Using Image Recognition and Processing Technology to Measure the Gas Volume in a Miniature Water Electrolysis Device Constructed with Simple Materials Yizhou Ling,* ,∇ Pengwen Chen, ∇ Juan Li, Junyao Zhang, and Kai Chen* Cite This: J. Chem. Educ. 2020, 97, 695-702 Read Online ACCESS Metrics & More Article Recommendations * sı Supporting Information ABSTRACT: This paper proposes an experiment in which students build microdevices for conveniently electrolyzing water and use image recognition and processing technology to measure the volume of gas generated during the reaction in a noncontact way. Taking advantage of three ratio relationships between (i) gas volume and the area of bubbles, (ii) the factual size and the size on the picture, and (iii) the area on the picture and the pixel number, the experiment converts the measurement of trace gas volume into measurement of bubbles’ pixel number on the picture, which simplifies the complex problem. The device includes pencil refills as the electrodes, compressing the reaction space in the interval between two glass slides to squeeze the gas generated into flat-cylinder shaped bubbles with a fixed height. The students use a smartphone to take pictures and use software to process the images. Briefly, edges of bubbles are recognized and enhanced by PowerPoint, followed by a cutout process by Photoshop to get the bubbles’ image without the background; a histogram tool is then used for analyzing the pixel number of bubbles to finally get the volume of hydrogen and oxygen. Results of the survey among participating students proved the pedagogical effectiveness of this project. KEYWORDS: High School/Introductory Chemistry, Interdisciplinary/Multidisciplinary, Computer-Based Learning, Inquiry-Based/Discovery Learning, Gases, Electrochemistry, Electrolytic/Galvanic Cells/Potentials, Mathematics/Symbolic Mathematics, Microscale Lab, Water/Water Chemistry ■ INTRODUCTION The experiment of electrolyzing water is a basic experiment in high school that initiates students’ study of electrochemistry. During electrolysis experiments, students observe the gases generated after the water is connected to the power. By measuring the gas volume and calculating the volume ratio between the gases generated at the two electrodes to be approximately 2:1, the students can gain an understanding of the principles of electrolytic cells, energy conversion, and the composition of water. 1 The classic device for the experiment is a Hoffman electrolyzer, which is expensive and nonportable and has a slow reaction rate. 2-4 Considering these disadvan- tages, many teachers have canceled demonstration in class, which is unsatisfactory for students’ learning. 5 Some research studies have aimed at reducing the size and cost of the experimental device. 6-9 However, the method to measure the gas volume is still the classic “contacting measurement”, which needs a complex structure including a two-column space for storing and measuring the generated hydrogen and oxygen. Image processing technology is technology using a computer algorithm to process image information, including image enhancement, restoration, encoding, and compression. Image recognition technology refers to technology using a computer to analyze and understand the information in images, to realize the recognition of targets and objects in various patterns. 10 Currently, so-called image recognition and processing technology is widely used in different fields, such as engineering, 11,12 medical science, 13 chemistry, 14,15 food in- dustry, 16 environment science, 17 safety investigation, 18 and chemical education experiments. 19,20 This work introduces digital photography, image recognition, and processing technology into high school chemistry experiments to realize the “non-contact measurement” of gas volumes to simplify the experimental device and reduce costs. Students can conven- iently build the device in the lab or at home. (Considering high school students may lack programming skills and basic computer knowledge, here we mainly focus on image processing. For image recognition, only simple applications are involved, without advanced technology like deep learning or a neural network, which means that the image processing Received: August 20, 2019 Revised: January 18, 2020 Published: February 4, 2020 Article pubs.acs.org/jchemeduc © 2020 American Chemical Society and Division of Chemical Education, Inc. 695 https://dx.doi.org/10.1021/acs.jchemed.9b00777 J. Chem. Educ. 2020, 97, 695-702 Downloaded via SRINAKHARINWIROT UNIV on April 23, 2020 at 19:43:59 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.