Biocompatible Cs2PtX6 (X = Cl, Br, I) Vacancy Ordered Perovskites and Shewanella oneidensis MR-1 Bacteria Hybrid for Potential Photocatalytic Solar Fuel Production

IF 4.3 Q2 ENGINEERING, CHEMICAL
Shweta Shinde, Muhammed Hamdan, Prerna Bhalla and Aravind Kumar Chandiran*, 
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Abstract

Semiconductor-bacterial hybrid systems have been shown to be effective for photochemical conversion. The combination of two systems delineates the light absorption from the catalytic ability, wherein a semiconductor absorbs light, generating an electron–hole pair, followed by the transfer of photogenerated charges to catalytically active bacteria that assume the roles of carrying out redox reactions. The halide perovskite materials possess excellent optoelectronic properties and, if they exhibit biocompatibility with microorganisms, shall provide an opportunity to carry out environmentally important redox reactions including carbon dioxide conversion to value added products. In this work, we report the biocompatibility of panchromatic visible light absorption and stable vacancy ordered halide perovskite (VOP), Cs2PtX6 (X = halide) with Shewanella oneidensis MR-1 nonphotosynthetic bacterium. This microbe is shown to grow in culture media containing VOP, and the growth rate is found to be unaffected by the presence of semiconductor media. Although Shewanella oneidensis MR-1 is a well-known metal-reducing bacteria, in this work, we find that the vacancy ordered perovskite materials remain intact with this bacterium. With constraint-based metabolic modeling, we report that this biohybrid system shall potentially be used for solar energy conversion of water and carbon dioxide to hydrogen and formate, respectively.

Abstract Image

Abstract Image

具有生物兼容性的 Cs2PtX6(X = Cl、Br、I)空位有序包晶石与 Shewanella oneidensis MR-1 细菌杂交,用于生产潜在的光催化太阳能燃料
半导体-细菌混合系统已被证明能有效地进行光化学转换。两个系统的结合将光吸收与催化能力区分开来,其中半导体吸收光,产生电子-空穴对,然后将光生电荷转移给具有催化活性的细菌,由细菌承担氧化还原反应的作用。卤化物包晶材料具有优异的光电特性,如果它们与微生物具有生物相容性,就有机会进行重要的环境氧化还原反应,包括将二氧化碳转化为高附加值产品。在这项工作中,我们报告了全色可见光吸收和稳定空位有序卤化物包晶(VOP)Cs2PtX6(X = 卤化物)与 Shewanella oneidensis MR-1 非光合细菌的生物相容性。结果表明,这种微生物能在含有 VOP 的培养基中生长,而且生长速度不受半导体培养基的影响。虽然 Shewanella oneidensis MR-1 是一种众所周知的金属还原细菌,但在这项研究中,我们发现这种细菌体内的空位有序透辉石材料仍然保持完好。通过基于约束的新陈代谢建模,我们发现这种生物杂交系统可用于将水和二氧化碳分别转化为氢气和甲酸盐的太阳能转化。
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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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