Determination of Copper Ion in Drinking Water Using Poly(vinyl alcohol)/3-Mercaptopropionic Acid-Capped InP/ZnS Quantum Dots Fluorescent Film

doi:10.7506/spkx1002-6630-20230724-261

FOOD SCIENCE ›› 2024, Vol. 45 ›› Issue (14): 227-233.doi: 10.7506/spkx1002-6630-20230724-261

• Safety Detection • Previous Articles Next Articles

Determination of Copper Ion in Drinking Water Using Poly(vinyl alcohol)/3-Mercaptopropionic Acid-Capped InP/ZnS Quantum Dots Fluorescent Film

ZHANG Jiaran, XU Zeyu, YU Jie, SHI Ce, YUE Yuntao

(1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 2. National Engineering Research Center for Agri-product Quality Traceability, Key Laboratory of Cold Chain Logistics Technology for Agro-product, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Online:2024-07-25 Published:2024-08-04

Abstract

Abstract: PVA/MPA-InP/ZnS QD fluorescent film for rapid detection of trace Cu2+ was developed using 3-mercaptopropionic acid (MPA)-capped InP/ZnS quantum dots (MPA-InP/ZnS QDs) as the fluorescence-sensitive material and polyvinyl alcohol (PVA) as the substrate. The microstructure, optical properties and mechanical properties of the fluorescent film were characterized. The results showed that PVA interacted with MPA-InP/ZnS QDs through hydrogen bonds. The fluorescence intensity of the film was significantly correlated with Cu2+ concentration in the range of 0–1 000 nmol/L (R2 = 0.95). The limit of detection (LOD) for Cu2+ was 23 nmol/L, and the response time was 6 minutes. Finally, the film was applied to the detection of trace Cu2+ in drinking water, beverages and alcoholic drinks with the advantages of high selectivity, rapid response and good portability, which exhibited a broad application prospect.

Key words: copper ion; fluorescent film; quantum dots; drinking water; rapid detection

CLC Number:

TS201.6

ZHANG Jiaran, XU Zeyu, YU Jie, SHI Ce, YUE Yuntao. Determination of Copper Ion in Drinking Water Using Poly(vinyl alcohol)/3-Mercaptopropionic Acid-Capped InP/ZnS Quantum Dots Fluorescent Film[J]. FOOD SCIENCE, 2024, 45(14): 227-233.

[1]	Jiang JinYou Shu-Long ZHANG Zhen Yang. Research progress on detection of food hazard factors and safety risk prediction [J]. FOOD SCIENCE, 0, (): 0-0.
[2]	Cui-Ting WANG Xue Liu. Research Progress on Bioaccumulation Characteristics and Uptake Mechanisms of Arsenic and Mercury in Edible Mushrooms [J]. FOOD SCIENCE, 0, (): 0-0.
[3]	Xin ZHANG Jia-bin YU. A Trusted Sharing Model for Risk Information of Food Full-process and All-information Based on Blockchain and Federated Learning [J]. FOOD SCIENCE, 0, (): 0-0.
[4]	XIONG Huan, HE Yue, LIU Lei, ZHAO Qiyang, ZHANG Yaohai, JIAO Bining, WANG Chengqiu. Residue Dynamics and Dietary Risk Assessment of Flusilazole and Ningnanmycin in Citrus Juice Processing [J]. FOOD SCIENCE, 2024, 45(14): 249-256.
[5]	YAN Ting, GE Tiansi, HUANG Caihuan, LUO Ziming, OU Shiyi, ZHENG Jie. Research Progress on Polycyclic Aromatic Hydrocarbons: Formation, Deleterious Effects and Control Technologies [J]. FOOD SCIENCE, 2024, 45(14): 257-266.
[6]	HAO Jinhua, GONG Ziqi, LI Min, YIN Jiaqi, HE Jiaqi, LI Yanqing, ZHANG Chunling, ZHANG Min. Research Progress in Electrochemical Aptamer-Based Sensors for Salmonella Detection [J]. FOOD SCIENCE, 2024, 45(14): 277-287.
[7]	. Research progress of mathematical simulation and precise prevention and control strategy on cross-contamination risk of foodborne pathogens [J]. FOOD SCIENCE, 0, (): 0-0.
[8]	进华 FimA 特异性适配郝 Zi-Qi Gong Min LI Jia-Qi YIN Jiaqi JiaHe Min Zhang. Progress on electrochemical aptamer sensors in Salmonella [J]. FOOD SCIENCE, 0, (): 0-0.
[9]	. Residual Dynamics and Risk Assement of Flusilazole and Ningnanmycin in Citrus Juice Processing [J]. FOOD SCIENCE, 0, (): 0-0.
[10]	. Nephrotoxicity of PBAT-modified starch material migrants in vitro [J]. FOOD SCIENCE, 0, (): 0-0.
[11]	GUO Xianglan, WANG li, JIN Xuebo, YU Jiabin, BAI Yuting, LI Hanyu, WEI Li’ang, MA Qian, WEN Haoran. Expansion and Prediction of Small Sample Data of Contaminants in Grain Processing Using Combination of Generative Adversarial Networks and Deep Forest [J]. FOOD SCIENCE, 2024, 45(12): 22-30.
[12]	Xiang-Lan GUO Jia-bin YU Han-Yu LI 倩 Hao-Ran WEN. Machine Learning-Expansion and Prediction of Small Sample Data on Contaminants in Grain Processing Based on the Combination of GAN and DF [J]. FOOD SCIENCE, 0, (): 0-0.
[13]	. Virtual Screening and Activity Evaluationof Key Antibiofilm Components in Green Sichuan Pepper Essential Oil Against Bacillus amyloliquefaciens [J]. FOOD SCIENCE, 0, (): 0-0.
[14]	Yu-xuan HE Fei-Wu LI Si-tong ZHANG. Method of Quantitative Detection for the Genetically Modified Crop by Digital PCR Based on Screening Marker FMV 35S [J]. FOOD SCIENCE, 0, (): 0-0.
[15]	LIU Yijun, YAN Wei, LONG Likun, MA Yue, HE Yuxuan, ZHAO Ning, LI Feiwu, ZHANG Sitong. A Digital Polymerase Chain Reaction Method Based on Figwort Mosaic Virus 35S Promoter (FMV 35S) for Quantitative Detection of Genetically Modified Crops [J]. FOOD SCIENCE, 2024, 45(11): 278-284.

Determination of Copper Ion in Drinking Water Using Poly(vinyl alcohol)/3-Mercaptopropionic Acid-Capped InP/ZnS Quantum Dots Fluorescent Film

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments