Computer Vision in Chemistry: Automatic Titration

Computer Vision in Chemistry: Automatic Titration
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化学中的计算机视觉:自动滴定

DOI:
10.1021/acs.jchemed.1c00810
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发表时间:
2021
影响因子:
3
通讯作者:
Kosenkov, Dmitri
Kosenkov, Dmitri
中科院分区:
化学2区
文献类型:
--
作者:
Kosenkov, Yana;Kosenkov, Dmitri

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提出了一种利用计算机视觉实现自动滴定实验的新技术。实验的目的是为了促进本科生对CV技术的基础知识及其在化学中的应用的理解。选择了滴定的标准化学程序,因为它是完成普通化学或类似基础化学课程的学生所熟知的。基于CV的自动化的一个显著优势是使用开源软件和现成的电子设备,因为不需要昂贵的专用设备或专有软件。该实验可以远程进行,无论是在线直播还是预先录制的视频。所报告的技术几乎对任何教育机构和个人都是可用的。所提出的技术为残疾学生远程执行滴定实验提供了负担得起的安全手段。因此,建议的实验适用于传统的实验室教学以及远程同步或异步课程交付,这在COVID-19大流行期间获得了实际的重要性。最后,提出的基于CV的自动化为重新思考传统的化学过程开辟了一个新的机会领域,其中计算机视觉用于提高标准实验室仪器和技术的性能。
A novel technology that employs computer vision (CV) to carry out an automatic titration experiment is presented. The experiment is designed to facilitate understanding of the basics of the CV technology and its application in chemistry among undergraduate students. The standard chemical procedure of titration has been chosen, since it is well-known to students who completed general chemistry or similar foundation chemistry courses. A significant advantage of CV-based automation is the use of open-source software and readily available electronic devices, as no expensive specialized equipment or proprietary software is required. The experiment can be performed remotely, either live online or with prerecorded videos. The reported technology is accessible to virtually any educational institution as well as to individuals. The proposed technology provides affordable and safe means for remote execution of titration experiments for students with disabilities. Therefore, the proposed experiment is suitable for traditional laboratory instruction as well as remote synchronous or asynchronous course delivery which gained practical importance during the COVID-19 pandemic. Finally, the proposed CV-based automation opens a new realm of opportunities for rethinking traditional chemistry procedures where computer vision is used to improve performance of standard laboratory instruments and techniques.
在两周内将第一年的物理化学单元转换为在线课程的经验教训
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