of Corrosion Inhibitors

Bag om Computational Modelling and Simulations for Designing of Corrosion Inhibitors

Computational Modeling and Simulations for Designing of Corrosion Inhibitors: Fundamentals and Realistic Applications offers a collection of major advancements in the field of computational modeling for the design and testing of corrosion inhibition effectiveness of organic corrosion inhibitors. This guide presents the latest developments in molecular modeling of organic compounds using computational software, which has emerged as a powerful approach for theoretical determination of corrosion inhibition potentials of organic compounds. The book covers common techniques involved in theoretical studies of corrosion inhibition potentials, and mechanisms such as density functional theory, molecular dynamics, Monte Carlo simulations, artificial neural networks, and quantitative structure-activity relationship.

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  • Sprog:
  • Ukendt
  • ISBN:
  • 9780323951616
  • Indbinding:
  • Paperback
  • Sideantal:
  • 564
  • Udgivet:
  • 24. april 2023
  • Størrelse:
  • 236x38x191 mm.
  • Vægt:
  • 1112 g.
Leveringstid: Ukendt - mangler pt.

Beskrivelse af Computational Modelling and Simulations for Designing of Corrosion Inhibitors

Computational Modeling and Simulations for Designing of Corrosion Inhibitors: Fundamentals and Realistic Applications offers a collection of major advancements in the field of computational modeling for the design and testing of corrosion inhibition effectiveness of organic corrosion inhibitors. This guide presents the latest developments in molecular modeling of organic compounds using computational software, which has emerged as a powerful approach for theoretical determination of corrosion inhibition potentials of organic compounds. The book covers common techniques involved in theoretical studies of corrosion inhibition potentials, and mechanisms such as density functional theory, molecular dynamics, Monte Carlo simulations, artificial neural networks, and quantitative structure-activity relationship.

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