Quantitative Colorimetric Detection of Dissolved Ammonia Using Polydiacetylene Sensors Enabled by Machine Learning Classifiers
1
Issued Date
2022-06-07
Resource Type
eISSN
24701343
Scopus ID
2-s2.0-85131856127
Journal Title
ACS Omega
Volume
7
Issue
22
Start Page
18714
End Page
18721
Rights Holder(s)
SCOPUS
Bibliographic Citation
ACS Omega Vol.7 No.22 (2022) , 18714-18721
Suggested Citation
Siribunbandal P., Kim Y.H., Osotchan T., Zhu Z., Jaisutti R. Quantitative Colorimetric Detection of Dissolved Ammonia Using Polydiacetylene Sensors Enabled by Machine Learning Classifiers. ACS Omega Vol.7 No.22 (2022) , 18714-18721. 18721. doi:10.1021/acsomega.2c01419 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84080
Title
Quantitative Colorimetric Detection of Dissolved Ammonia Using Polydiacetylene Sensors Enabled by Machine Learning Classifiers
Author(s)
Other Contributor(s)
Abstract
Easy-to-use and on-site detection of dissolved ammonia are essential for managing aquatic ecosystems and aquaculture products since low levels of ammonia can cause serious health risks and harm aquatic life. This work demonstrates quantitative naked eye detection of dissolved ammonia based on polydiacetylene (PDA) sensors with machine learning classifiers. PDA vesicles were assembled from diacetylene monomers through a facile green chemical synthesis which exhibited a blue-to-red color transition upon exposure to dissolved ammonia and was detectable by the naked eye. The quantitative color change was studied by UV-vis spectroscopy, and it was found that the absorption peak at 640 nm gradually decreased, and the absorption peak at 540 nm increased with increasing ammonia concentration. The fabricated PDA sensor exhibited a detection limit of ammonia below 10 ppm with a response time of 20 min. Also, the PDA sensor could be stably operated for up to 60 days by storing in a refrigerator. Furthermore, the quantitative on-site monitoring of dissolved ammonia was investigated using colorimetric images with machine learning classifiers. Using a support vector machine for the machine learning model, the classification of ammonia concentration was possible with a high accuracy of 100 and 95.1% using color RGB images captured by a scanner and a smartphone, respectively. These results indicate that using the developed PDA sensor, a simple naked eye detection for dissolved ammonia is possible with higher accuracy and on-site detection enabled by the smartphone and machine learning processes.