Journal of Chemical Education最新文献

The application of chemistry concepts in biological settings plays an important role in the interdisciplinary field of drug discovery and development. This is true for molecular docking, where an understanding of intermolecular forces and noncovalent interactions is useful for rational drug design and development. Here we report the design and use of a molecular docking activity for cancer drug discovery for users that requires minimal coding knowledge. Although used in a drug discovery context, this activity can be incorporated into a range of undergraduate/graduate chemistry and biochemistry courses either as a stand-alone activity or integrated into existing curricula. The activity uses AutoDock Vina, AutoDockTools, Strawberry Perl, and PyMOL, all of which are free, open-source software. The activity is used to carry out molecular docking of multiple ligands at once and predict the binding energy of hits identified from a high-throughput drug repurposing screen against a target enzyme overexpressed in human tumors. Students analyze their docking results to determine drugs that should go on to further in vitro testing based on the predicted noncovalent ligand–protein interactions. This activity serves as an introduction to molecular docking and as a review of intermolecular forces, highlighting their importance in biological fields.

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.

Numerous syntheses of the green painter’s pigment malachite, Cu₂(OH)₂CO₃, are proposed in the literature, some yielding distinctly blue products. To help instructors choose a method that will produce a green product, a laboratory experiment was designed to test eight procedures for making malachite. All involve copper(II) sulfate or nitrate and a carbonate source: Na₂CO₃, CaCO₃, NaHCO₃, or carbonated water (seltzer water). Some reactant mixtures are heated, and others are aged for a few days at room or low temperatures before filtration. Students evaluated the colors of the products by comparing their CIELAB color space, measured using a reflectance spectrophotometer, to that of a sample of natural malachite. As the data were displayed in plots, the students’ ability to interpret graphical information was tested. Five of the eight syntheses yielded green products, more vividly green than natural malachite, while three yielded blue. Thermal decomposition of the green samples to CuO gave results consistent with the formula of malachite. Infrared spectra support identification of the green products but not the blue products with malachite.

We report an educational tool for the upper level undergraduate quantum chemistry or quantum physics course that uses a symbolic approach via the PySyComp Python library. The tool covers both time-independent and time-dependent quantum chemistry, with the latter rarely considered in the foundations course due to topic complexity. We use quantized Hamiltonian dynamics (QHD) that provides a simple extension of classical dynamics and captures key quantum effects. The PySyComp library can compute various concepts regarding the fundamental postulates of quantum mechanics, including normalized wave functions, expectation values, and commutators, which are at the core of solving the Heisenberg equations of motion. It provides a tool for students to experiment with simple models and explore the key quantum concepts, such as zero-point energy, tunneling, and decoherence.

The commonly accepted goal of doctoral education is to train students to be independent researchers and scientists. The backward design framework was used to model how graduate handbooks should be developed; by setting measurable outcomes and working backward to design programmatic elements that will meet those desired goals. Under the backward design framework, each of the programmatic elements of doctoral programs is based on learning goals designed to help to progress students to accomplish this overarching goal. Because the graduate student handbook represents the primary documentation of programmatic elements, it is possibly the only place where learning goals are explicitly written out. In this qualitative study, publicly available graduate handbooks from 60 chemistry departments were investigated for the learning goals of the programmatic elements to know how these contribute to the overarching goal of graduate education and compared to a literature-based model of the goals of each major programmatic element. Through document and thematic analysis, we found that most handbooks did not explicitly state the learning goals of the programmatic elements, indicating that backward design was not likely implemented fully during the crafting of these documents. Considering the prior success of backward design, this study implies that graduate handbooks written with an explicit alignment with backward design could better prepare students for the workforce and more broadly meet the desired goals of doctorate-level chemistry education.

