Engineering advancements in microfluidic systems for enhanced mixing at low Reynolds numbers

Mixing within micro- and millichannels is a pivotal element across various applications, ranging from chemical synthesis to biomedical diagnostics and environmental monitoring. The inherent low Reynolds number flow in these channels often results in a parabolic velocity profile, leading to a broad residence time distribution. Achieving efficient mixing at such small scales presents unique challenges and opportunities. This review encompasses various techniques and strategies to evaluate and enhance mixing efficiency in these confined environments. It explores the significance of mixing in micro- and millichannels, highlighting its relevance for enhanced reaction kinetics, homogeneity in mixed fluids, and analytical accuracy. We discuss various mixing methodologies that have been employed to get a narrower residence time distribution. The role of channel geometry, flow conditions, and mixing mechanisms in influencing the mixing performance are also discussed. Various emerging technologies and advancements in microfluidic devices and tools specifically designed to enhance mixing efficiency are highlighted. We emphasize the potential applications of micro- and millichannels in fields of nanoparticle synthesis, which can be utilized for biological applications. Additionally, the prospects of machine learning and artificial intelligence are offered toward incorporating better mixing to achieve precise control over nanoparticle synthesis, ultimately enhancing the potential for applications in these miniature fluidic systems.

Microkinetic insights into the role of catalyst and water activity on the nucleation, growth, and dissolution during COF-5 synthesis

The chemical pathway for synthesizing covalent organic frameworks (COFs) involves a complex medley of reaction sequences over a rippling energy landscape that cannot be adequately described using existing theories. Even with the development of state-of-the-art experimental and computational tools, identifying primary mechanisms of nucleation and growth of COFs remains elusive. Other than empirically, little is known about how the catalyst composition and water activity affect the kinetics of the reaction pathway. Here, for the first time, we employ time-resolved in situ Fourier transform infrared spectroscopy (FT-IR) coupled with a six-parameter microkinetic model consisting of ∼10 million reactions and over 20 000 species. The integrated approach elucidates previously unrecognized roles of catalyst pK_a on COF yield and water on growth rate and size distribution. COF crystalline yield increases with decreasing pK_a of the catalysts, whereas the effect of water is to reduce the growth rate of COF and broaden the size distribution. The microkinetic model reproduces the experimental data and quantitatively predicts the role of synthesis conditions such as temperature, catalyst, and precursor concentration on the nucleation and growth rates. Furthermore, the model also validates the second-order reaction mechanism of COF-5 and predicts the activation barriers for classical and non-classical growth of COF-5 crystals. The microkinetic model developed here is generalizable to different COFs and other multicomponent systems.

Selective desolvation in two-step nucleation mechanism steers crystal structure formation

The two-step nucleation (TSN) theory and crystal structure prediction (CSP) techniques are two disjointed yet popular methods to predict nucleation rate and crystal structure, respectively. The TSN theory is a well-established mechanism to describe the nucleation of a wide range of crystalline materials in different solvents. However, it has never been expanded to predict the crystal structure or polymorphism. On the contrary, the existing CSP techniques only empirically account for the solvent effects. As a result, the TSN theory and CSP techniques continue to evolve as separate methods to predict two essential attributes of nucleation - rate and structure. Here we bridge this gap and show for the first time how a crystal structure is formed within the framework of TSN theory. A sequential desolvation mechanism is proposed in TSN, where the first step involves partial desolvation to form dense clusters followed by selective desolvation of functional groups directing the formation of crystal structure. We investigate the effect of the specific interaction on the degree of solvation around different functional groups of glutamic acid molecules using molecular simulations. The simulated energy landscape and activation barriers at increasing supersaturations suggest sequential and selective desolvation. We validate computationally and experimentally that the crystal structure formation and polymorph selection are due to a previously unrecognized consequence of supersaturation-driven asymmetric desolvation of molecules.