The use of geotechnologies combined with remote sensing has become increasingly essential and important for efficiently and economically understanding land use and land cover in specific regions. The objective of this study was to observe changes in agricultural activities, particularly agriculture/livestock farming, in the North Forest Zone of Pernambuco (Mata Norte), a political-administrative region where sugarcane cultivation has historically been the backbone of the local economy. The region’s sugarcane biomass also contributes to land use and land cover observations through remote sensing techniques applied to digital satellite images, such as those from Landsat-8, which was used in this study. This study was conducted through digital image processing, allowing the calculation of the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), and the Leaf Area Index (LAI) to assess vegetation cover dynamics. The results revealed that sugarcane cultivation is the predominant agricultural and vegetation activity in Mata Norte. Livestock farming areas experienced a significant reduction over the observed decade, which, in turn, led to an increase in agricultural and forested areas. The most dynamic spatiotemporal behavior was observed in the expansion and reduction of livestock areas, a more significant change compared to sugarcane areas. Therefore, land use and land cover in this region are more closely tied to sugarcane cultivation than any other agricultural activity.

We report on the measurement of the response of Rhodamine 6G (R6G) dye to enhanced local surface plasmon resonance (LSPR) using a plasmonic-active nanostructured thin gold film (PANTF) sensor. This sensor features an active area of approximately ≈ 2.5 × 10¹³ nm² and is immobilized with gold nanourchins (GNU) on a thin gold film substrate (TGFS). The hexane-functionalized TGFS was immobilized with a 90 nm diameter GNU via the strong sulfhydryl group (SH) thiol bond and excited by a 637 nm Raman probe. To collect both Raman and SERS spectra, 10 μL of R6G was used at concentrations of 1 μM (6 × 10¹² molecules) and 10 mM (600 × 10¹⁴ molecules), respectively. FT-NIR showed a higher reflectivity of PANTF than TGFS. SERS was performed three times at three different laser powers for TGFS and PANTF with R6G. Two PANTF substrates were prepared at different GNU incubation times of 10 and 60 min for the purpose of comparison. The code for processing the data was written in Python. The data was filtered using the filtfilt filter from scipy.signals, and baseline corrected using the Improved Asymmetric Least Squares (ISALS) function from the pybaselines.Whittaker library. The results were then normalized using the minmax_scale function from sklearn.preprocessing. Atomic force microscopy (AFM) was used to capture the topography of the substrates. Signals exhibited a stochastic fluctuation in intensity and shape. An average corresponding enhancement factor (EF) of 0.3 × 10⁵ and 0.14 × 10⁵ was determinedforPANTFincubated at 10 and 60 min, respectively.

Quantum dot can be seen as an amazing nanotechnological discovery, including inorganic semiconducting nanodots as well as carbon nanodots, like graphene quantum dots. Unlike pristine graphene nanosheet having two dimensional nanostructure, graphene quantum dot is a zero dimensional nanoentity having superior aspect ratio, surface properties, edge effects, and quantum confinement characters. To enhance valuable physical properties and potential prospects of graphene quantum dots, various high-performance nanocomposite nanostructures have been developed using polymeric matrices. In this concern, noteworthy combinations of graphene quantum dots have been reported for a number of thermoplastic polymers, like polystyrene, polyurethane, poly(vinylidene fluoride), poly(methyl methacrylate), poly(vinyl alcohol), and so on. Due to nanostructural compatibility, dispersal, and interfacial aspects, thermoplastics/graphene quantum dot nanocomposites depicted unique microstructure and technically reliable electrical/thermal conductivity, mechanical/heat strength, and countless other physical properties. Precisely speaking, thermoplastic polymer/graphene quantum dot nanocomposites have been reported in the literature for momentous applications in electromagnetic interference shielding, memory devices, florescent diodes, solar cells photocatalysts for environmental remediation, florescent sensors, antibacterial, and bioimaging. To the point, this review article offers an all inclusive and valuable literature compilation of thermoplastic polymer/graphene quantum dot nanocomposites (including design, property, and applied aspects) for field scientists/researchers to carry out future investigations on further novel designs and valued property-performance attributes.

