Analysis of Large Data Sets in a Physical Chemistry Laboratory NMR Experiment Using Python.

Area of Science:

• Physical Chemistry Education
• Spectroscopy Techniques
• Computational Chemistry

Background:

• Traditional undergraduate chemistry curricula often lack comprehensive training in large dataset analysis.
• Nuclear Magnetic Resonance (NMR) spectroscopy is a fundamental technique in chemistry, but its application in undergraduate labs can be limited by data processing complexity.
• There is a growing need for accessible, modern computational tools in chemistry education to prepare students for research and industry.

Purpose of the Study:

• To present an updated experimental protocol for low-field NMR spectroscopy suitable for undergraduate physical chemistry laboratories.
• To develop and implement a Python-based data processing and analysis workflow for this NMR experiment.
• To introduce students to essential data analysis methodologies relevant to science and engineering through an interactive JupyterLab environment.

Main Methods:

• Implementation of a Python-based data analysis protocol within JupyterLab interactive worksheets.
• Utilizing low-field Nuclear Magnetic Resonance (NMR) spectroscopy for data acquisition.
• Step-by-step interactive data handling and analysis guided by the developed protocol.

Main Results:

• Successful integration of a Python protocol for processing and analyzing low-field NMR data.
• Students gain hands-on experience with modern data analysis techniques using Python.
• The protocol enhances the educational value of NMR spectroscopy experiments by incorporating computational analysis.

Conclusions:

• The developed Python protocol effectively supports low-field NMR spectroscopy analysis in undergraduate physical chemistry labs.
• This approach bridges the gap in data analysis training within traditional chemistry education.
• The open-source nature of Python and JupyterLab promotes accessibility and adoption in educational settings.