Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses

IF 6.1 Q1 CHEMISTRY, MULTIDISCIPLINARY
Diego Iglesias, Dina Haddad, Dr. Victor Sans
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引用次数: 2

Abstract

Digitalisation and industry 4.0 are set to profoundly change the way chemical and materials discovery and development work. The integration of multiple enabling technologies such as flow synthesis, automation, analytics, and real-time reaction control lead to highly efficient, productive, data-driven discovery and synthetic protocols. For instance, the development of flow chemistry enables the fine control and automation of process parameters such as flow rates, temperature, and pressure, which inherently enhances process efficiency. Flow chemistry presents a more sustainable means of manufacturing in terms of waste minimisation, as it enables the integration of synthetic processes with downstream processing. Furthermore, it allows the integration of analytical techniques to provide in situ process monitoring of large amounts of process and product data. The application of Artificial Intelligence (AI) and/or Machine Learning (ML) techniques allows rapid decision making that can optimise existing processes, and it has also been applied in the discovery of novel materials, synthetic pathways and chemicals. All this is contributing to an effective digitalisation of chemical and material synthetic processes from the laboratory to large-scale industrial deployment.

This paper presents recent developments in the effective digitalisation of chemical synthetic processes which integrates continuous flow synthesis, analytics and artificial intelligence technologies. Specifically, this paper illustrates the emerging trend of process digitalisation through the advanced syntheses of materials with catalytic, optical and optoelectronic applications.

Abstract Image

先进纳米材料合成过程数字化的最新进展
数字化和工业4.0将深刻改变化学和材料发现和开发的工作方式。集成多种技术,如流量合成、自动化、分析和实时反应控制,可实现高效、高产、数据驱动的发现和合成协议。例如,流动化学的发展使工艺参数(如流量、温度和压力)的精细控制和自动化成为可能,这从本质上提高了工艺效率。流动化学在废物最小化方面提供了一种更可持续的制造手段,因为它使合成过程与下游加工相结合。此外,它允许分析技术的集成,以提供大量的过程和产品数据的现场过程监测。人工智能(AI)和/或机器学习(ML)技术的应用允许快速决策,可以优化现有流程,并且它也被应用于新材料,合成途径和化学品的发现。所有这些都有助于从实验室到大规模工业部署的化学和材料合成过程的有效数字化。本文介绍了化学合成过程有效数字化的最新进展,该过程集成了连续流合成、分析和人工智能技术。具体来说,本文阐述了通过催化、光学和光电子应用的先进材料合成过程数字化的新兴趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.30
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0.00%
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