A Microfluidic Device for Automated High Throughput Detection of Ice Nucleation of Snomax®

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

A highly active mineral-based ice nucleating agent supports in situ cell cryopreservation in a high throughput format

Cryopreservation of biological matter in microlitre scale volumes of liquid would be useful for a range of applications. At present, it is challenging because small volumes of water tend to supercool, and deep supercooling is known to lead to poor post-thaw cell viability. Here, we show that a mineral ice nucleator can almost eliminate supercooling in 100 µl liquid volumes during cryopreservation. This strategy of eliminating supercooling greatly enhances cell viability relative to cryopreservation protocols with uncontrolled ice nucleation. Using infrared thermography, we demonstrate a direct relationship between the extent of supercooling and post-thaw cell viability. Using a mineral nucleator delivery system, we open the door to the routine cryopreservation of mammalian cells in multiwell plates for applications such as high throughput toxicology testing of pharmaceutical products and regenerative medicine.

Measurements of Static and Dynamic Bubble Surface Tension Using a Deformation-Based Microfluidic Tensiometer

The surface tension of bubbles is critical for processes involving mixed liquid-gas systems, from sea spray aerosol generation to firefighting foam aspiration. In particular, the size- and surfactant-dependent time scales of dynamic surface tension decay due to adsorption of surface-active chemicals at the curved interface significantly dictate the multiphase system dynamics. While size-dependent surfactant adsorption and interfacial dynamics have been well characterized for liquid-liquid systems using microfluidic platforms, application of microfluidic methods to liquid-gas systems has received less attention. This work uses a high-throughput microfluidic tensiometer to measure the static and dynamic surface tension of microscale bubbles compared with millimeter bubbles characterized by pendant drop. It is shown that the static surface tension measurements for surfactant-free interfaces with microfluidics show good agreement with pendant drop for most systems. At the same time, its accuracy can be affected by bubble pressure, inertia force at high Re, drag force, bubble expansion, and image processing limitation. In the presence of surfactants, the dynamic surface tension measurements show that both smaller bubbles and higher surfactant concentrations can lead to a much shorter time to reach equilibrium compared with pendant drop, similar to the observation for liquid-liquid interfaces. This work shows the potential of a microfluidic tensiometer to capture early time surface tension decay and accurately measure surface tension even in the presence of Marangoni stress tangential to the interface.

Exploiting machine learning for bestowing intelligence to microfluidics

Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.