Online conferencing platforms such as Zoom and Microsoft Teams have been widely adopted as substitutes for physical classes during the COVID-19 pandemic. This dramatic change is accompanied by many challenges for educators to keep their students engaged online and promote live interactions to mimic a real classroom environment. While progress has been made in teaching theoretical concepts in the online setting, teaching laboratory skills online remains challenging. Such skills are usually taught and learned and require a high level of human interaction. Here, we share our experience in designing Gather.town as an online learning space for a laboratory course to facilitate social interactions during the COVID-19 pandemic. Gather.town is a video conferencing platform that allows educators to construct individualized 2D spaces and interact with other people through their avatars. The use of avatars is hypothesized to be the key difference that distinguishes it from traditional video conferencing platforms. Gather.town has also been explored by researchers from other fields to be able to enhance online learning through improved interaction between students but not yet in the chemistry education space. Empirical evidence shows that students agree that the designed Gather.town has increased social interaction in the time of online learning.

This article describes practical lessons and experiences acquired as part of a journey in teaching a blind student at The University of Arizona to master the written and graphical language of first-semester organic chemistry and its associated concepts. These practical lessons include details on how to adapt an organic chemistry model set (with simple, minimal modifications) to make it suitable for teaching organic chemistry to blind and visually impaired students and lesson examples of how the modified model sets are an indispensable tool to effectively teach a majority of foundational topics in first-semester organic chemistry in concert with a tactile drawing board. Printing chemical structures and text in braille, along with semester printing preparations to ensure smooth experiences for students, teachers, and support staff, is a recommendation, as is the prioritization of one-on-one teaching to ensure the best possible outcomes.

The essential component of expanding an undergraduate curriculum is the inclusion of lab experiments in nanoscience and nanomaterials, which significantly impact health and the environment through their use in food, cosmetics, agriculture, and medicine. We designed a laboratory experiment based on the atomic force microscopy (AFM) analysis of the physical characteristics of polymer blends and crystals, including surface morphology, Young’s modulus, deformation, and stiffness. The laboratory exercise exposes students to the main aspects of the crystallization of polyethylene glycol and the formation of an immiscible polystyrene/polybutadiene blend, followed by optical microscopy and AFM characterization. In addition to providing information about the surface morphology and microstructure of the samples through AFM topography scanning, nanoindentation measurements allow for the mechanical characterization of materials with nanoscale resolution. Mechanical characterization offers students a broader application area where they can use their chemical understanding to regulate the material’s physical characteristics. AFM force curve mapping enables assessment of the components’ distribution in composite materials while analyzing each constituent independently with nanoscale precision. The versatility of AFM considerably increases the number of laboratory experiments that can be developed in undergraduate courses on nanoscience and nanomaterials. The knowledge acquired about polymer blending, crystallization, and their characterization at the nanoscale equips students with practical and transferable skills that they may apply in other chemistry and engineering classes to address real-world issues.

Student performance on open-response calculations involving acid and base solutions before and after instruction in general chemistry and analytical chemistry courses was compared with the output from the artificial intelligence chatbot ChatGPT. Applying a theoretical model of expertise for problem solving that includes problem conceptualization, problem strategy, and solution, it is found that students errors following instruction primarily involved problem conceptualization and the misapplication of heuristics like the Henderson–Hasselbalch equation When the same problems were used as input to ChatGPT the responses were comparable to worked examples found in general chemistry textbooks in terms of length and detail and usually displayed strong problem conceptualization. Response accuracy of the chatbot varied greatly for different topics, being best for calculations of pH for a strong acid or strong base and much lower for more complex problems involving titrations or aqueous salts. Chatbot and student errors differed in that the chatbot did not misapply heuristics but did make mathematical errors uncommon for students. The variability in the correctness of ChatGPT’s responses and the nature of its errors vis-à-vis students will influence its potential use as an instructional resource for calculations involving acids and bases.

An understanding of basic organic chemical reactivity is key for many biosciences students. The reactivities of different carbonyl groups affect their function in many biomolecules. A practical class, the two-step synthesis of paracetamol, has been devised to illustrate the electrophilic reactivities of carbonyls, which was covered in the accompanying lecture program. Students also examine the UV, IR, NMR, and mass spectra of the esters and amides, building further on the understanding gained in lectures. The practical work itself has been devised to be able to be run in bioscience laboratories with limited chemical facilities. The class has been enhanced during four academic years with strong support from the students.