Short course #1: Basic Impedance Spectroscopy

Sunday, 1 October 2017: 09:00-16:30
Chesapeake 11 (Gaylord National Resort and Convention Center)
This course is intended for chemists, physicists, materials scientists, and engineers with an interest in applying electrochemical impedance techniques to study a broad variety of electrochemical processes. The attendee will develop a basic understanding of the technique, the sources of errors in impedance measurements, the manner in which experiments can be optimized to reduce these errors, and the use of graphical methods to interpret measurements in terms of meaningful physical properties. The course is based on M. E. Orazem and B. Tribollet, Electrochemical Impedance Spectroscopy, 2nd edition, John Wiley & Sons, Hoboken, New Jersey, 2017. The basic impedance course will cover the front matter (page xxxiii) and parts of Chapters 1, 4, 5, 7-10, 17, and 18. The advanced course will cover chapters 5, 9-14 and 17-22.

Topics to be covered
  • the motivation for using impedance spectroscopy advantages as compared to other transient techniques and the conditions under which its use is ideally suited,
  • complex numbers and electrical circuit parameters,
  • the basic concepts of how impedance is measured,
  • proper selection of experimental parameters,
  • graphical representation of impedance data, including methods to extract some physically meaningful parameters,
  • application of electrical circuit analogues, and
  • an introduction to interpretation based on proposed reaction mechanisms.

The concepts will be illustrated by applications to different systems including corrosion, fuel cells, batteries, and transport through membranes such as skin. A list of suggested references will be provided.

This course is the first in a two course sequence offered at alternating ECS meetings by Professor Orazem. The second course in the series, “Advanced Impedance Spectroscopy,” introduces model development based on proposed reaction mechanisms, statistical analysis of impedance data, and regression analysis.

Mark E Orazem
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