Machine learning for microfluidic design and control

Enhancing Analysis of Extracellular Vesicles by Microfluidics

Extracellular vesicles (EVs) play crucial roles in intercellular communication and hold great promise as biomarkers for noninvasive disease diagnosis. Intensive research efforts have been devoted to discovering the EV subpopulations responsible for specific functions or with enhanced effectiveness as disease markers, through extensive EV purification and content analysis. However, their high heterogeneity in size and cargo composition poses significant challenges for reaching such goals. Isolation methods like ultracentrifugation and size-exclusion chromatography, as well as content analysis approaches like polymerase chain reaction and enzyme-linked immunosorbent assay, have made significant contributions to improving our understanding of EV biology. Nonetheless, these methods face limitations in isolation efficiency, EV purity, and detection sensitivity and specificity due to issues like large sample consumption, unsatisfactory purity, and insufficient resolution in EV subtyping. Microfluidic technology presents promising solutions to these challenges, leveraging their intrinsic capabilities in precise flow and external energy field manipulation, sample compartmentalization, and signal enhancement at the micro- and nanoscale. Hence, this review summarizes the recent developments in microfluidics-enabled EV analysis, paying special attention to the unique microfluidic features exploited. Strategies such as viscoelastic and inertial flow, fluid mixing, and external-field-assisted approaches in improving EV purification, as well as compartmentalization and micro/nanostructures for enhancing EV detection, are examined. Furthermore, the current limitations and potential future directions are discussed to inspire advancements in this rapidly developing field.

Advancements in Circulating Tumor Cell Detection for Early Cancer Diagnosis: An Integration of Machine Learning Algorithms with Microfluidic Technologies

Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant challenges for detection. Microfluidic systems, or "lab-on-a-chip" devices, have emerged as a revolutionary tool in liquid biopsy, enabling efficient isolation and analysis of CTCs. These systems offer advantages such as reduced sample volume, enhanced sensitivity, and the ability to integrate multiple processes into a single platform. Several microfluidic techniques, including size-based filtration, dielectrophoresis, and immunoaffinity capture, have been developed to enhance CTC detection. The integration of machine learning (ML) with microfluidic systems has further improved the specificity and accuracy of CTC detection, significantly advancing the speed and efficiency of early cancer diagnosis. ML models have enabled more precise analysis of CTCs by automating detection processes and enhancing the ability to identify rare and heterogeneous cell populations. These advancements have already demonstrated their potential in improving diagnostic accuracy and enabling more personalized treatment approaches. In this review, we highlight the latest progress in the integration of microfluidic technologies and ML algorithms, emphasizing how their combination has changed early cancer diagnosis and contributed to significant advancements in this field.

Dimensional analysis meets AI for non-Newtonian droplet generation

Non-Newtonian droplets are used across various applications, including pharmaceuticals, food processing, drug delivery and material science. However, predicting droplet formation using such complex fluids is challenging due to the intricate multiphase interactions between fluids with varying viscosities, elastic properties and geometrical constraints. In this study, we introduce a novel hybrid machine-learning architecture that integrates dimensional analysis with machine learning to predict the flow rates required to generate droplets with specified sizes in systems involving non-Newtonian fluids. Unlike previous approaches, our model is designed to accommodate shear-rate-dependent viscosities and a simple estimate of the elastic properties of the fluids. It provides accurate predictions of the dispersed and continuous phases flow rates for given droplet length, height, and viscosity curves, even when the fluid properties deviate from those used during training. Our model demonstrates strong predictive power, achieving R2 values of up to 0.82 for unseen data. The significance of our work lies in its ability to generalize across a broad range of non-Newtonian systems having different viscosity curves, offering a powerful tool for optimizing droplet generation. This model represents a significant advancement in the application of machine learning to microfluidics, providing new opportunities for efficient experimental design in complex multiphase systems.

Biosensor Technologies for Water Quality: Detection of Emerging Contaminants and Pathogens

This review explores the development, technological foundations, and applications of biosensor technologies across various fields, such as medicine for disease diagnosis and monitoring, and the food industry. However, the primary focus is on their use in detecting contaminants and pathogens, as well as in environmental monitoring for water quality assessment. The review classifies different types of biosensors based on their bioreceptor and transducer, highlighting how they are specifically designed for the detection of emerging contaminants (ECs) and pathogens in water. Key innovations in this technology are critically examined, including advanced techniques such as systematic evolution of ligands by exponential enrichment (SELEX), molecularly imprinted polymers (MIPs), and self-assembled monolayers (SAMs), which enable the fabrication of sensors with improved sensitivity and selectivity. Additionally, the integration of microfluidic systems into biosensors is analyzed, demonstrating significant enhancements in performance and detection speed. Through these advancements, this work emphasizes the fundamental role of biosensors as key tools for safeguarding public health and preserving environmental integrity.

