Simple fabrication of a hexagonal prisms with hexagonal pyramid tips V2O5@MOF(V, Co) and its application as electrochemical sensor for Pb2+

Vein-Like Ni-BTC@Ni3S4 with Sulfur Vacancy and Ni3+ Fabricated In Situ Etching Vulcanization Strategy for an Electrochemical Sensor of Dopamine

In this study, a novel Ni-BTC@Ni₃S₄ composite was fabricated by solvothermal reaction using an in situ etching vulcanization strategy and characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), electron paramagnetic resonance (EPR), and Brunauer-Emmett-Teller (BET) analyses. The existence of a sulfur vacancy and Ni³⁺ in the as-prepared vein-like Ni-BTC@Ni₃S₄ greatly promoted the electrochemical sensing activity of the materials. Herein, a simple electrochemical sensor (Ni-BTC@Ni₃S₄/CPE) has been fabricated and used for the detection of dopamine (DA). The current signal of the Ni-BTC@Ni₃S₄/CPE-modified electrode was linear with the concentration of DA in the range of 0.05-750 μM (R² = 0.9995) with a sensitivity of 560.27 μA·mM^-1·cm^-2 and a detection limit of 0.016 μM. At the same time, the sensor has good stability and anti-interference ability. This study could provide a new idea and strategy for the structural regulation of composite electrode-modified materials and sensitive sensing detection of small biological molecules.

Recent Progress in Morphology-Tuned Nanomaterials for the Electrochemical Detection of Heavy Metals

Heavy metals are one of the most important classes of environmental pollutants which are toxic to living beings. Many efforts are made by scientists to fabricate better sensors for the identification and quantification of heavy metal ions (HMI) in water and food samples to ensure good health. Electrocatalysts have been demonstrated to play an important role in enhancing the sensitivity and selectivity of HMI detection in electrochemical sensors. In this review, we presented morphologically well-tuned nanomaterials used as efficient sensor materials. Based on the molecular dimensions, shapes, and orientation, nanomaterials can be classified into 0-D, 1-D, 2-D, and 3-D nanomaterials. Active surface areas with significant exposure of active sites and adsorption-desorption abilities are extensively varied with dimensionality, which in turn ultimately influence the sensing performance for HMI.