Pushing Nanomaterials past the Kilogram Scale—a Targeted Approach Integrating Scalable Microreactors, Machine Learning and High-Throughput Analysis

Abstract

Novel materials are the backbone of major technological advances. However, the development and wide-scale introduction of new materials, such as nanomaterials, is limited by three main factors—the expense of experiments, inefficiency of synthesis methods and complexity of scale-up. Reaching the kilogram scale is a hurdle that takes years of effort for many nanomaterials. We introduce an improved methodology for materials development, combining state-of-the-art techniques—multi-objective machine learning optimization, high yield microreactors and high throughput analysis. We demonstrate this approach by efficiently developing a kg per day reaction process for highly active antibacterial ZnO nanoparticles. The proposed method has the potential to significantly reduce experimental costs, increase process efficiency and enhance material performance, which culminate to form a new pathway for materials discovery.

Consideration for the scale‐up manufacture of nanotherapeutics—A critical step for technology transfer

“…We have discussed the roles of multiparameters during nanoparticle synthesis, which may require an exhaustive amount of experimentation and/or empirical design to understand how the multivariable nano-CMC impacts the final products. With the rapid development of artificial intelligence (AI), it was possible to use computational approach, including machine learning, to determine the optimal engineering parameters [42][43][44] (Figure 4). Moreover, it is advantageous to consider the orthogonal design of experiments (DOE), which ensures that all the parameters of interest may be estimated without the need to experimentally explore every single possibility.…”

“…[46] In the case of liposome scale-up production, through careful controls on microfluidics channel design, flow rate, feed ratio, concentration and temperature, it was possible to utilize laminar flow and tunable mixing for both lab scale and industrial development, which is more efficient than the traditional methods such as thin-film hydration and reverse-phase evaporation (Figure 5). [47] Microfluidic systems, which could be highly advantageous [44] Copyright 2020, chemrxiv.org F I G U R E 5 Microfluidic system for translational nanoparticle scale-up synthesis. (A) Design and control of microfluidics system to make high quality nanoparticles for nanomedicine.…”