Nanocube (NC) assemblies display complex superlattice behaviors, which require a systematic understanding of their nucleation and growth as well transformation toward construction of a consistent superlattice phase diagram. This work made use of Fe3O4 NCs with controlled environments, and assembled NCs into three-dimensional (3D) superlattices of simple cubic (sc), body-centered cubic (bcc), and face-centered cubic (fcc), acute and obtuse rhombohedral (rh) polymorphs, and 2D superlattices of square and hexagon. Controlled experiments and computations of in situ and static small-angle X-ray scattering (SAXS) as well as electron microscopic imaging revealed that the fcc and bcc polymorphs preferred a primary nucleation at the early stage of NC assembly, which started from the high packing planes of fcc(111) and bcc(110), respectively, in both 3D and 2D cases. Upon continuous growth of superlattice grain (or domain), a confinement stress appeared and distorted fcc and bcc into acute and obtuse rh polymorphs, respectively. The variable magnitudes of competitive interactions between configurational and directional entropy determine the primary superlattice polymorph of either fcc or bcc, while emergent enhancement of confinement effect on enlarged grains attributes to late developed superlattice transformations. Differently, the formation of a sc polymorph requires a strong driving force that either emerges simultaneously or is applied externally so that one easy case of the sc formation can be achieved in 2D thin films. Unlike the traditional Bath deformation pathway that involves an intermediate body-centered tetragonal lattice, the observed superlattice transformations in NC assembly underwent a simple rhombohedral distortion, which was driven by a growth-induced in-plane compressive stress. Establishment of a consistent phase diagram of NC-based superlattices and reconstruction of their assembly pathways provide critical insight and a solid base for controlled design and scalable fabrication of nanocube-based functional materials with desired superlattices and collective properties for real-world applications.

Due to high toxicity, arsenic is regarded as a major global environmental pollutant. The present study is investigated the potential factors influencing to elevate concentration of arsenic in groundwater, surface water, and soil of the Dongting basin. The arsenic contamination potential prediction map and categories were developed using various GIS techniques such as Ordinary Kriging and the Quantile method. Then the “Raster calculator” tool was applied to verify the impact of the factors on arsenic. Eighty-four single-factor, bi-factor, and multi-factor models were established to investigate effective combinations among factors of each phase. Additionally, statistical tests were computed to evaluate arsenic between classes and factors. The arsenic value varies in groundwater from 0.0001 to 0.1582 mg/l, while in surface water between 0.0001–0.0287 mg/l and soil sediments range from 1.8–45.69 mg/kg. JunShan and GongAn groundwater resources have been identified as posing a high risk to human health. The single factors showed the best match frequency of arsenic with a population density of 66.86% in water and land use depicted match frequency of arsenic 73.19% in soil. The statistical calculations with percentage frequency factors also depicted positive trends. The correlation of the factors with arsenic in soil and water showed slow oxidation and reduction in the groundwater system. Treated portable water could be the best option to reduce the health risk of the local community.

Arsenic is considered a poison because of its seriously toxic effects on the human body; elevated concentrations of arsenic in drinking water have been reported in different parts of the world. Investigating the arsenic distributions in soil, surface water (SW), and groundwater (GW) is an interesting topic of research, along with probing its correlations with local factors of the ecosystem and other hydrogeochemical parameters. This study mainly aims to investigate the impacts of various factors on elevated arsenic concentrations in water and soil. The following factors are assessed for their relationship to the propagation of arsenic in Jianghan Plain, which is the study area: population density, pumping rate, rain, land use, surface elevation, water level, and heavy metal contamination. The arsenic contamination potential prediction map and categories were developed using GIS-based techniques, such as ordinary kriging and quantile methods. Then, the “raster calculator” tool was applied to verify the impacts of the abovementioned factors on arsenic concentration. Eighty-four single-factor, bi-factor, and multi-factor models were established to investigate the effective combinations among the factors. Land use and pumping rate were identified from the soil through an equal frequency tool, whereas water population density and pumping rate were obtained with high matching percentages. The arsenic concentrations varied in the ranges of 0.0001–0.1582 mg/L in GW, 0.0003–0.05926 mg/L in SW, and 1.820–46.620 mg/kg in soil sediment. The single factors showed the best equal frequency of arsenic concentration in water for population density (68.62%) and in soil for land use (65.57%) and pumping (63.66%). Statistical calculations with percentage frequency factors also depicted a positive trend. Arsenic was reported to have high correlations with Fe in GW (r2 = 0.4193), with EC in SW (r2 = 0.4817), and with Cu in soil (r2 = 0.623). It is observed that the alkaline behaviors of water bodies are associated with arsenic mobility. Elevated arsenic values were observed in grids along surface flows with high anthropogenic activities and urbanization. Additionally, low concentrations of Fe depicted reduced activities in aquifer systems. Filtering drinking water as well as controlling the suspected sources and factors affecting concentrations of arsenic in the three phases are options for reducing the health risks of the local populations.