What you'll learn?
Course Outcomes:
- Students Identify thermodynamics and its applications
- Students describe the fundamentals of thermodynamics
- Students able to do chemical calculations involving the thermodynamics and energy transformations
Description
Sub Course Outcomes:
- Define key thermodynamic terms, including system, surroundings, and state functions.
- Explain the Zeroth and First Laws of Thermodynamics and their applications in energy transfer.
- Calculate changes in internal energy, heat, and work in thermodynamic processes.
- Apply enthalpy and specific heat concepts to solve calorimetry problems
- Describe the Second Law of Thermodynamics and its implications for spontaneity and entropy
- Analyze the efficiency of heat engines using the Carnot cycle.
- Apply the Third Law of Thermodynamics to understand entropy at absolute zero.
- Calculate Gibbs free energy changes to determine reaction spontaneity. Interpret phase diagrams and equilibrium conditions for various systems.
- Apply Hess’s Law and bond enthalpy concepts to calculate energy changes in chemical reactions.
- Explain thermodynamic principles in real-world applications like engines, refrigerators, and biological systems.
- Analyze phase transitions and determine critical and triple points using phase diagrams.
- Solve complex thermodynamic problems using systematic methods and strategies.
- Identify and correct common misconceptions in thermodynamics through practice exercises.
Course content
Total:
10 lectures
Total Duration:
1 hours, 48 minutes, and 49 seconds
the instructor
Ayisha Safeeda
Ayisha Safeeda T K is a Consultant Scientist specializing in AI-driven drug discovery and molecular dynamics. Her expertise lies in applying machine learning algorithms to computational chemistry, significantly advancing drug discovery research. She is a dedicated educator, having taught a diverse range of chemistry modules, including quantum mechanics, computational chemistry, stereochemistry, and coordination compounds, to graduate and postgraduate students. Her current research focuses on developing AI-based solutions for drug discovery and the virtual screening of Ayurvedic formulations to combat epidemic and pandemic diseases. Ayisha Safeeda T K is a Consultant Scientist specializing
in AI-driven drug discovery and molecular dynamics. Her expertise lies in applying machine learning algorithms to computational chemistry, significantly advancing drug discovery research. She is a dedicated educator, having taught a diverse range of chemistry modules, including quantum mechanics, computational chemistry, stereochemistry, and coordination compounds, to graduate and postgraduate students. Her current research focuses on developing AI-based solutions for drug discovery and the virtual screening of Ayurvedic formulations to combat epidemic and pandemic diseases.