GenChem Modeling with Colab

This module introduces students to the use of Google Colab and Gemini for data analysis and visualization in a second-semester general chemistry course. The activities are designed to familiarize students with Python syntax and scientific libraries, develop effective AI prompting skills for chemistry problem solving, and deepen understanding of key chemical concepts.

The module consists of five lessons, each designed for a 75-minute recitation session. The activities are best implemented in a synchronous guided-inquiry setting, with teaching assistants and/or learning assistants available to support student learning. The first lesson serves as an introductory activity that familiarizes students with the Colab-Gemini workflow and is intended for use early in the semester. The remaining four lessons are independent, self-contained activities that can be implemented in any order to align with the course curriculum.

Lesson/Module Contents

This module contains five lessons:

1. Intro to Google Colab, Python, and Gemini

   • Directory: 01_Intro-Gas-Laws
   • Description: This lesson introduces students to the use of Python, Google Colab, and Gemini for data analysis and visualization in the context of ideal gas laws.

2. Reaction Kinetics of Organic Reactions

   • Directory: `02_Reaction-Kinetics
   • Description: In this lesson, students use Python, Google Colab, and Gemini to analyze reaction kinetics data using integrated rate laws. Through data analysis and visualization, students determine reaction order, evaluate kinetic models, and deepen their understanding of chemical kinetics.

3. Reaction Kinetics of Drug Degradation

   • Directory: `03_Drug-Degradation
   • Description: In this lesson, students use Python, Google Colab, and Gemini to analyze drug degradation kinetics using the Arrhenius equation. Through data analysis and visualization, students determine activation energies, examine the effect of temperature and pH on reaction rates, and deepen their understanding of chemical kinetics and pharmaceutical stability.

4. Titration Curves of Common Household Acids

   • Directory: `04_Titration-Curves
   • Description: In this lesson, students use Python, Google Colab, and Gemini to analyze the titration curves of weak acids. Through data analysis and visualization, students compare titration curves of unknown and reference acids, identify the unknown acid, and deepen their understanding of acid-base equilibria, pKₐ values, and titration behavior.

5. Impact of Concentrations on Voltaic Cell potentials

   • Directory: `05_Voltaic-Cells
   • Description: In this lesson, students use Python, Google Colab, and Gemini to investigate the relationship between cell potential and concentration using the Nernst equation. Through data analysis and visualization, students examine how changes in concentration affect cell potential, calculate electrochemical work, and deepen their understanding of electrochemical cells and thermodynamics.