In this work, a ratiometric fluorescence sensing platform was established to detect Cu 2+ and D-PA ( d -penicillamine) based on nitrogen-doped Ti 3 C 2 MXene quantum dots ( N -MODs) that was prepared via a simple hydrothermal method and exhibited strong fluorescent and photoluminescence performance as well as excellent stability. Since the oxidation reaction between o -phenylenediamine (OPD) and Cu 2+ induced the formation of 2,3-diaminophenazine (ox-OPD) which not only can emerge an emission peak at 570 nm, but also inhibit the fluorescence intensity of N -MQDs at 450 nm, a ratiometric reverse fluorescence sensor via fluorescence resonance energy transfer (FRET) was designed to sensitively detect Cu 2+ , where N -MQDs acted as energy donor and ox-OPD as energy acceptor. More importantly, another considerably interesting phenomenon was that their catalytic oxidation reaction can be restrained in the presence of D-PA because of the coordination of Cu 2+ with D-PA, further triggering the obvious changes in ratio fluorescent signal and color, thus a ratiometric fluorescent sensor of determining D-PA was proposed also in this work. After optimizing various conditions, the ratiometric sensing platform showed rather low detection limits for Cu 2+ (3.0 nM) and D-PA (0.115 μM), coupled with excellent sensitivity and stability.
Rui Chen,Jinjie You,Yuge Chen,Zeming Zhang,Aili Sun,Hua Liu,Xizhi Shi
A highly selective and sensitive fluorescence sensor, using the decision tree (DT) machine learning, was successfully fabricated for the quantitative detection of Cu 2+ , which is based on near-infrared carbonized polymer dots (r-CPDs). Using o-phenylenediamine and 1,3,5-benzene tricarboxylic acid as raw materials first to synthesize the r-CPDs (quantum yield of 32.9%), which pyrrole NH ring can specifically form metal chelates with Cu 2+ hiding the electron leap result in the fluorescence burst, by a one-step hydrothermal synthesis method. Furthermore, the fluorescence sensor based on the r-CPDs was fabricated for ultra-sensitively detect Cu 2+ in the range of 0.5–80.0 nM (R 2 =0.9986) in aqueous environments and aquatic products with relative standard deviation (RSD) below 4.4% and a limit of detection (LOD) of 0.24 nM. Combined with the machine learning algorithm model, the r-CPDs fluorescence color changes accompanied by different Cu 2+ concentrations were classified. A self-developed smartphone application equipped with 3D printing technology to prepare portable cartridges successfully applied to rapid real-time detection of trace Cu 2+ in various practical samples. The experimental results show that the method has not only convenient for calculating but also accurate. Generally, this study displayed a perfect fusion of r-CPDs fluorescence sensors with machine learning which applied to the determination of Cu 2+ in environmental and aquatic products.
Cu 2+ serves as a significant biomarker in various physiological processes. This study presents the development of a novel Ru-UiO-TPEA functionalized metal-organic framework (MOFs) probe for Cu 2+ detection, employing both electrochemiluminescence (ECL) and electrochemical (EC) methods. Employing a dual ligand-assisted energy transfer approach, we synthesized Ru-UiO with optimal ECL and EC properties by adjusting the synthesis ratio of biphenyldicarboxylic acid to Ru(dcbpy) 3 2+ . This was followed by its conjugation with N-(2-aminoethyl)-N, N′, N′-tris(pyridin-2-ylmethyl) ethane-1,2-diamine (AE-TPEA) to form the highly conjugated Ru-UiO-TPEA probe for Cu 2+ detection. As the Cu 2+ concentration increased, the probe exhibited a diminished ECL signal and an enhanced differential pulse voltammetry (DPV) signal. The dual-mode detection method enabled cross-validation of the ECL and EC signals, enhancing accuracy and effectively minimizing errors. The ECL and EC methods had detection limits of 0.26 nM (range: 1 nM to 10 μM) and 0.65 nM (range: 2 nM to 80 μM), respectively. Meanwhile, these two methods can effectively detect Cu 2+ in rat brain microdialysis fluid. Notably, the measured concentrations correlated well with those determined by Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES), highlighting its potential in biological and clinical bioelectronic sensing applications.
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