SANTA: Smart and Automated Nanomanufacturing Technologies with Artificial Intelligence

随着“纳米医学”在最近的大流行期间通过纳米技术启用的疫苗而兴起，迫切需要先进的制造技术来克服大规模良好制造规范(GMP)生产的挑战。传统的合成药物纳米颗粒的方法受到间歇反应器特性不佳的限制，而相对较新的技术具有小(微米)规模的流动系统，能够精确控制纳米颗粒的特征。电流体动力学(EHD)工艺是一种最先进的制造技术的集合，可以产生微米到纳米尺寸的结构特征。尽管已经开发了理论模型和模拟来了解EHD所涉及的机理，但由于带电射流的复杂行为以及缺乏包含过多工艺参数的预测模型，使用EHD进行大规模生产仍然受到阻碍。此外，将EHD过程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Industry 4.0的扩展，以调整关键的EHD工艺参数以及工作流程，以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。

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.