An amino-functionalized MgFe ₂ O ₄ /reduced graphene oxide (rGO) was prepared by the combination of MgFe ₂ O ₄ /rGO and 1,6-hexanediamine, and employed for the electrochemical detection of Cu( ii ) ions.

Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R²) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R² and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.

A reduced graphene oxide (rGO)/Fe3O4 composites modified screen-printed carbon electrode (SPCE) was used to determinate As(III) in a HAc-NaAc buffer solution. The rGO/Fe3O4 composites were prepared by a simple and one-pot synthesis method, and characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (SEM), FT-IR and Raman spectra. The electrochemical behaviors of the composite electrode were characterized by cycle voltammetry and electrochemical impedance spectroscopy. The experimental parameters, such as supporting electrolyte, solution pH, deposition potential, deposition time were optimized, respectively. The calibration curve for the detection of As(III) in the range of 2 to 20 ppb was I = -4.495 + 1.922C, with a coefficient of 0.994. The sensitivity was 1.922 μA/ppb, and with about twice Fe3O4 modified SPCE, a detection limit as low as 0.3 ppb was achieved. The proposed electrode was validated by analyzing the As(III) content in real water samples.