室温绿色合成法制备铂基直接甲醇燃料电池用可持续低层石墨烯

IF 4.3 Q2 ENGINEERING, CHEMICAL
Vildan Erduran, Ramazan Bayat, Iskender Isik, Tugba Bayazit and Fatih Şen*, 
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引用次数: 0

摘要

在这项研究中,提出了一种成本效益高、可扩展的低层石墨烯(LLG)生产方法,该方法使用过碳酸钠(SPC)作为绿色分层剂,并将其应用于燃料电池。得到的石墨烯在XRD分析中显示出信号高度的降低,表明层较薄。拉曼分析证实了7-8层石墨烯的存在。场发射扫描电镜分析显示其晶体结构均匀,适合多种应用。直接甲醇燃料电池(dmfc)被广泛认为是一种高效、环保的将化学能转化为电能的设备。石墨烯负载的铂纳米颗粒(NPs)作为催化剂在dmfc中的应用提高了其性能。本研究以SPC得到的石墨烯为原料,采用化学还原法制备了pt -石墨烯催化剂。通过XRD和SEM表征,证实了NPs在碳载体上的均匀分布。通过甲醇氧化研究,Pt@LLG和Pt催化剂的氧化值分别为57.73和21.45 mA/cm2。通过长期的稳定性和耐久性试验,发现Pt@LLG催化剂可以有效地用于金属氧化实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Production of Sustainable Low-Layer Graphene by Green Synthesis at Room Conditions for Platinum-Based Direct Methanol Fuel Cell

Production of Sustainable Low-Layer Graphene by Green Synthesis at Room Conditions for Platinum-Based Direct Methanol Fuel Cell

Production of Sustainable Low-Layer Graphene by Green Synthesis at Room Conditions for Platinum-Based Direct Methanol Fuel Cell

In this study, a cost-effective and scalable method for the production of low-layer graphene (LLG) using sodium percarbonate (SPC) as a green delamination agent and its application in fuel cells is proposed. The obtained graphene showed a decrease in signal height in XRD analysis, indicating thinner layers. Raman analysis confirmed the presence of 7–8 layers of graphene. Field-emission scanning electron microscopy analysis revealed a uniform crystal structure, making it suitable for various applications. Direct methanol fuel cells (DMFCs) are widely recognized as efficient and environmentally friendly devices for converting chemical energy to electrical energy. The utilization of graphene-supported platinum (Pt) nanoparticles (NPs) as catalysts in DMFCs enhances their performance. In this study, Pt-graphene catalysts were synthesized by the chemical reduction method with graphene obtained by using SPC. Characterization through XRD and SEM analyses confirmed the homogeneous distribution of NPs on the carbon support. As a result of methanol oxidation studies, 57.73 and 21.45 mA/cm2 values were obtained by using Pt@LLG and Pt catalysts, respectively. As a result of long-term stability and durability tests, it has been found that the Pt@LLG catalyst can be used effectively in metal oxidation experiments.

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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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