Machine learning assisted fast prediction of inertial lift in microchannels

A universal inverse design methodology for microfluidic mixers

The intelligent design of microfluidic mixers encompasses both the automation of predicting fluid performance and the structural design of mixers. This article delves into the technical trajectory of computer-aided design for micromixers, leveraging artificial intelligence algorithms. We propose an automated micromixer design methodology rooted in cost-effective artificial neural network (ANN) models paired with inverse design algorithms. Initially, we introduce two inverse design methods for micromixers: one that combines ANN with multi-objective genetic algorithms, and another that fuses ANN with particle swarm optimization algorithms. Subsequently, using two benchmark micromixers as case studies, we demonstrate the automatic derivation of micromixer structural parameters. Finally, we automatically design and optimize 50 sets of micromixer structures using the proposed algorithms. The design accuracy is further enhanced by analyzing the inverse design algorithm from a statistical standpoint.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Generation of Multiple Concentration Gradients Using a Two-Dimensional Pyramid Array

Concentration heterogeneity of diffusible reactants is a prevalent phenomenon in biochemical processes, requiring the generation of concentration gradients for the relevant experiments. In this study, we present a high-density pyramid array microfluidic network for the effective and precise generation of multiple concentration gradients. The complex gradient distribution in the 2D array can be adaptively adjusted by modulating the reactant velocities and concentrations at the inlets. In addition, the unique design of each reaction chamber and mixing block in the array ensures uniform concentrations within each chamber during dynamic changes, enabling large-scale reactions with low reactant volumes. Through detailed numerical simulation of mass transport within the complex microchannel networks, the proposed method allows researchers to determine the desired number of reaction chambers within a given concentration range based on experimental requirements and to quickly obtain the operating conditions with the help of machine learning-based prediction. The effectiveness in generating a multiple concentration gradient environment was further demonstrated by concentration-dependent calcium carbonate crystallization experiments. This device provides a highly efficient mixing and adaptable concentration platform that is well suited for high-throughput and multiplexed reactions.

Sheathless inertial particle focusing methods within microfluidic devices: a review

The ability to manipulate and focus particles within microscale fluidic environments is crucial to advancing biological, chemical, and medical research. Precise and high-throughput particle focusing is an essential prerequisite for various applications, including cell counting, biomolecular detection, sample sorting, and enhancement of biosensor functionalities. Active and sheath-assisted focusing techniques offer accuracy but necessitate the introduction of external energy fields or additional sheath flows. In contrast, passive focusing methods exploit the inherent fluid dynamics in achieving high-throughput focusing without external actuation. This review analyzes the latest developments in strategies of sheathless inertial focusing, emphasizing inertial and elasto-inertial microfluidic focusing techniques from the channel structure classifications. These methodologies will serve as pivotal benchmarks for the broader application of microfluidic focusing technologies in biological sample manipulation. Then, prospects for future development are also predicted. This paper will assist in the understanding of the design of microfluidic particle focusing devices.

Unraveling the motion and deformation characteristics of red blood cells in a deterministic lateral displacement device

Deterministic Lateral Displacement (DLD) device has gained widespread recognition and trusted for filtering blood cells. However, there remains a crucial need to explore the complex interplay between deformable cells and flow within the DLD device to improve its design. This paper presents an approach utilizing a mesoscopic cell-level numerical model based on dissipative particle dynamics to effectively capture this complex phenomenon. To establish the model's credibility, a series of numerical simulations were conducted and the numerical results were validated with nominal experimental data from the literature. These include single cell stretching experiment, comparisons of the morphological characteristics of cells in DLD, and comparison the specific row-shift fraction of DLD required to initiate the zigzag mode. Additionally, we investigate the effect of cell rigidity, which serves as an indicator of cell health, on average flow velocity, trajectory, and asphericity. Moreover, we extend the existing theory of predicting zigzag mode for solid spherical particles to encompass the behavior of red blood cells. To achieve this, we introduce a new concept of effective diameter and demonstrate its applicability in providing highly accurate predictions across a wide range of conditions.

A general neural network model co-driven by mechanism and data for the reliable design of gas-liquid T-junction microdevices

In recent years, many models have been developed to describe the gas-liquid microdispersion process, which mainly rely on mechanistic analysis and may not be universally applicable. In order to provide a more comprehensive model and, most significantly, to provide a model for design, we have established a general database of microbubble generation in T-junction microdevices, including 854 data points from 12 pieces of literature. A neural network model that combines mechanistic and data modeling is developed. By transfer learning, more accurate results can be obtained. Additionally, we have proposed a design method that enables a relative deviation of less than 5% from the expected bubble size. A new device was designed and prepared to confirm the reliability of the method, which can prepare smaller bubbles than other common T-junction devices. In this way, a general and universal database and model are established and a design method for a gas-liquid T-junction microreactor is developed.

