Synthesis and Characterization of Dy2O3@TiO2 Nanocomposites for Enhanced Photocatalytic and Electrocatalytic Applications

IF 4.3 Q2 ENGINEERING, CHEMICAL
Balachandran Subramanian*, K. Jeeva Jothi, Mohamedazeem M. Mohideen, R. Karthikeyan, A. Santhana Krishna Kumar*, Ganeshraja Ayyakannu Sundaram, K. Thirumalai, Munirah D. Albaqami, Saikh Mohammad and M. Swaminathan*, 
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引用次数: 0

Abstract

Industrial wastewater pollution is a crucial global issue due to the increasing need for clean water. Traditional photocatalytic methods for eliminating harmful dyes are often ineffective and are environmentally damaging. This study introduces a new, efficient photocatalyst combining Dy2O3 with TiO2 using a single-step hydrothermal approach. Dy2O3@TiO2 nanostructures were synthesized and characterized by using XRD, SEM, EDS, TEM, BET, and UV–visible spectroscopy. Dy2O3 was evenly distributed on TiO2, preventing clumping and resulting in a larger surface area with more active sites. UV irradiation (365 nm) replaced the traditional thermal energy for photocatalytic dye breakdown, leveraging the varying conductivity of the Dy2O3@TiO2 nanocomposites. Incorporating Dy2O3 decreased band gaps, enhancing redox reactions and expanding the range of degradable contaminants. For Rhodamine B dye degradation, the Dy2O3@TiO2 composite demonstrated significantly higher degradation rates than Dy2O3 or TiO2 alone at reaction parameters such as neutral pH (pH 7) and catalyst concentration (2 g L–1). The hybrid material also demonstrated improved electrocatalytic activity in oxygen reduction reactions (ORRs) under alkaline conditions with an initial potential of 0.88 V and a Tafel slope of 73 mV dec–1. The enhanced catalytic activity and durability are attributed to the synergistic interaction between Dy2O3 and TiO2. This novel photocatalyst offers a sustainable alternative for treating industrial effluents while reducing the environmental impact.

用于增强光催化和电催化应用的 Dy2O3@TiO2 纳米复合材料的合成与表征
由于对清洁水的需求日益增长,工业废水污染已成为一个至关重要的全球性问题。传统的消除有害染料的光催化方法往往效果不佳,而且会破坏环境。本研究介绍了一种新型高效光催化剂,它采用一步水热法将 Dy2O3 与 TiO2 结合在一起。研究人员合成了 Dy2O3@TiO2 纳米结构,并利用 XRD、SEM、EDS、TEM、BET 和紫外-可见光谱对其进行了表征。Dy2O3 均匀地分布在 TiO2 上,防止了结块,从而获得了更大的表面积和更多的活性位点。利用 Dy2O3@TiO2 纳米复合材料的不同传导性,紫外线照射(365 纳米)取代了光催化染料分解的传统热能。Dy2O3 的加入减小了带隙,增强了氧化还原反应,扩大了可降解污染物的范围。对于罗丹明 B 染料的降解,在中性 pH 值(pH 值 7)和催化剂浓度(2 g L-1)等反应参数下,Dy2O3@TiO2 复合材料的降解率明显高于 Dy2O3 或单独的 TiO2。这种混合材料在碱性条件下的氧还原反应(ORRs)中也表现出更高的电催化活性,初始电位为 0.88 V,塔菲尔斜率为 73 mV dec-1。催化活性和耐久性的增强归功于 Dy2O3 和 TiO2 之间的协同作用。这种新型光催化剂为处理工业废水提供了一种可持续的替代方法,同时减少了对环境的影响。
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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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