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

DMREF：合作研究：未来生物精炼行业多金属多相催化剂的设计和发现

• 批准号: 1534269
• 负责人: Jesse Bond
• 金额: $ 40万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534269&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Design; Discovery

#1534260 / #1534269Heyden, Andreas / Bond, Jesse Q.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.

#1534260/#1534269 Heyden，Andreas/Bond，Jesse Q。该提案利用统计分析来估计与琥珀酸催化加氢脱氧(Succ HDO)有关的实验数据和理论计算中的不确定性，该催化加氢脱氧是将生物质衍生化学品精制成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--为识别改进的催化材料提供了一种比单纯通过实验或理论更准确和更强大的方法。该方法适用于广泛的催化应用，并可为发现和设计催化材料的新方法提供蓝图。这项研究的结果将通过网站和软件工具提供给催化界。具体地说，该项目涉及制备定义明确且分散良好的锡(Sn)双金属簇合物，它们吸附在沉积在无定形二氧化硅或碳载体上的Ru(Ru)、Ptt或Rh(Rh)上。对催化剂的结构、组成和表面酸性进行了详细的表征，然后在成功的HDO反应中进行了评价。将采用多尺度策略，根据研究人员实验室开发的密度泛函方法和技术进行计算，目的是减少自由能估计中的不确定性。实验和计算分析中的不确定性都将接受贝叶斯统计分析。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。研究中采用的方法可能会指导材料的选择和催化剂的设计，在这里展示的特定催化剂和反应之外的许多应用。实验和计算工作都设定了严格的标准，当与统计分析相结合时，提供了迄今为止缺乏的预测新催化材料的确定性的信心。选定的反应用于生物质加工，不仅展示了这些方法在复杂系统中的应用，而且表明这些方法在水相和气相反应中的潜在用途，这些方法对可再生资源和能源可持续发展非常重要。