Course - Statistical Thermodynamics in Chemistry and Biology - TKJ4215
Statistical Thermodynamics in Chemistry and Biology
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About the course
Course content
The course introduces the basis of statistical thermodynamics with examples from chemistry and biology: entropy and Boltzmann distribution law, lattice models for liquids and mixtures, free energies, partition functions, chemical equilibrium, kinetics and transport processes, electrochemistry, intermolecular forces, adsorption, phase transitions, catalysis, and polymers.
Learning outcome
After the course, the student is expected to be able to:
- Explain the basic concepts and principles in statistical thermodynamics.
- Use lattice models to study basic phenomena in chemistry and nanoscience.
- Construct new models based on the basic principles in statistical thermodynamics.
Skills:
- Use Python programming to solve exercise
Learning methods and activities
Lectures and exercises. Obligatory programming exercises in Python.
Expected work load in the course is 200-225 hours.
Compulsory assignments
- Exercises
Further on evaluation
If there is a re-sit examination, only the written exam is repeated.
If there is a re-sit examination, the examination form may be changed from written to oral.
Recommended previous knowledge
Basic knowledge in physics (in particular basic quantum mechanics and thermal physics/thermodynamics), chemistry, mathematics, informatics (Python programming) and statistics.
Course materials
K. A. Dill & S. Bromberg, Molecular Driving Forces: Statistical Thermodynamics in Chemistry and Biology, Garland Science, 2nd ed. 2011.
Subject areas
- Materials Science and Engineering
- Molecular Biophysics
- Thermodynamics
- Physical Chemistry
- Chemistry
- Nanotechnology