SANTA: Smart and Automated Nanomanufacturing Technologies with Artificial Intelligence

SANTA：人工智能智能自动化纳米制造技术

• 批准号: 2736907
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2736907
• 关键词: SANTA; Smart; Automated; Nanomanufacturing; Technologies

With the rise of "nanomedicine" through the nanotechnology-enabled vaccines during the recent pandemic, there is a pressing need for advanced manufacturing technologies that can overcome the challenges of large-scale good manufacturing practice (GMP) production. Conventional methods for synthesizing pharmaceutical nanoparticles are limited by poorly characterised batch reactors, whilst relatively new technologies with flow systems in small (micrometre) scale enable precise control over nanoparticle features. Electrohydrodynamic (EHD) processes are a collection of state-of-the-art fabrication techniques that can generate structural features in micron to nano-size. Despite theoretical models and simulations to understand the mechanisms involved in EHD were developed, large-scale production with EHD is still hindered by the complex behaviour of the electrified jets and the lack of predictive models that encompass a plethora of process parameters. In addition, translation of the EHD process into industry is limited by human resources that possess expertise and multidisciplinary knowledge. This project aims to use machine learning (ML) as an extension of Industry 4.0 to regulate the key EHD process parameters as well as the workflow to achieve a sustainable manufacturing process and machine intelligence. Predictive and optimization ML algorithms will be developed to facilitate process monitoring and quality control through in-line systems and automation.

随着“纳米医学”在最近的大流行期间通过纳米技术使能的疫苗而兴起，迫切需要能够克服大规模良好生产规范（GMP）生产的挑战的先进制造技术。用于合成药物纳米颗粒的常规方法受到表征不佳的分批反应器的限制，而具有小（微米）尺度的流动系统的相对较新的技术能够精确控制纳米颗粒特征。电流体动力学（EHD）工艺是一系列最先进的制造技术，可以产生微米到纳米尺寸的结构特征。尽管开发了理论模型和模拟来理解EHD所涉及的机制，但EHD的大规模生产仍然受到电气化射流的复杂行为以及缺乏包含过多工艺参数的预测模型的阻碍。此外，将EHD工艺转化为工业受到拥有专业知识和多学科知识的人力资源的限制。该项目旨在使用机器学习（ML）作为工业4.0的扩展，以调节关键的EHD工艺参数以及工作流程，从而实现可持续的制造工艺和机器智能。将开发预测和优化ML算法，以通过在线系统和自动化促进过程监控和质量控制。