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

DMREF：合作研究：未来生物炼制行业多金属多相催化剂的设计和发现

• 批准号: 1534260
• 负责人: Andreas Heyden
• 金额: $ 84万
• 依托单位: University of South Carolina at Columbia
• 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=1534260&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Design; Discovery

#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.

#1534260 / #1534269Heyden, 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的不确定性，这是将生物质衍生化学品精炼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与统计误差分析相结合，提供了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，并且可以为催化材料的发现和设计新方法提供蓝图。 研究结果将通过网站和软件工具向催化界公布。具体来说，该项目涉及制备吸附在钌 (Ru)、铂 ( Pt) 或铑 (Rh) 沉积在无定形二氧化硅或碳载体上。催化剂将在结构、组成和表面酸度方面进行详细表征，然后在 SUCC HDO 反应中进行评估。 多尺度策略将用于基于研究人员实验室开发的 DFT 方法和技术的计算，旨在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将受到贝叶斯统计分析。 将对实验和计算方法进行改进，以尽量减少不确定性并获得理论和实验之间有意义的比较。 该研究中采用的方法可以潜在地指导除此处演示的特定催化剂和反应之外的许多应用的材料选择和催化剂设计。为实验和计算工作设定了严格的标准，与统计分析相结合，为迄今为止缺乏预测新催化材料的确定性提供了信心。所选反应是在生物质加工中，不仅展示了该方法在复杂系统中的应用，而且表明该方法在对可再生资源和能源可持续性很重要的水相和气相反应中的潜在用途。