Defective Hydrogel-Based Paper and Smartphone-Assisted Intelligent Colorimetric Sensors for Dual Monitoring of Glyphosate and Copper Ions Based on Ru-CoMOF@MoS 2

草甘膦 化学 离子 催化作用 选择性 检出限 材料科学 荧光 灵敏度(控制系统) 比色法 生物传感器 无机化学 纳米技术
作者
Emmanuel Chigozie Aham,Ravi kumar Ayyanu,Arunjegan Amalraj,Charles Obinwanne Okoye,Maruf Ahmed,Yibin Deng,Zhen Zhang,Qi Xu
出处
期刊:ACS applied nano materials [American Chemical Society]
卷期号:9 (20): 9420-9432
标识
DOI:10.1021/acsanm.6c00940
摘要

Glyphosate and copper ions often coexist in food and the environment, posing combined health risks to humans, yet developing simple, sensitive, and rapid methods for their simultaneous detection remain challenging. Here, we present a defective hydrogel and smartphone-assisted colorimetric sensor for intelligent, reversible, and sequential monitoring of glyphosate (Gly) and copper ions (Cu2+). The sensor exploits the peroxidase-like properties of metal–organic frameworks (MOFs) CoMOF, Ru-CoMOF, and Ru-CoMOF@MoS2 to catalytically oxidize colorless 3,3,5,5-tetramethylbenzidine (TMB) into its blue oxidized form, oxTMB (oxidized 3,3,5,5-tetramethylbenzidine), in the presence of H2O2. Among these, Ru-CoMOF@MoS2 exhibited the highest catalytic activity due to enhanced active sites and electron transport and was therefore selected as the sensing material. In this system, the presence of glyphosate inhibits the catalytic oxidation of TMB, resulting in fading of the blue color, while subsequent addition of Cu2+ restores the blue color through the formation of a Gly–Cu complex, thereby reversing the inhibition. Based on this mechanism, the sensing system enables dual detection of Gly and Cu2+ with nanomolar sensitivity of 1.88 and 1.08 nM respectively, which are lower than the permissible limits of 5.32 μM for Gly and 31.5 μM for Cu2+ set by WHO, highlighting its high sensitivity and regulatory relevance. Furthermore, hydrogel encapsulation improves stability and portability, while paper-based formats and smartphone-assisted colorimetric analysis facilitates rapid, quantitative, on-site monitoring. This work offers an innovative MOF-based sensor for intelligent, real-time, and rapid dual-analyte detection in a single sensing platform for food and environmental water monitoring.
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