Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research

Abstract

Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.

Keywords: accelerated discovery; advanced integrated microreactor development platform; artificial intelligence; flow chemistry; machine learning; microreactor.

Figure 1

This is a general framework of accelerated discovery campaigns consisting of the creation of a knowledge base, the construction of an inference model, the generation and experimental validation of a hypothesis, and the feedback for the next search cycle.

Figure 2

Material discovery with integrated microreactor development platform.

Figure 3

Four 3D printing configurations. (a,b) stereo-lithography apparatus type 3D printer and digital light projection type 3D printer. (c,d) multiple-jet modeling type 3D printer and fused deposition modeling type 3D printer.

Figure 4

Current manual (a) and proposed semi-automated (b) workflows for mVLSI LoC design. CAD algorithms can augment the semi-automated workflow, accelerating the design process without replacing it entirely.

Figure 5

Flow system setup and segment preparation. (A) Schematic depiction of the flow system. (B) Segment preparation and injection into flow stream showing potential mixing and diffusion outcomes, (B1) idealized–no diffusion, (B2) no mixing, and (B3) observed. (C) Portrayal of UV trace of the emerging reactions’ segments and fractionation into alternating LC-MS units.

Figure 6

Multistage microfluidic platform for the synthesis of InP/ZnS core/shell QDs. The first three stages (mixing, aging, and sequential growth reactors) are used for the synthesis of InP cores and the following three stages (two shell formation reactors and one annealing reactor) for the synthesis core/shell morphologies.

Figure 7

A representation of how linear gradient flow ramps can be utilized to sample with a high data density on the initial curvature of the kinetic plot.

Figure 8

Typical components used in the assembly of continuous flows.

Figure 9

Workflow for IDE and device discovery agent. On Insight Monitoring [32,33,34], Outside Monitoring [28,35,36,37,38], Rapid proto-typing [26,27,28,29], and Inference model [39,40,41,42,43,44,45,46,47,48,49] are cited.