Integrating impedance cytometry with other microfluidic tools towards multifunctional single-cell analysis platforms

Microfluidic impedance cytometry (MIC) is a label-free technique that characterizes individual flowing particles/cells based on their interaction with a multifrequency electric field. The technique has been successfully applied in different scenarios including life-science research, diagnostics, and environmental monitoring. The aim of this review is to illustrate the fascinating opportunities enabled by the integration of MIC with other microfluidic tools. Specifically, we identify five categories according to their synergistic advantage: (i) improving the multiparametric characterization capability, (ii) enabling on-chip sample preparation steps, (iii) stimulating the sample, (iv) sample carrying/confinement, and (v) impedance-activated sample sorting. We discuss examples from each category, highlighting integration challenges and promising perspectives for next-generation multifunctional systems.

The Latest Advances in Microfluidic DLD Cell Sorting Technology: The Optimization of Channel Design

Cell sorting plays a crucial role in both medical and biological research. As a key passive sorting technique in the field of microfluidics, deterministic lateral displacement (DLD) has been widely applied to cell separation and sorting. This review aims to summarize the latest advances in the optimization of channel design for microfluidic DLD cell sorting. First, we provide an overview of the design elements of microfluidic DLD cell sorting channels, focusing on key factors that affect separation efficiency and accuracy, including channel geometry, fluid dynamics, and the interaction between cells and channel surfaces. Subsequently, we review recent innovations and progress in channel design for microfluidic DLD technology, exploring its applications in biomedical fields and its integration with machine learning. Additionally, we discuss the challenges currently faced in optimizing channel design for microfluidic DLD cell sorting. Finally, based on existing research, we make a summary and put forward prospective views on the further development of this field.

Advancing microfluidic design with machine learning: a Bayesian optimization approach

Microfluidic technology, which involves the manipulation of fluids in microchannels, faces challenges in channel design and performance optimization due to its complex, multi-parameter nature. Traditional design and optimization approaches usually rely on time-consuming numerical simulations, or on trial-and-error methods, which entail high costs associated with experimental evaluations. Additionally, commonly used optimization methods require many numerical simulations, and to avoid excessive computation time, they approximate simulation results with faster surrogate models. Alternatively, machine learning (ML) is becoming increasingly significant in microfluidics and technology in general, enabling advancements in data analysis, automation, and system optimization. Among ML methods, Bayesian optimization (BO) stands out by systematically exploring the design space, usually using Gaussian processes (GP) to model the objective function and guide the search for optimal designs. In this paper, we demonstrate the application of BO in the design optimization of the microfluidic systems, by enhancing the mixing performance of a micromixer with parallelogram barriers and a Tesla micromixer modified with parallelogram barriers. Micromixer models were made using Comsol Multiphysics software® and their geometric parameters were optimized using BO. The presented approach minimizes the number of required simulations to reach the optimal design, thus eliminating the need for developing a separate surrogate model for approximation of the simulation results. The results showed the effectiveness of using BO for design optimization, both in terms of the execution speed and reaching the optimum of the objective function. The optimal geometries for efficient mixing were achieved at least an order of magnitude faster compared to state-of-the-art optimization methods for microfluidic design. In addition, the presented approach can be widely applied to other microfluidic devices, such as droplet generators, particle separators, etc.

