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Showing 1 to 15 of 81 results Save | Export
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Gagan Chandra Mandal; Forid Saikh; Sumit K. Ray; Kuheli Pramanik; Laboni Giri – Journal of Chemical Education, 2024
Detection of metal ions in solution has been performed without employing H[subscript 2]S or any other sulfide materials. The new method is free from the interference from anions. Identification of Na[superscript +], K[superscript +], and NH[subscript 4][superscript +] has been made possible directly from an aqueous extract of the sample mixture. A…
Descriptors: Inorganic Chemistry, Identification, Evaluation Methods, Classification
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Raoyu Qiu; Zequn Lin; Zican Yang; Liang Gao – Journal of Chemical Education, 2024
Machine learning (ML) is extensively applied in chemistry, particularly in vibrational spectroscopy. However, few teaching examples effectively demonstrate the capabilities of ML in classifying polymeric materials, exhibiting subtle spectral differences that elude visual discrimination. This study presents a teaching example specifically tailored…
Descriptors: Artificial Intelligence, Classification, Undergraduate Study, Chemistry
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Shin-Yu Kim; Inseong Jeon; Seong-Joo Kang – Journal of Chemical Education, 2024
Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds…
Descriptors: Chemistry, Science Instruction, Laboratory Experiments, Artificial Intelligence
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Thomas S. Kuntzleman; Andrea Matti; Dajena Tomco – Journal of Chemical Education, 2024
A mixture of salt water and isopropyl alcohol is immiscible, forming two separate liquids with the organic layer on top and aqueous layer on bottom. When universal indicator is added to such a mixture, all of the indicator compounds in the universal indicator preferentially dissolve in the alcohol--except for phenolphthalein at high pH. Mixtures…
Descriptors: Chemistry, Color, Science Education, Scientific Concepts
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Alanah Grant St. James; Luke Hand; Thomas Mills; Liwen Song; Annabel S. J. Brunt; Patrick E. Bergstrom Mann; Andrew F. Worrall; Malcolm I. Stewart; Claire Vallance – Journal of Chemical Education, 2023
Applications of machine learning in chemistry are many and varied, from prediction of structure-property relationships, to modeling of potential energy surfaces for large scale atomistic simulations. We describe a generalized approach for the application of machine learning to the classification of spectra which can be used as the basis for a wide…
Descriptors: Artificial Intelligence, Chemistry, Science Instruction, Classification
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Hyungjun Park; Dong Jun Shin; Junhua Yu – Journal of Chemical Education, 2021
Recently, various studies related to the photophysical properties of various nanoscale and subnanoscale particles have been actively carried out in the chemical and biological fields. However, the terminology of these nanoparticles has not been clearly defined, which causes confusion among research groups and students. This article aims to clarify…
Descriptors: Classification, Scientific Concepts, Definitions, Chemistry
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Brian P. Woods – Journal of Chemical Education, 2024
In this in-class activity, organic chemistry undergraduates try to place an assortment of molecules in chronological order from oldest to most recently synthesized. In the students' first attempt, they use their knowledge of reactions and synthesis to analyze the organic compounds for their structural complexity. Before a second attempt, the names…
Descriptors: Organic Chemistry, Entrepreneurship, Learning Activities, Undergraduate Students
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Simin Cheng; Zhuoning Xie; Qingyuan Hu; Yao Qian; Xiaoxiao Ma – Journal of Chemical Education, 2023
This study highlights the importance of incorporating modern mass spectrometry techniques into undergraduate education. By focusing on the structure-characterizing capability of MS, students are able to gain hands-on experience with cutting-edge techniques and better understand how MS can be used to solve real-world problems. The three-step lipid…
Descriptors: Undergraduate Students, Scientific Concepts, Science Instruction, College Science
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Watson, Gregory S.; Green, David W.; Watson, Jolanta A. – Journal of Chemical Education, 2021
In some universities, there is a significant population of first year chemistry students who enter the system with very little prior knowledge of the subject. This, coupled with preconceived ideas of subject difficulty, necessitates that the introduction of key concepts is carried out in a nonthreatening, engaging, simplistic, and efficacious…
Descriptors: Chemistry, Scientific Concepts, College Freshmen, Instructional Innovation
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Shia J. Badajos; Trisha Kate E. Obsioma; Tharah Tibette W. Tungal; Angelo Mark P. Walag – Journal of Chemical Education, 2023
One approach that has been gaining significant attention among chemistry education scholars and practitioners is the employment of game-based learning to teach least-learned and difficult topics at various educational levels. One reason for this is that it promotes active, constructive learning and makes learning science a fun experience through…
Descriptors: Science Instruction, Chemistry, Game Based Learning, Educational Games
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Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garci´a, Agusti´n Alejo; Bringas, Mauro; Morzan, Ezequiel; Onna, Diego – Journal of Chemical Education, 2021
Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks…
Descriptors: Undergraduate Students, Chemistry, Electronic Learning, Artificial Intelligence
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Noyes, Keenan; McKay, Robert L.; Neumann, Matthew; Haudek, Kevin C.; Cooper, Melanie M. – Journal of Chemical Education, 2020
Computer-assisted analysis of students' written responses to questions is becoming a possibility due to developments in technology. This could make such constructed response questions more feasible for use in large classrooms where multiple choice assessments are often considered a more practical option. In this study, we use a previously…
Descriptors: Automation, Artificial Intelligence, Computer Uses in Education, Classification
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Thrall, Elizabeth S.; Lee, Seung Eun; Schrier, Joshua; Zhao, Yijun – Journal of Chemical Education, 2021
Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces…
Descriptors: Undergraduate Students, Artificial Intelligence, Man Machine Systems, Science Education
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Baosen Zhang; Ariana Frkonja-Kuczin; Zhong-Hui Duan; Aliaksei Boika – Journal of Chemical Education, 2023
Computer vision (CV) is a subfield of artificial intelligence (AI) that trains computers to understand our visual world based on digital images. There are many successful applications of CV including face and hand gesture detection, weather recording, smart farming, and self-driving cars. Recent advances in computer vision with machine learning…
Descriptors: Classification, Laboratory Equipment, Visual Aids, Optics
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Mack, Michael R.; Hensen, Cory; Barbera, Jack – Journal of Chemical Education, 2019
Quasi-experiments are common in studies that estimate the effect of instructional interventions on student performance outcomes. In this type of research, the nature of the experimental design, the choice in assessment, the selection of comparison groups, and the statistical methods used to analyze the comparison data dictate the validity of…
Descriptors: Science Instruction, Comparative Analysis, Inferences, Validity
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