Robust optimization of nanoparticle synthesis in a supercritical CO2 process for energy applications

需要格罗弗系统方法来量化纳米制造过程中的不确定性，并随后设计出对这些不确定性具有健壮性的过程。由于固有的随机动力学，以及对温度和压力等宏观过程输入的敏感性，纳米级现象对制造提出了新的挑战。然而，目前正在开发利用纳米科学的新发现和进步的过程，需要具有成本效益的工程方法和工具来更有效地探索设计空间，以开发纳米技术产品。在这项工作中，PI专注于金属纳米颗粒的合成，这些纳米颗粒在从能源到医学的广泛应用中得到了应用。例如，需要尺寸和尺寸分布受控的纳米颗粒来创造高性能的催化剂，用于柴油发动机的NOx处理，与汽油发动机相比，这种催化剂产生的二氧化碳排放量更低。然而，开发一种高通量的制造工艺来以经济高效的方式制造耐用的负载型催化剂一直难以实现，部分原因是设计上的权衡，比如更高的性能，但在较小的纳米颗粒尺寸下耐用性较低。此外，由于粒子成核时间的固有分布，以及由于操作条件和噪声变量的漂移，在单个批次的纳米颗粒内以及批次之间都存在显著的变异性。该项目是一种综合的方法，使用通过严格的贝叶斯方法整合的各种信息源来稳健地优化批处理过程。首先，将开发平均过程行为的机械模型，这在工程学科中很常见。由于纳米级现象的模型通常在制造公差范围内不准确，机械模型将补充随机组件，将批次内和批次间的变化与可控的工艺参数和噪声变量联系起来，以实现稳健的工艺设计。专家意见有助于模型趋势和预期变化，以便将模型升级为随机-机械模拟工具。生成的模拟数据将被用来建立一个统计-机理模型，该模型比模拟模型简单，适合于有效地探索工艺配方。然后，将根据制定的最优试验设计方案收集物理数据，以验证和改进统计力学模型。最后，精炼的模型将被用来经济高效地搜索优化的工艺配方，以获得所需的纳米颗粒尺寸和窄的尺寸分布，同时最小化批次之间的差异。这一方法将弥合目前统计学中的稳健设计领域和工程中的机械建模领域之间的脱节。整合关于平均行为和方差的所有信息源需要特定领域的知识和机械理解。这种过程变量的均值和方差的建模方法是推导出稳健优化过程的配方所必需的。PI团队具备独特的能力来开发这一新方法，以实现稳健的流程优化。他们结合了实验、机械建模、过程控制和实验设计方面的专业知识，以及我们与行业的密切合作。将参与该项目的不同教职员工和学生(研究生、本科生和高中生)将获得经验和洞察力，使他们能够在跨学科的纳米制造环境中工作。

0933430GroverSystematic methods are needed to quantify uncertainty in nanomanufacturing processes, and subsequently to design processes that are robust to these uncertainties. Nanoscale phenomena present a new challenge for manufacturing, due to the inherent stochastic dynamics, in addition to sensitivities to macroscopic process inputs like temperature and pressure. However, processes are currently being developed to take advantage of the new discoveries and advancements in nanoscience, and cost-effective engineering approaches and tools are needed to more efficiently explore the design space to develop nanotechnology-enabled products. In this work the PIs focus on the synthesis of metal nanoparticles, which are used in a wide range of applications from energy to medicine. For example, nanoparticles of controlled size and size distribution are needed to create high performance catalysts for NOx treatment in diesel engines, which produce lower CO2 emissions relative to gasoline engines. However, developing a high-throughput manufacturing process to create durable supported catalysts in a cost-effective manner has been elusive, in part due to design tradeoffs like higher performance but lower durability at smaller nanoparticle size. Moreover, significant variability exists both within a single batch of nanoparticles, due to the inherent distribution of particle nucleation times, and also between batches, due to drift in operating conditions and noise variables.This project is a comprehensive methodology for robust optimization of a batch process, using various sources of information integrated by a rigorous Bayesian method. First, mechanistic models of mean process behaviors, as is common in the engineering disciplines, will be developed. Since models of nanoscale phenomena are typically not accurate within manufacturing tolerances, mechanistic models will be supplemented with stochastic components linking within- and between-batch variations to controllable process parameters and noise variables for robust process design. Expert opinions help model trends and expected variance for upgrading the models into a stochastic-mechanistic simulation tool. The simulated data generated will be used to build a statistical-mechanistic model, which is less complex than the simulation model, suitable for efficient exploration of process recipes. Then, physical data will be collected based on optimal experimental design plans developed to validate and improve the statistical-mechanistic model. Finally, the refined model will be used to cost-effectively search for the optimized process recipe, to achieve the desired nanoparticle size with a narrow size distribution while minimizing batch-to-batch variation.Intellectual merit. The current disconnect between the fields of robust design in statistics and mechanistic modeling in engineering will be bridged by this methodology. Incorporating all sources of information on mean behavior and variance requires domain-specific knowledge and mechanistic understanding. This modeling approach for mean and variance of process variables is required to derive the recipe for a robust optimal process.Broader impact. The PI team is uniquely equipped to develop this new methodology for robust process optimization. They combine expertise in experiments, mechanistic modeling, process control, and experimental design, along with our close collaborations with industry. The diverse team of faculty and students (graduate, undergraduate, and high school) who will participate in the project will gain experience and insight that will allow them to work in interdisciplinary nanomanufacturing environments.