Teaching chemical engineering using Jupyter notebook: Problem generators and lecturing tools

Domínguez, Juan C.; Alonso, M. Virginia; González, Emilio J.; Guijarro, María; Miranda, Rubén; Oliet, Mercedes; Rigual, Victoria; Toledo, José M.; Villar-Chavero, M. Mar; Yustos, Pedro

doi:10.1016/j.ece.2021.06.004

Cited by 15 publications

(16 citation statements)

References 13 publications

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“…Python has been among the most popular programming languages in the past ten years, is one of the most commonly used among chemical engineers, and has been used in many classes within chemistry and chemical engineering to teach programming. − Python is therefore an excellent choice as the base language for our application. Specifically, Porchlight is developed using Python 3.9, NumPy 1.21.5, Pandas 1.4.2, Matplotlib 3.5.1, and Tkinter as standard libraries.…”

Section: Python Applicationmentioning

confidence: 99%

Porchlight: An Accessible and Interactive Aid in Preprocessing of Spectral Data

Konkol

Tsilomelekis

2023

J. Chem. Educ.

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Preprocessing is a critical step in the analysis pipeline of spectroscopic data. However, students are rarely introduced to preprocessing when learning spectral techniques in laboratory courses which in turn may affect and delay their progress in the field. Despite its undoubtable importance, students will be mainly performing spectroscopic analysis in the context of a research project where preprocessing is encountered as part of a routine or “recipe” to follow. In this work, a Python-based application has been developed that allows facile application of common spectral preprocessing techniques with instantaneous results to support student learning. The developed application, i.e. Porchlight, and supplied Jupyter notebooks can substitute costly commercial software and make spectroscopic analysis widely available to students, trainees, and users in general.

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Section: Python Applicationmentioning

confidence: 99%

Porchlight: An Accessible and Interactive Aid in Preprocessing of Spectral Data

Konkol

Tsilomelekis

2023

J. Chem. Educ.

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“…Jupyter Notebook is a multifaceted interactive development environment which uses a web browser to run the IDE. Jupyter Notebook supports over 40 coding languages, has a modular and intuitive interface, and is incredibly flexible . Similarly to Python, Jupyter has a large community with easy access to online tutorials and help.…”

Section: Structure and Implementationmentioning

confidence: 99%

“…Jupyter Notebook supports over 40 coding languages, has a modular and intuitive interface, and is incredibly flexible. 26 Similarly to Python, Jupyter has a large community with easy access to online tutorials and help. Jupyter uses expandable text blocks known as "cells" which can be used to write code or notes in a piecewise fashion.…”

Section: Overviewmentioning

confidence: 99%

Foregrounding the Code: Computational Chemistry Instructional Activities Using a Highly Readable Fluid Simulation Code

et al. 2023

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Computational chemistry instructional activities are often based around students running chemical simulations via a graphical user interface (GUI). GUI-based activities offer many advantages, as they enable students to run chemical simulations with a few mouse clicks. Although these activities are excellent for introducing students to the capabilities of chemical simulations, the disadvantage is that the students' experience is not representative of how professional computational chemists work. Just as it is important that students in an organic chemistry instructional lab gain hands-on experience with equipment commonly used by professional organic chemists, students of computational chemistry must gain hands-on experience with coding, as professional computational chemists do not rely on GUIs; we write code. Motivated by the need for instructional activities that provide hands-on experience with computer code, a pair of activities were created around a free lightweight (runs on standard laptops) open-source Lennard-Jones (LJ) fluid simulation code written in Python, a programming language that prioritizes readability. The first activity, aimed at undergraduate physical chemistry lab courses, involves students writing Python code in a Jupyter Notebook that is used to run LJ simulations and fit a van der Waals gas model to data produced by the LJ fluid simulations. The second is a jigsaw activity, aimed at advanced undergraduate or graduate students, where students are assigned different sections of the LJ fluid simulation code, and must demonstrate the functionality of their section to the class by both giving an oral presentation and sharing a Jupyter Notebook demonstration of their own design.

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“…Jupyter notebook supports over 40 coding languages, has a modular and intuitive interface, and is incredibly flexible. 20 Similarly to Python, Jupyter has a large community with easy access to online tu-write code or notes in a piecewise fashion. Each cell can be executed individually to test code blocks independently, or cells can be run all at once.…”

Section: Overviewmentioning

confidence: 99%

Foregrounding the code: Computational chemistry instructional activities using a highly readable fluid simulation code

Grazioli

Ingwerson

Santiago

et al. 2022

Preprint

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Most computational chemistry instructional activities are based around students running chemical simulations via a graphical user interface (GUI). GUI-based activities offer many advantages, as they enable students to run chemical simulations with a few mouse clicks. Although these activities are excellent for introducing students to the capabilities of chemical simulations, the disadvantage is that the students’ experience is not representative of how professional computational chemists work. Just as it is important that students in an organic chemistry instructional lab gain hands-on experience with equipment commonly used by professional organic chemists, students of computational chemistry must gain hands-on experience with coding, as professional computational chemists do not rely on GUIs; we write code. Motivated by the need for instructional activities that provide hands-on experience with computer code, a pair of activities were created around a free lightweight (runs on standard laptops) open-source Lennard-Jones (LJ) fluid simulation code written in Python, a programming language that prioritizes readability. The first activity, aimed at undergraduate physical chemistry lab courses, involves students writing Python code in a Jupyter Notebook that is used to run LJ simulations and fit a Van der Waals gas model to data produced by the LJ fluid simulations. The second is a jigsaw activity, aimed at advanced undergraduate or graduate students, where students are assigned different sections of the LJ fluid simulation code, and must demonstrate the functionality of their section to the class by both giving an oral presentation and sharing a Jupyter Notebook demonstration of their own design.

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Teaching chemical engineering using Jupyter notebook: Problem generators and lecturing tools

Cited by 15 publications

References 13 publications

Porchlight: An Accessible and Interactive Aid in Preprocessing of Spectral Data

Porchlight: An Accessible and Interactive Aid in Preprocessing of Spectral Data

Foregrounding the Code: Computational Chemistry Instructional Activities Using a Highly Readable Fluid Simulation Code

Foregrounding the code: Computational chemistry instructional activities using a highly readable fluid simulation code

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