Human health has been seriously endangered by arsenic pollution in drinking water. In this paper, iron hydroxide nanopetalines were synthesized through a precipitation method using KBH₄ and their performance and mechanism of As(V) and As(III) removal were investigated. The prepared material was characterized by SEM–EDX, XRD, BET, zeta potential and FTIR analyses. Batch experiments indicated that the iron hydroxide nanopetalines exhibited more excellent performance for As(V) and As(III) removal than ferrihydrite. The adsorption processes were very fast in the first stage, followed a relatively slower adsorption rate and reached equilibria after 24 h, and the reaction could be fitted best by the pseudo-second order model, followed by the Elovich model. The adsorption isotherm data followed to the Freundlich model, and the maximal adsorption capacities of As(V) and As(III) calculated by the Langmuir model were 217.76 and 91.74 mg/g at pH 4.0, respectively, whereas these values were 187.84 and 147.06 mg/g at pH 8.0, respectively. Thermodynamic studies indicated that the adsorption process was endothermic and spontaneous. The removal efficiencies of As(V) and As(III) were significantly affected by the solution pH and presence of PO₄^3– and citrate. The reusability experiments showed that more than 67% of the removal efficiency of As(V) could be easily recovered after four cycles. The SEM and XRD analyses indicated that the surface morphology and crystal structure before and after arsenic removal were stable. Based on the analyses of FTIR, XRD and XPS, the predominant adsorption mechanism was the formation of inner-sphere surface complexes by the surface hydroxyl exchange reactions of Fe–OH groups with arsenic species. This research provides a new strategy for the development of arsenic immobilization materials and the results confirm that iron hydroxide nanopetalines could be considered as a promising material for removing arsenic from As-contaminated water for their highly efficient performance and stability.

Arsenic (As(III)), more toxic and with less affinity than arsenate (As(V)), is hard to remove from the aqueous phase due to the lack of efficient adsorbents. In this study, a core–shell structured MnO₂@La(OH)₃ nanocomposite was synthesized via a facile two-step precipitation method. Its removal performance and mechanisms for As(V) and As(III) were investigated through batch adsorption experiments and a series of analysis methods including the transformation kinetics of arsenic species in As(III) removal, FTIR, XRD and XPS. Solution pH could significantly influence the removal efficiencies of arsenic. The adsorption process of As(V) occurred rapidly in the first 5 h and then gradually decreased, whereas the As(III) removal rate was relatively slower. The maximum adsorption capacities of As(V) and As(III) were up to 138.9 and 139.9 mg/g at pH 4.0, respectively. For As(V) removal, the inner-sphere complexes of lanthanum arsenate were formed through the ligand exchange reactions and coprecipitation. The oxidation of As(III) to the less toxic As(V) by δ-MnO₂ and subsequently the synergistic adsorption process by the lanthanum hydroxide on the MnO₂@La(OH)₃ nanocomposite to form lanthanum arsenate were the dominant mechanisms of As(III) removal. XPS analysis indicated that approximately 20.6% of Mn in the nanocomposite after As(III) removal were Mn(II). Furthermore, a small amount of Mn(II) and La(III) were released into solution during the process of As(III) removal. These results confirm its efficient performance in the arsenic-containing water treatment, such as As(III)-contaminated groundwater used for irrigation and As(V)-contaminated industrial wastewater.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network’s focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.