Using a Modular Approach to Introduce Python Coding to Support Existing Course Learning Outcomes in a Lower Division Analytical Chemistry Course

Students determine the iron mass in dietary supplements using four colorimetric assays. These colorimetric assays were selective for Fe3+. It was complexed by thiocyanide, salicylate, gallic acid, and Fe(CN)6 4–, forming blood-red, red, violet, and blue complexes, respectively. The λmax of these complexes were 447, 480, 570, and 700 nm, respectively. These assays were scaled-down, where 1 mL of chromogenic reagent was mixed with 0.1 mL of sample. Salicylic acid, gallic acid, and K4[Fe(CN)6] were harmless and cheap chromogenic reagents that could be used instead of traditional chromogenic reagents such as KSCN and phenanthroline. Quantitative analysis was carried out using absorbance measured at λmax of each assay (standard method) and 96-well-plate digital images obtained with a desktop scanner (proposed method). The matrix effect was investigated by comparing the standard addition curves with the external calibration curves, where no matrix effect was found. Results obtained using the four assays were compared using hypothesis tests such as one-way ANOVA, post-hoc tests, and plots. Investigation of interactions between samples and assays was carried out using two-way ANOVA. Statistical tests were carried out using open-source and graphical user interface programs (JASP and R Commander). The student learning goals of this experiment include comparing multiple assays using hypothesis tests. Learn about the principles of green chemistry, method scale-down, and matrix effect.

Section: Experimental Overviewmentioning

confidence: 99%

Iron Quantification in Dietary Supplements using Four Colorimetric Assays

Filgueiras

Borges

2022

“…In recent years, programming software such as MATLAB, − Python, − Octave, − R Project, and R Commander have been used to teach statistical calculations. Here, boxplots were built using Jeffreys’s Amazing Statistics Program (JASP) .…”

Section: Introductionmentioning

confidence: 99%

Data Visualization Using Boxplots: Comparison of Metalloid, Metal, and Nonmetal Chemical and Physical Properties

Borges

2023

In this activity, students observe chemical and physical properties of elements using an online periodic table, where these properties were shown numerically and by colors. Then the elements were divided into metals, nonmetals, and metalloids, and their periodic properties were visualized using statistical concepts such as normality, kurtosis, histograms, Q−Q plots, p values, and, most important, boxplots. These statistical tests were carried out using JASP, which is free and opensource software written in the R language.

“…10 Given the popularity of the beginner-friendly Python programming language in scientific research, 11 many introductory-level programming courses in the physical sciences have chosen to use Python as well. 12−14 To improve code readability and reproducibility, there is a growing trend to write and execute Python code inside Jupyter notebooks, 15 which are now commonplace in chemoinformatics/MI courses 14,16,17 and workshops. 18−20 These digital notebooks merge prose, Python code, and additional multimedia elements into rich computational narratives that provide a gentle introduction for students.…”

Section: ■ Introductionmentioning

confidence: 99%

“…In particular, when designing any informatics curriculum, it is appealing to include programming exercises to give students hands-on experience with authentic problem solving tasks . Given the popularity of the beginner-friendly Python programming language in scientific research, many introductory-level programming courses in the physical sciences have chosen to use Python as well. − To improve code readability and reproducibility, there is a growing trend to write and execute Python code inside Jupyter notebooks, which are now commonplace in chemoinformatics/MI courses ,, and workshops. − These digital notebooks merge prose, Python code, and additional multimedia elements into rich computational narratives that provide a gentle introduction for students. They are often provided as standalone files ( extension) for users to run locally, but this requires installing additional Python packages and managing software configurations on personal devices, which can be daunting for beginners.…”

Section: Introductionmentioning

confidence: 99%

Using Jupyter Tools to Design an Interactive Textbook to Guide Undergraduate Research in Materials Informatics

Chen

Asta

2022

With the growing desire to incorporate data science and informatics into STEM curricula, there is an opportunity to integrate research-based software and tools (e.g., Python) within existing pedagogical methods to craft new, accessible learning experiences. We show how the open-source Jupyter Book software can achieve this goal by creating a digital, interactive textbook compiled from Jupyter notebooks, which are already commonplace in research. Using Jupyter Book, we design an open-source, introductory materials informatics research curriculum where the Python programming exercises are supplemented with prose, graphics, slides, and discussion questions, all of which are embedded into a uniform web interface for streamlined access. Interactive programming capabilities, enabled through the JupyterHub cloud infrastructure, provide opportunities in these digital spaces for students to interrogate the code, test their own hypotheses, and deepen their comprehension. These authentic learning experiences demonstrate the broad utility of the Jupyter ecosystem in sustaining the growth of materials informatics education.