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

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，高表面因素比和纳米颗粒的量量子效应在医疗保健，诊断，能源存储，电子，电子学，传感器，催化等中的应用中具有前所未有大规模。在过去的几十年中，已经开发了大量的纳米颗粒合成，旨在控制纳米颗粒作为属性确定参数的大小，形状和组成。通常，纳米颗粒是在批处理反应器较差的情况下合成的。流系统可以连续合成，但是由于它们固有的不稳定性问题，目前它们仅限于快速过程（MS至几分钟）。该项目将提供一种新型的模型驱动的自我优化的制造技术，用于按需大小和成分定量的纳米颗粒。拨号粒子平台将首次集成实时表征和流体动力学理解，以使数学预测算法的发展。它们将是自主识别最有趣的制造路线的支柱。这种方法的新颖性特征是i。按需合成范围较宽（2-100 nm）和组成（核心，空心，多组分），ii。自我控制以减轻与多阶段连续过程相关的不稳定性来源（将当前的最新时间从几秒钟扩展到几分钟/小时）和III。普遍性，得益于基于数学模型的机械知识。