创新的天然自掺杂catio3钙钛矿与磺化生物炭耦合在可见光下制氢

IF 9 1区 工程技术 Q1 ENERGY & FUELS
Safaa Ragab , Marwa R. Elkatory , Mohamed A. Hassaan , Mohamed A. El-Nemr , Ahmed El Nemr
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引用次数: 0

摘要

本研究通过将自掺杂CaTiO3 (SD-CaTiO3)钙钛矿纳米颗粒与西瓜磺化生物炭(WMSB)结合,开发一种低成本的废物衍生光催化剂体系,推动可再生能源技术的发展,用于太阳能驱动的绿色制氢,通过可见光驱动水分解制氢。SD-CaTiO3通过两种不同的方法合成:一种是溶胶-凝胶法,以硝化的Triesta Marble (NTM)作为钙源,另一种是熔融盐法,将Triesta Marble粉末直接与TiO2结合。表征表明,溶胶-凝胶衍生材料具有优异的光催化性能,具有正交晶型结构,粒径范围为52.43 ~ 126.52 nm,与未掺杂的CaTiO3 (3.5 eV)相比,带隙减小了2.39 eV,能够有效吸收可见光。实验优化结果表明,两种合成方法的产氢量存在显著差异,WMSB掺入对其产氢量的影响显著。溶胶-凝胶SD-CaTiO3在pH为10、催化剂负载为900 mg/L时H2产率最高,为6781.48 μmol/L。当与200 mg/L WMSB配合使用时,在相同光照条件下,pH值为2时,系统性能达到9993.75 μmol/L。该过程使用人工神经网络(ANN)进行有效建模,该网络具有三个隐藏层(Log-Sigmoid, Tan-Sigmoid和线性激活函数),分别用于第1层,第2层和第3层隐藏层,仅在两个训练周期内就实现了最佳预测性能。本研究开发了一种用于可再生制氢(可见光下产氢量为9993.75 μmol/L)的废物源太阳能光催化剂体系,直接推进了太阳能燃料技术和循环经济一体化,为脱碳高耗能产业提供了动力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Innovative natural self-doped-CaTiO3 perovskite coupled with sulphonated biochar for hydrogen production under visible light
This study advances renewable energy technology by developing a low-cost, waste-derived photocatalyst system by using self-doped CaTiO3 (SD-CaTiO3) perovskite nanoparticles combined with watermelon sulfonated biochar (WMSB) for solar-driven green hydrogen production hydrogen production through visible-light-driven water splitting. The SD-CaTiO3 was synthesized via two distinct approaches: a sol-gel method using nitrated Triesta Marble (NTM) as a calcium source and a molten-salt method combining Triesta Marble powder directly with TiO2. Characterization revealed the sol-gel-derived material exhibited superior photocatalytic performance, featuring an orthorhombic crystalline structure with particle sizes ranging from 52.43 to 126.52 nm and a reduced bandgap of 2.39 eV compared to undoped CaTiO3 (3.5 eV), enabling effective visible light absorption. Experimental optimization demonstrated significant hydrogen production differences between the two synthesis methods and the effect of WMSB incorporation. The sol-gel SD-CaTiO3 alone achieved maximum H2 production of 6781.48 μmol/L at pH 10 with 900 mg/L catalyst loading. The system enhanced performance when combined with 200 mg/L WMSB, reaching 9993.75 μmol/L at pH 2 under identical light conditions. The process was effectively modelled using an artificial neural network (ANN) with three hidden layers (Log-Sigmoid, Tan-Sigmoid, and linear activation functions) for the 1st, 2nd and 3rd hidden layers, respectively, achieving optimal predictive performance in just two training epochs. This study develops a waste-derived solar photocatalyst system for renewable hydrogen production (9993.75 μmol/L yield under visible light), directly advancing solar fuel technologies and circular economy integration for decarbonizing energy-intensive industries.
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来源期刊
Renewable Energy
Renewable Energy 工程技术-能源与燃料
CiteScore
18.40
自引率
9.20%
发文量
1955
审稿时长
6.6 months
期刊介绍: Renewable Energy journal is dedicated to advancing knowledge and disseminating insights on various topics and technologies within renewable energy systems and components. Our mission is to support researchers, engineers, economists, manufacturers, NGOs, associations, and societies in staying updated on new developments in their respective fields and applying alternative energy solutions to current practices. As an international, multidisciplinary journal in renewable energy engineering and research, we strive to be a premier peer-reviewed platform and a trusted source of original research and reviews in the field of renewable energy. Join us in our endeavor to drive innovation and progress in sustainable energy solutions.
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