Team-Based Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions
| العنوان: | Team-Based Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions |
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| المؤلفون: | Arielle Rothman, Siavash Zamirpour, Tim Menke, Teresa Tamayo-Mendoza, Alán Aspuru-Guzik, Sukin Sim, Jonathan Romero, Shreya Menon, Florian Häse, Santiago Vargas |
| المصدر: | Journal of Chemical Education. 97:689-694 |
| بيانات النشر: | American Chemical Society (ACS), 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Cooperative learning, business.industry, Software development, General Chemistry, Python (programming language), Automation, Education, Computational science, Visualization, Team-based learning, Software, Data visualization, business, computer, computer.programming_language |
| الوصف: | The increasing integration of software and automation in modern chemical laboratories prompts special emphasis on two important skills in the chemistry classroom. First, students need to learn the technical skills involved in modern scientific computing and automation. Second, applying these techniques in practice requires effective collaboration in teams. This work aims at developing a teaching module to help students gain both skills. In particular, we describe a modular and collaborative approach for introducing undergraduate students to scientific computing in the context of automated and autonomous chemical laboratories. Using online collaboration tools, students work in parallel teams to develop central components of an automated computer vision system that monitors color changes in ongoing chemical reactions. These components include three different aspects: image capture, communication, and data visualization. The image capture team collects and stores the images of the chemical reaction, the communication team processes the images, and the visualization team develops the tools for analyzing the processed image data. Using this educational framework, students built an open-source Python tool called AutoVis that enables the automated tracking of color and intensity changes in a liquid. The software is tested by simulating chemical reactions with dilute solutions of food coloring in water. It is shown that the system reliably tracks color and intensity, providing feedback to the experimentalist and enabling further computational analysis. Over the course of the project, students gain proficiency in scientific computing using Python and collaborate on software development using GitHub. In this way, they learn the role of software in chemical laboratories of the future. |
| تدمد: | 1938-1328 0021-9584 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::1df27ec523b03d5c313b2cef05e9651b https://doi.org/10.1021/acs.jchemed.9b00603 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........1df27ec523b03d5c313b2cef05e9651b |
| قاعدة البيانات: | OpenAIRE |
Find this issue from the American Chemical Society | تدمد: | 19381328 00219584 |
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