Newly-modeled graphene-based ternary nanocomposite for the magnetophotocatalytic reduction of CO2 with electrochemical performance

The development of CO₂ into hydrocarbon fuels has emerged as a green method that could help mitigate global warning. The novel structured photocatalyst is a promising material for use in a photocatalytic and magneto-electrochemical method that fosters the reduction of CO₂ by suppressing the recombination of electron−hole pairs and effectively transferring the electrons to the surface for the chemical reaction of CO₂ reduction. In our study, we have developed a novel-structured AgCuZnS₂–graphene–TiO₂ to analyze its catalytic activity toward the selective evolution of CO₂. The selectivity of each nanocomposite substantially enhanced the activity of the AgCuZnS₂–graphene–TiO₂ ternary nanocomposite due to the successful interaction, and the selectivity of the final product was improved to a value 3 times higher than that of the pure AgCuZnS₂ and 2 times higher than those of AgCuZnS₂–graphene and AgCuZnS₂–TiO₂ under ultra-violet (UV)-light (λ = 254 nm) irradiation in the photocatalytic process. The electrochemical CO₂ reduction test was also conducted to analyze the efficacy of the AgCuZnS₂–graphene–TiO₂ when used as a working electrode in laboratory electrochemical cells. The electrochemical process was conducted under different experimental conditions, such as various scan rates (mV·s^–1), under UV-light and with a 0.07 T magnetic-core. The evolution of CO₂ substantially improved under UV-light (λ = 254 nm) and with 0.07 T magnetic-core treatment; these improvements were attributed to the facts that the UV-light activated the electron-transfer pathway and the magnetic core controlled the pathway of electron-transmission/prevention to protect it from chaotic electron movement. Among all tested nanocomposites, AgCuZnS₂–graphene–TiO₂ absorbed the CO₂ most strongly and showed the best ability to transfer the electron to reduce the CO₂ to methanol. We believe that our newly-modeled ternary nanocomposite opens up new opportunities for the evolution of CO₂ to methanol through an electrochemical and photocatalytic process.     

Chemical composition and immunomodulatory activity of a pectic polysaccharide from the ground thistle Cirsium esculentum Siev.

The pectic polysaccharide cirsiuman CE was extracted from the stems of the ground thistle Cirsium esculentum Siev. using 0.7% aqueous ammonium oxalate, yielding 10.4% of the air-dried material. The backbone of cirsiuman was proved to consist of 1,4-α-d-galactopyranosyluronan blocks interconnected by 1,2-linked l-rhamnose residues. The side chains were attached to the 4-position of the l-rhamnopyranose residues. The side chains include 1,4-β-galactopyranan, which contains 3,4- and 4,6-substituted d-galactopyranose residues as branched points, and 1,5-α-arabinofuranan bearing 3,5-substituted α-l-arabinofuranose residues as the branching points. Oral administration of cirsiuman was found to prevent induction of oral tolerance to ovalbumin (OVA), increased the levels of serum IgG2a, and downregulated serum IgE responses, therefore it was tested for anti-allergic properties. The score of systemic anaphylactic reaction and the levels of both serum IgE and IL-4 were found to decrease; however, IFN-γ response was upregulated by cirsiuman feeding.     

Mapping pasture biomass in Mongolia using Partial Least Squares,Random Forest regression and Landsat 8 imagery

The aim of this study was to develop a robust methodology to estimate pasture biomass across the huge land surface of Mongolia (1.56 × 10⁶ km²) using high-resolution Landsat 8 satellite data calibrated against field-measured biomass samples. Two widely used regression models were compared and adopted for this study: Partial Least Squares (PLS) and Random Forest (RF). Both methods were trained to predict pasture biomass using a total of 17 spectral indices derived from Landsat 8 multi-temporal satellite imagery as predictor variables. For training, reference biomass data from a field survey of 553 sites were available. PLS results showed a satisfactory correlation between field measured and estimated biomass with coefficient of determination (R²) = 0.750 and Root Mean Square Error (RMSE) = 101.10 kg ha^?1. The RF regression gave similar results with R² = 0.764, RMSE = 98.00 kg ha^?1. An examination of feature importance found the following vegetation indices to be the most relevant: Green Chlorophyll Index (CL_green), Simple Ratio (SR), Wide Dynamic Range Vegetation Index (WDRVI), Enhanced Vegetation Index EVI₁ and Normalized Difference Vegetation Index (NDVI) indices. With respect to the spectral reflectances, Red and Short Wavelength Infra-Red2 (SWIR₂) bands showed the strongest correlation with biomass. Using the developed PLS models, a spatial map of pasture biomass covering Mongolia at a spatial resolution of 30 m was generated. Our study confirms the high potential of RF and PLS regression (PLSR) models to predict pasture biomass. The computationally simpler PLSR model is preferred for applications involving large regions. This method can be implemented easily, provided that sufficient reference data and cloud-free observations are available.     

New modeling of AgFeNi2S4-graphene-TiO2 ternary nanocomposite with chelate compounds and its photocatalytic reduction of CO2

Journal of Materials Science: Materials in Electronics - The most efficient method of converting CO2 to hydrocarbon fuels is a photocatalyst-based process, in which the efficient charge transfer...     

Photo-Electrochemical Reduction of CO2 to Methanol on Quaternary Chalcogenide Loaded Graphene-TiO2 Ternary Nanocomposite Fabricated via Pechini Method

Journal of Inorganic and Organometallic Polymers and Materials - Nano-sized catalysts have been widely studied for CO2 reduction to hydrocarbon fuels. Herein, we study the new-modeled ternary...