Citation: Zhaojing Huang, Hao Li, Jiayi Luo, Shunxing Li, Ming Zhao, Fengjiao Liu, Haijiao Xie. Deep learning-based simultaneous bioavailability assessment and speciation analysis of dissolved organic copper[J]. Chinese Chemical Letters, ;2025, 36(5): 110209. doi: 10.1016/j.cclet.2024.110209 shu

Deep learning-based simultaneous bioavailability assessment and speciation analysis of dissolved organic copper

Zhaojing Huang ^{a, b, 1} ,
Hao Li ^{c, 1} ,
Jiayi Luo ^{b, d, 1} ,
Shunxing Li ^{b, e, *,} ,
Ming Zhao ^b ,
Fengjiao Liu ^{b, e} ,
Haijiao Xie ^f

a.
College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
b.
College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
c.
College of Computer and Data Science/College of Software, Fuzhou University, Fuzhou 350108, China
d.
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
e.
Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 363003, China
f.
Hangzhou Yanqu Information Technology Company of Limited Liability, Hangzhou 310003, China

lishunxing@mnnu.edu.cn

Received Date: 6 May 2024
Revised Date: 25 June 2024
Accepted Date: 2 July 2024
Available Online: 4 July 2024

Figures(5)

Algal copper uptake (i.e., Cu bioavailability) in the euphotic zone plays a vital role in algal photosynthesis and respiration, affecting the primary productivity and the source and sink of atmospheric carbon. Algal Cu uptake is controlled by natural dissolved organic Cu (DOCu) speciation (i.e., complexed with the dissolved organic matter) that conventionally could be tested by model prediction or molecular-level characterizations in the lab, while DOCu uptake are hardly directly assessed. Thus, the new chemistry-biology insight into the mechanisms of the Cu uptake process in algae is urgent. The DOCu speciation transformation (organic DOCu to free Cu(Ⅱ) ions), enzymatic reduction-induced valence change (reduction of free Cu(Ⅱ) to Cu(Ⅰ) ions), and algal Cu uptake at the algae-water interface are imitated. Herein, an intelligent system with DOCu colorimetric sensor is developed for real-time monitoring of newly generated Cu(Ⅰ) ions. Deep learning with whole sample image-based characterization and powerful feature extraction capabilities facilitates colorimetric measurement. In this context, the Cu bioavailability with 7 kinds of organic ligands (e.g., amino acids, organic acids, carbohydrates) can be predicted by the mimetic intelligent biosensor within 15.0 min, i.e., the DOCu uptake and speciation is successfully predicted and streamlined by the biomimetic approach.
- Metal bioavailability,
- Metal speciation,
- Primary productivity,
- Intelligent analysis,
- Biomimetic sensor
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