Numerical study of bacteria removal from microalgae solution using an asymmetric contraction-expansion microfluidic device: A parametric analysis approach

Microalgae have been remarkably taken into account due to their wide applications in the biopharmaceutical, nutraceutical and bio-energy fields. However, contamination of microalgae with bacteria still appears to be a concern, adversely impacting products' quality and process efficiency. Microalgae decontamination with conventional techniques is usually expensive and time-consuming. Moreover, damage to microalgae cells is highly possible. Asymmetric contraction-expansion microchannels (Asym-CEMCs) are promising passive microfluidic devices that can overcome conventional techniques' drawbacks with their standing-out features. However, the flexibility of Asym-CEMCs performance arising from their various tunable geometrical parameters results in the fact that their performance for separating a target particle cannot be predicted without an investigation. In this work, for the first time, Asym-CEMCs were numerically studied for the removal of a very conventional bacteria, B. subtilis (1 μm), from one of the most popular microalgae, C. vulgaris (5.7 μm). The influences of the microchannel aspect ratio, length and width ratios of the expansion-to-contraction zones, and the total flow rate on the separation resolution and focusing width of the particles were investigated by a 3D numerical model. The aspect ratio had the strongest influence on the Asym-CEMC performance, however, the length ratio had no considerable effect on the results. A decrease in the aspect ratio augmented the shear-induced lift force and Dean drag force, leading to a significant separation resolution improvement. Microalgae decontamination was also enhanced by an increase in the total flow rate and expansion-to-contraction width ratio. Finally, a locally optimized Asym-CEMC with an aspect ratio of one and expansion-to-contraction width and length ratios of 4.7 and 2.07, respectively, was proposed, leading to complete microalgae decontamination with a high normalized separation resolution of 0.6. In a word, Asym-CEMCs with tailored dimensions are promising for successfully decontaminating microalgae from bacteria.

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.

Inertial microfluidics: current status, challenges, and future opportunities

Inertial microfluidics uses the hydrodynamic effects induced at finite Reynolds numbers to achieve passive manipulation of particles, cells, or fluids and offers the advantages of high-throughput processing, simple channel geometry, and label-free and external field-free operation. Since its proposal in 2007, inertial microfluidics has attracted increasing interest and is currently widely employed as an important sample preparation protocol for single-cell detection and analysis. Although great success has been achieved in the inertial microfluidics field, its performance and outcome can be further improved. From this perspective, herein, we reviewed the current status, challenges, and opportunities of inertial microfluidics concerning the underlying physical mechanisms, available simulation tools, channel innovation, multistage, multiplexing, or multifunction integration, rapid prototyping, and commercial instrument development. With an improved understanding of the physical mechanisms and the development of novel channels, integration strategies, and commercial instruments, improved inertial microfluidic platforms may represent a new foundation for advancing biomedical research and disease diagnosis.

A machine learning-based framework to design capillary-driven networks

We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specifically developed to generate the necessary data to train the ML-GA. This 1D model was validated using analytical results issued from a Y-shaped capillary network and experimental data. For a given microfluidic network, we defined the objective of the ML-GA to obtain the set of geometric parameters that produces the closest matching results against two prescribed curves of delivered volume against time. We performed more than 20 generations of 10 000 simulations to train the ML-GA and achieved the optimal solution of the inverse design problem. The optimisation took less than 6 hours, and the results were successfully validated using experimental data. This work establishes the utility of the presented method for the fast and reliable design of complex capillary-driven devices, enabling users to optimise their designs via an easy-to-use 1D numerical tool and machine learning technique.

A numerical lift force analysis on the inertial migration of a deformable droplet in steady and oscillatory microchannel flows

Inertial migration of deformable particles has become appealing in recent years due to its numerous applications in microfluidics and biomedicine. The physics underlying the motion of these particles is contingent upon the presence of lift forces in microchannels. Therefore, in this work, we present a lift force analysis for such migration of a deformable droplet in steady and oscillatory flow regimes and identify the effects of varying Capillary number and oscillation frequency on its dynamics. We then propose an expression that mimics the lift force behavior in oscillatory flows accurately. Finally, we introduce a procedure to derive and predict a simple expression for the steady and averaged oscillatory lift for any given combination of Capillary number and oscillation frequency within a continuous range.

Machine learning for microfluidic design and control

Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.

Geometric structure design of passive label-free microfluidic systems for biological micro-object separation

Passive and label-free microfluidic devices have no complex external accessories or detection-interfering label particles. These devices are now widely used in medical and bioresearch applications, including cell focusing and cell separation. Geometric structure plays the most essential role when designing a passive and label-free microfluidic chip. An exquisitely designed geometric structure can change particle trajectories and improve chip performance. However, the geometric design principles of passive and label-free microfluidics have not been comprehensively acknowledged. Here, we review the geometric innovations of several microfluidic schemes, including deterministic lateral displacement (DLD), inertial microfluidics (IMF), and viscoelastic microfluidics (VEM), and summarize the most creative innovations and design principles of passive and label-free microfluidics. We aim to provide a guideline for researchers who have an interest in geometric innovations of passive label-free microfluidics.