This study introduces a novel Groundwater Flooding Risk Assessment (GFRA) model to evaluate risks associated with groundwater flooding (GF), a globally significant hazard often overshadowed by surface water flooding. GFRA utilizes a conditional probability function considering critical factors, including topography, ground slope, and land use-recharge to generate a risk assessment map. Additionally, the study evaluates the return period of GF events (GFRP) by fitting annual maxima of groundwater levels to probability distribution functions (PDFs). Approximately 57% of the pilot area falls within high and critical GF risk categories, encompassing residential and recreational areas. Urban sectors in the north and east, containing private buildings, public centers, and industrial structures, exhibit high risk, while developing areas and agricultural lands show low to moderate risk. This serves as an early warning for urban development policies. The Generalized Extreme Value (GEV) distribution effectively captures groundwater level fluctuations. According to the GFRP model, about 21% of the area, predominantly in the city’s northeast, has over 50% probability of GF exceedance (1 to 2-year return period). Urban outskirts show higher return values (> 10 years). The model’s predictions align with recorded flood events (90% correspondence). This approach offers valuable insights into GF threats for vulnerable locations and aids proactive planning and management to enhance urban resilience and sustainability.

This study employs a transfer matrix, dynamic degree, stability index, and the PLUS model to analyze the spatiotemporal changes in forest land and their driving factors in Yibin City from 2000 to 2022. The results reveal the following: (1) The land use in Yibin City is predominantly characterized by cultivated land and forest land (accounting for over 95% of the total area). The area of cultivated land initially increased and then decreased, while forest land continued to decline and construction land expanded significantly. The rate of forest land loss has slowed (with the dynamic degree decreasing from −0.62% to −0.04%), and ecosystem stability has improved (the F-value increased from 2.27 to 2.9). The conversion of cultivated land to forest land is the primary driver of forest recovery, whereas the conversion of forest land to cultivated land is the main cause of reduction; (2) cultivated land is concentrated in the central and northeastern regions, while forest land is distributed in the western and southern mountainous areas. Construction land is predominantly located in urban areas and along transportation routes. Areas of forest land reduction are mainly found in the central and southern regions with rapid economic development, while areas of forest land increase are concentrated in high-altitude zones or key ecological protection areas. Stable forest land is distributed in the western and southern ecological conservation zones; (3) changes in forest land are primarily influenced by annual precipitation, elevation, and distance to rivers. Road accessibility and GDP have significant impacts, while slope, annual average temperature, and population density exert moderate influences. Distance to railways, aspect, and soil type have relatively minor effects. The findings of this study provide a scientific basis for the sustainable management of forest resources and ecological conservation in Yibin City.

This work investigated the photocatalytic properties of polymorphic nanostructures based on silica (SiO₂) and magnetite (Fe₃O₄) for the photodegradation of tartrazine yellow dye. In this sense, a fast, easy, and cheap synthesis route was proposed that used sugarcane bagasse biomass as a precursor material for silica. The Fourier transform infrared (FTIR) spectroscopy results showed a decrease in organic content due to the chemical treatment with NaOH solution. This was confirmed through the changes promoted in the bonds of chromophores belonging to lignin, cellulose, and hemicellulose. This treated biomass was calcined at 800 ℃, and FTIR and X-ray diffraction (XRD) also confirmed the biomass ash profile. The FTIR spectrum showed the formation of silica through stretching of the chemical bonds of the silicate group (Si-O-Si), which was confirmed by DXR with the predominance of peaks associated with the quartz phase. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) confirmed the morphological and chemical changes due to the chemical and thermal treatments applied to this biomass. Using the coprecipitation method, we synthesized Fe₃O₄ nanoparticles (Np) in the presence of SiO₂, generating the material Fe₃O₄/SiO₂-Np. The result was the formation of nanostructures with cubic, spherical, and octahedral geometries with a size of 200 nm. The SEM images showed that the few heterojunctions formed in the mixed material increased the photocatalytic efficiency of the photodegradation of tartrazine yellow dye by more than two times. The degradation percentage reached 45% in 120 min of reaction time. This mixed material can effectively decontaminate effluents composed of organic pollutants containing azo groups.