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

Title:
Analysis of Large Data Sets in a Physical Chemistry Laboratory NMR Experiment Using Python.
Authors:
Zhang Z; Department of Chemistry, Texas A&M University, 3255 TAMU, College Station, Texas 77843, United States., Gautam A; Department of Chemistry, Texas A&M University, 3255 TAMU, College Station, Texas 77843, United States., Lim SM; Department of Chemistry, Texas A&M University, 3255 TAMU, College Station, Texas 77843, United States., Hilty C; Department of Chemistry, Texas A&M University, 3255 TAMU, College Station, Texas 77843, United States.
Source:
Journal of chemical education [J Chem Educ] 2023 Sep 19; Vol. 100 (10), pp. 4109-4113. Date of Electronic Publication: 2023 Sep 19 (Print Publication: 2023).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Chemical Society. Division Of Chemical Education Country of Publication: United States NLM ID: 2985122R Publication Model: eCollection Cited Medium: Print ISSN: 0021-9584 (Print) Linking ISSN: 00219584 NLM ISO Abbreviation: J Chem Educ Subsets: PubMed not MEDLINE
Imprint Name(s):
Publication: Tucson Az : American Chemical Society. Division Of Chemical Education
Original Publication: Easton, Pa., Division of Chemical Education, American Chemical Society.
Entry Date(s):
Date Created: 20240215 Latest Revision: 20240215
Update Code:
20250114
PubMed Central ID:
PMC10862468
DOI:
10.1021/acs.jchemed.3c00586
PMID:
38357475
Database:
MEDLINE

Weitere Informationen

We describe an update to an experiment demonstrating low-field NMR spectroscopy in the undergraduate physical chemistry laboratory. A Python-based data processing and analysis protocol is developed for this experiment. The Python language is used in fillable worksheets in the notebook software JupyterLab, providing an interactive means for students to work with the measured data step by step. The protocol teaches methods for the analysis of large data sets in science or engineering, a topic that is absent from traditional chemistry curricula. Python is among the most widely used modern tools for data analysis. In addition, its open-source nature reduces the barriers for adoption in an educational laboratory.
(© 2023 American Chemical Society and Division of Chemical Education, Inc.)

The authors declare no competing financial interest.