RII Track-4: NSF: An Integrated Multiphysics Machine Learning Modeling and Experimental Framework for Optimizing Micro-Needle Patches

RII Track-4：NSF：用于优化微针贴片的集成多物理场机器学习建模和实验框架

• 批准号: 2229555
• 负责人: Serveh Kamrava
• 金额: $ 21.45万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229555&HistoricalAwards=false
• 关键词: RII; Track; NSF; Integrated; Multiphysics

Microneedle patches (MNPs) have provided a solution for different problems associated with needle injection in children and adults such as needle phobia, pain, infection, and even the requirement for a specialist. MNPs deliver a local, pain-free, safe, high-efficiency, and cost-effective way for drug and vaccine delivery. The small needles on MNPs are barely visible to the naked eye. Therefore, manufacturing such products with such details requires state-of-the-art techniques. Among different methods, one of the most efficient techniques is additive manufacturing (3D printing), which itself is a complex process and is controlled by various environmental and physical parameters. Controlling and optimizing all factors at different stages of production is vital for achieving a target design of MNPs. The design of the final product, consequently, controls its mechanical properties. Since the process of optimizing all the parameters involved is computationally very expensive, a machine learning technique will be applied in this project. To test the hypothesis, the 3D printing equipment, which is uniquely available at Stanford University, will be advanced. The proposed research and the associated partnerships will pave the way for developing more efficient MNPs by shedding light on the underlying phenomena and integration of theory and experiments.This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project would provide a fellowship to an Assistant Professor and a graduate student University of Wyoming (UW). In the field of 3D printing, one of the popular printing techniques used in fabricating MNPs is continuous liquid interface production (CLIP), which is a category of vat polymerization technique. Aside from the variables involved in the manufacturing device, processes, and materials, the whole process occurs in a multiphysics environment, which has made the development of computational modeling complicated and time-demanding. All these variabilities can lead to insufficient repeatability, uncertainty, and inconsistency between the produced MNPs, and what is considered the target model, and often the targeted structure is not produced. Reducing uncertainty is one of the prominent problems in MNPs fabrication which we aim to study by integrating both theoretical and experimental tests within the machine-learning framework. We hypothesize that more accurate and effective MNPs, in terms of mechanical stability, can be produced when a large number of scenarios are tested in a closed-loop framework, and it also can help reduce the cost and required time significantly. The proposed research involves a real-time and supervised process that can potentially transform our understanding of the underlying parameters of efficiency and how such effects control the performances.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

微针贴片 (MNP) 为儿童和成人与针头注射相关的不同问题提供了解决方案，例如针头恐惧症、疼痛、感染，甚至需要专科医生。 MNP 提供了一种本地、无痛、安全、高效且经济高效的药物和疫苗递送方式。 MNP 上的小针肉眼几乎看不见。因此，制造具有如此细节的产品需要最先进的技术。在不同的方法中，最有效的技术之一是增材制造（3D打印），它本身是一个复杂的过程，并受到各种环境和物理参数的控制。控制和优化不同生产阶段的所有因素对于实现 MNP 的目标设计至关重要。因此，最终产品的设计控制着其机械性能。由于优化所有涉及参数的过程在计算上非常昂贵，因此该项目将应用机器学习技术。为了验证这一假设，斯坦福大学独有的 3D 打印设备将得到改进。拟议的研究和相关的合作伙伴关系将通过揭示潜在现象以及理论与实验的整合，为开发更高效的 MNP 铺平道路。该研究基础设施改进 Track-4 EPSCoR 研究人员 (RII Track-4) 项目将提供怀俄明大学（UW）助理教授和研究生的奖学金。在3D打印领域，用于制造MNP的流行打印技术之一是连续液体界面生产（CLIP），它是一类还原聚合技术。除了涉及制造设备、工艺和材料的变量之外，整个过程发生在多物理场环境中，这使得计算建模的开发变得复杂且耗时。所有这些可变性都可能导致所产生的 MNP 与目标模型之间的重复性不足、不确定性和不一致，并且通常不会产生目标结构。减少不确定性是 MNP 制造中的突出问题之一，我们的目标是通过在机器学习框架内整合理论和实验测试来研究这一问题。我们假设，在闭环框架中测试大量场景时，可以产生在机械稳定性方面更准确、更有效的 MNP，并且还可以帮助显着降低成本和所需时间。拟议的研究涉及实时和监督过程，有可能改变我们对效率的基本参数以及这些影响如何控制性能的理解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。