| Title: |
Intelligent Recognition of the Polarimeter's Field of View and Automatic Optical Rotation Measurement of Sucrose Hydrolysis Using Machine Learning |
| Language: |
English |
| Authors: |
Huan Xie (ORCID 0009-0000-3609-3227); Yanghaotian Wu; Zhenyu Chen; Zhongyun Wu; Yu-Qing Zheng (ORCID 0000-0003-3727-565X); Zhirong Liu (ORCID 0000-0001-5070-8048); Jinrong Xu |
| Source: |
Journal of Chemical Education. 2026 103(3):1586-1594. |
| Availability: |
Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc |
| Peer Reviewed: |
Y |
| Page Count: |
9 |
| Publication Date: |
2026 |
| Document Type: |
Journal Articles; Reports - Descriptive |
| Education Level: |
Higher Education; Postsecondary Education |
| Descriptors: |
Artificial Intelligence; Computer Uses in Education; Science Experiments; Automation; Optics; Measurement; Science Instruction; College Science |
| DOI: |
10.1021/acs.jchemed.5c01414 |
| ISSN: |
0021-9584; 1938-1328 |
| Abstract: |
Artificial intelligence (AI) and automation techniques have promoted the rapid development of scientific fields such as chemistry, biomedicine, and materials science, where multiple variables and tremendous data collection are required in experiments. By incorporating machine learning (ML), an independently devised digital control system, and integrating custom-developed software into the sucrose hydrolysis experiment, intelligent identification of the polarimeter's field of view and automatic data acquisition of the sucrose hydrolysis reaction are achieved. This innovation revolutionizes traditional experimental practices by replacing manual recognition and operation with automated processes, effectively addressing the inherent time-consuming and labor-intensive nature of conventional methods and thereby significantly improving experimental efficiency and accuracy. This novel, portable, and economical ML-based optical rotation measurement device will promote innovation in chemical experiment teaching models in the era of AI. |
| Abstractor: |
As Provided |
| Entry Date: |
2026 |
| Accession Number: |
EJ1499680 |
| Database: |
ERIC |