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Dial-a-particle: model-driven self-optimised manufacturing platform of nanoparticles

Dial-a-article：模型驱动的纳米颗粒自优化制造平台

基本信息

批准号：
EP/V025759/1
负责人：
Laura Torrente Murciano
金额：
$ 92.68万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV025759%2F1
关键词：
Dial particle model driven self

项目摘要

Their surface asymmetry, high surface-to-volume ratios and confinement quantum effects of nanoparticles result in unprecedented properties for applications in healthcare, diagnosis, energy storage, electronics, sensors, catalysis, etc. However, the full impact of these nanomaterials to overcome some of the most pressing global challenges, is hindered by the lack of a manufacturing technology capable of their production in a continuous and reproducible manner in large scale. A plethora of nanoparticle syntheses has been developed over the last decades, aiming for the control of the size, shape and composition of nanoparticles as property-determining parameters. Conventionally, nanoparticles are synthesised in poorly characterised batch reactors. Flow systems enable the continuous synthesis, but they are currently limited to rapid processes (ms to a few minutes) due to their inherent instability issues. This project will deliver a novel model-driven self-optimised manufacturing technology for on-demand size- and composition-customised nanoparticles. The dial-a-particle platform will integrate, for the first time, real-time characterisation and hydrodynamic understanding to enable the development of mathematical predictive algorithms. They will be the pillar for the autonomous identification of the most interesting manufacturing route. The distinguishing novelty features of this approach are i. On-demand synthesis with a wide range size (2-100 nm) and composition (core-shell, hollow, multicomponent), ii. Self-control to mitigate instability sources associated to multi-stage continuous processes (extending the current state-of-the-art from seconds to minutes/hours) and iii. Universality, thanks to the mechanistic knowledge underpinning the mathematical models.

它们的表面不对称性，高表面积与体积比和纳米粒子的限制量子效应导致在医疗保健，诊断，储能，电子，传感器，催化等应用中前所未有的性能。由于缺乏能够以连续和可重复的方式大规模生产它们的制造技术而受到阻碍。在过去的几十年中，已经开发了大量的纳米颗粒合成，目的是控制纳米颗粒的尺寸、形状和组成作为性质决定参数。传统上，纳米颗粒是在表征不佳的间歇式反应器中合成的。流动系统能够实现连续合成，但由于其固有的不稳定性问题，它们目前仅限于快速过程（毫秒至几分钟）。该项目将提供一种新型的模型驱动的自优化制造技术，用于按需定制尺寸和成分的纳米颗粒。Dial-a-particle平台将首次整合实时表征和流体动力学理解，以实现数学预测算法的开发。它们将成为自主识别最有趣的制造路线的支柱。这种方法的显著新奇特征是：具有宽范围尺寸（2-100 nm）和组成（核-壳、中空、多组分）的按需合成，ii.自我控制以减轻与多阶段连续过程相关联的不稳定性源（将当前的最新技术水平从秒扩展到分钟/小时）和iii.普遍性，这要归功于支撑数学模型的机械知识。