DMREF: Collaborative Research: Design and Discovery of Multimetallic Heterogeneous Catalysts for a Future Biorefining Industry

#1534260/#1534269 Heyden，Andreas/该提案利用统计分析来估计与琥珀酸催化加氢脱氧（SUCC HDO）相关的实验数据和理论计算中的不确定性-这是将生物质衍生化学品精炼为具有商业价值的产品的重要反应。 所采用的实验和计算方法-结合统计误差分析-提供了一种比单独通过实验或理论更准确和更强大的方法来识别改进的催化材料。 该方法适用于广泛的催化应用，并可以为发现和设计催化材料的新方法提供蓝图。 研究结果将通过一个网站和软件工具提供给催化界，具体而言，该项目涉及制备吸附在无定形二氧化硅或碳载体上的钌（Ru）、铂（Pt）或铑（Rh）上的定义明确、分散良好的锡（Sn）簇。催化剂将详细表征结构、组成和表面酸性，然后在SUCC HDO反应中进行评价。 一个多尺度的战略将用于计算的基础上DFT方法和技术开发的研究人员的实验室，旨在减少不确定性的估计自由能。在实验和计算分析中均未发现的情况将进行贝叶斯统计分析。 将对实验和计算方法进行改进，以最大限度地减少不确定性，并在理论和实验之间进行有意义的比较。 在研究中采用的方法可以潜在地指导材料的选择和催化剂设计的许多应用超出了特定的催化剂和反应在这里展示。为实验和计算工作设定了严格的标准，当与统计分析相结合时，提供了迄今为止缺乏的信心，可以预测新的催化材料。所选择的反应是在生物质加工，不仅演示了复杂系统的方法的应用，但建议的方法在水和气相反应的可再生资源和能源的可持续性重要的潜在用途。

#1534260 / #1534269Heyden, Andreas / The proposal utilizes statistical analysis to estimate uncertainties in both experimental data and theoretical calculations relating to catalytic hydrodeoxygenation of succinic acid (SUCC HDO) - an important reaction in the refining of biomass-derived chemicals to commercially valuable products. The experimental and computational methods employed - combined with statistical error analysis - provide a more accurate and powerful approach for identifying improved catalytic materials than possible by either experiments or theory alone. The approach is applicable to a broad range of catalytic applications, and could provide a blueprint for a new approach to the discovery and design of catalytic materials. The results of the study will be made available to the catalysis community via a website and software tool.Specifically, the project involves preparation of well-defined and well-dispersed bimetallic clusters of tin (Sn) adsorbed on ruthenium (Ru), platinum (Pt) or rhodium (Rh) deposited on amorphous silica or carbon supports. The catalysts will be characterized in detail with respect to structure, composition, and surface acidity, and then evaluated in the SUCC HDO reaction. A multiscale strategy will be used for the computations based on DFT methods and techniques developed in the investigators' laboratory aimed at reducing uncertainties in the estimation of free energies. Uncertainties in both the experimental and computational analyses will be subjected to Bayesian statistical analysis. Refinements to both the experimental and computational methods will be made to minimize the uncertainties and obtain meaningful comparisons between theory and experiment. The methodology employed in the study can potentially guide materials selection and catalyst design for many applications beyond the specific catalysts and reaction demonstrated here. Rigorous standards are set for both the experimental and computational work, that when combined with statistical analysis, provide confidence heretofore lacking in the certainty with which new catalytic materials can be predicted. The selected reaction is in biomass processing and not only demonstrates application of the methods to complicated systems, but suggests potential use of the methods in both aqueous and gas-phase reactions important to renewable resources and energy sustainability.