Experience-Based Learning Approach to Chemical Kinetics: Learning from the COVID-19 Pandemic

Autor/inn/en	Sucre-Rosales, Estefanía; Fernández-Terán, Ricardo; Carvajal, David; Echevarría, Lorenzo; Hernández, Florencio E.
Titel	Experience-Based Learning Approach to Chemical Kinetics: Learning from the COVID-19 Pandemic
Quelle	In: Journal of Chemical Education, 97 (2020) 9, S.2598-2605 (8 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Fernández-Terán, Ricardo) ORCID (Hernández, Florencio E.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0021-9584
Schlagwörter	COVID-19; Pandemics; Communicable Diseases; Microbiology; Chemistry; Kinetics; Scientific Concepts; Programming; Equations (Mathematics); Problem Solving; Models; Relevance (Education); College Science; Undergraduate Study; Interdisciplinary Approach + Suchen Sie Ihr Suchwort? Contagious disease; Contagious diseases; Communicable disease; Infektionskrankheit; Mikrobiologie; Chemie; Kinetik; Programmierung; Equations; Mathematics; Gleichungslehre; Problemlösen; Analogiemodell; Relevance; Relevanz; Grundstudium; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität
Abstract	Herein, we present an experience-based learning approach that uses the COVID-19 pandemics knowledge about virus spread and epidemics to establish an analogy between a simple epidemics model--the SIR model (susceptible--infected--removed), and a second-order autocatalytic reaction with subsequent catalyst deactivation. Our approach provides a simple and engaging way for students to learn chemical kinetics from a current situation (the pandemic) while presenting them with programming tools to numerically solve any system of differential equations that may result from more complex chemical kinetic schemes. We include the option to fit experimental data to extract meaningful rate constants. Following Kolb's cycle, we first establish the theoretical background of chemical kinetics and the SIR model and describe the analogy. Then, we propose the use of MATLAB and Python to numerically solve the system of differential equations associated with this model and plot the results showing the behavior of the system when changing the constants that describe the process, making the analogy with chemical kinetic constants. Finally, we fitted experimental data from the COVID-19 pandemic (up to early June 2020) to test the model and discuss the goodness of the fitting, the fitting parameters, and the utility of real kinetic data. (As Provided).
Anmerkungen	Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01