一种理解混合机制的新方法,以实现生物激发二氧化硅的可持续制造

IF 4.3 Q2 ENGINEERING, CHEMICAL
Yahaya D. Baba, Mauro Chiacchia and Siddharth V. Patwardhan*, 
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引用次数: 2

摘要

生物启发二氧化硅(BIS)近年来因其绿色合成而受到无与伦比的关注,它提供了一种可扩展、可持续和经济的方法来生产高价值的二氧化硅,用于广泛的应用,包括催化、环境修复、生物医学和储能。为了扩大BIS合成的规模,了解混合如何影响不同规模的反应至关重要。特别是,如果在不同的生产规模上测量、建模并保持恒定的混合时间,就可以成功地扩大规模。为此,开发了一种新的图像分析技术,将pH作为关键参数之一,用于监测反应和混合。具体而言,该技术涉及使用自定义编写的算法对颜色(pH)变化进行图像分析,以生成详细的pH图。通过对不同叶轮速度和进料喷射位置的分析,确定了混合程度和混合时间。所选帧的平均pH值与使用pH校准的测量值的交叉验证证明了图像处理技术的可靠性。结果表明,生物启发二氧化硅的形成受中尺度混合控制,在较小程度上受微观混合控制。基于这项研究的新数据,混合时间相关性被发展为雷诺数的函数─第一种绿色纳米材料。此外,我们关联了混合条件对反应和产物的影响。这些结果为扩大BIS和其他纳米材料的可持续制造提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Novel Method for Understanding the Mixing Mechanisms to Enable Sustainable Manufacturing of Bioinspired Silica

A Novel Method for Understanding the Mixing Mechanisms to Enable Sustainable Manufacturing of Bioinspired Silica

Bioinspired silica (BIS) has received unmatched attention in recent times owing to its green synthesis, which offers a scalable, sustainable, and economical method to produce high-value silica for a wide range of applications, including catalysis, environmental remediation, biomedical, and energy storage. To scale-up BIS synthesis, it is critically important to understand how mixing affects the reaction at different scales. In particular, successful scale-up can be achieved if mixing time is measured, modeled, and kept constant across different production scales. To this end, a new image analysis technique was developed using pH, as one of the key parameters, to monitor the reaction and the mixing. Specifically, the technique involved image analysis of color (pH) change using a custom-written algorithm to produce a detailed pH map. The degree of mixing and mixing time were determined from this analysis for different impeller speeds and feed injection locations. Cross validation of the mean pH of selected frames with measurements using a pH calibration demonstrated the reliability of the image processing technique. The results suggest that the bioinspired silica formation is controlled by meso- and, to a lesser extent, micromixing. Based on the new data from this investigation, a mixing time correlation is developed as a function of Reynolds number─the first of a kind for green nanomaterials. Further, we correlated the effects of mixing conditions on the reaction and the product. These results provide valuable insights into the scale-up to enable sustainable manufacturing of BIS and other nanomaterials.

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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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