Intelligent Microfluidics for Plasma Separation: Integrating Computational Fluid Dynamics and Machine Learning for Optimized Microchannel Design

Efficient separation of blood plasma and Packed Cell Volume (PCV) is vital for rapid blood sensing and early disease detection, especially in point-of-care and resource-limited environments. Conventional centrifugation methods for separation are resource-intensive, time-consuming, and off-chip, necessitating innovative alternatives. This study introduces "Intelligent Microfluidics", an ML-integrated microfluidic platform designed to optimize plasma separation through computational fluid dynamics (CFD) simulations. The trifurcation microchannel, modeled using COMSOL Multiphysics, achieved plasma yields of 90-95% across varying inflow velocities (0.0001-0.05 m/s). The input fluid parameters mimic the blood viscosity and density used with appropriate boundary conditions and fluid dynamics to optimize the designed microchannels. Eight supervised ML algorithms, including Artificial Neural Networks (ANN) and k-Nearest Neighbors (KNN), were employed to predict key performance parameters, with ANN achieving the highest predictive accuracy (R2 = 0.97). Unlike traditional methods, this platform demonstrates scalability, portability, and rapid diagnostic potential, revolutionizing clinical workflows by enabling efficient plasma separation for real-time, point-of-care diagnostics. By incorporating a detailed comparative analysis with previous studies, including computational efficiency, our work underscores the superior performance of ML-enhanced microfluidic systems. The platform's robust and adaptable design is particularly promising for healthcare applications in remote or resource-constrained settings where rapid and reliable diagnostic tools are urgently needed. This novel approach establishes a foundation for developing next-generation, portable diagnostic technologies tailored to clinical demands.

Component library creation and pixel array generation with micromilled droplet microfluidics

Droplet microfluidics enable high-throughput screening, sequencing, and formulation of biological and chemical systems at the microscale. Such devices are generally fabricated in a soft polymer such as polydimethylsiloxane (PDMS). However, developing design masks for PDMS devices can be a slow and expensive process, requiring an internal cleanroom facility or using an external vendor. Here, we present the first complete droplet-based component library using low-cost rapid prototyping and electrode integration. This fabrication method for droplet microfluidic devices costs less than $12 per device and a full design-build-test cycle can be completed within a day. Discrete microfluidic components for droplet generation, re-injection, picoinjection, anchoring, fluorescence sensing, and sorting were built and characterized. These devices are biocompatible, low-cost, and high-throughput. To show its ability to perform multistep workflows, these components were used to assemble droplet "pixel" arrays, where droplets were generated, sensed, sorted, and anchored onto a grid to produce images.

A Generative Adversarial Network Approach to Predict Nanoparticle Size in Microfluidics

To achieve precise control over the properties and performance of nanoparticles (NPs) in a microfluidic setting, a profound understanding of the influential parameters governing the NP size is crucial. This study specifically delves into poly(lactic-co-glycolic acid) (PLGA)-based NPs synthesized through microfluidics that have been extensively explored as drug delivery systems (DDS). A comprehensive database, containing more than 11 hundred data points, is curated through an extensive literature review, identifying potential effective features. Initially, we employed a tabular generative adversarial network (TGAN) to enhance data sets, increasing the reliability of the obtained results and elevating prediction accuracy. Subsequently, NP size prediction was performed using different machine learning (ML) techniques including decision tree (DT), random forest (RF), deep neural networks (DNN), linear regression (LR), support vector regression (SVR), and gradient boosting (GB). Among these ensembles, DT emerges as the most accurate algorithm, yielding an average prediction error of 8%. Further simulations underscore the pivotal role of the synthesis method, poly(vinyl alcohol) (PVA) concentration, and lactide-to-glycolide (LA/GA) ratio of PLGA copolymers as the primary determinants influencing NP size.

Microfluidic Nanoparticle Separation for Precision Medicine

A deeper understanding of disease heterogeneity highlights the urgent need for precision medicine. Microfluidics, with its unique advantages, such as high adjustability, diverse material selection, low cost, high processing efficiency, and minimal sample requirements, presents an ideal platform for precision medicine applications. As nanoparticles, both of biological origin and for therapeutic purposes, become increasingly important in precision medicine, microfluidic nanoparticle separation proves particularly advantageous for handling valuable samples in personalized medicine. This technology not only enhances detection, diagnosis, monitoring, and treatment accuracy, but also reduces invasiveness in medical procedures. This review summarizes the fundamentals of microfluidic nanoparticle separation techniques for precision medicine, starting with an examination of nanoparticle properties essential for separation and the core principles that guide various microfluidic methods. It then explores passive, active, and hybrid separation techniques, detailing their principles, structures, and applications. Furthermore, the review highlights their contributions to advancements in liquid biopsy and nanomedicine. Finally, it addresses existing challenges and envisions future development spurred by emerging technologies such as advanced materials science, 3D printing, and artificial intelligence. These interdisciplinary collaborations are anticipated to propel the platformization of microfluidic separation techniques, significantly expanding their potential in precision medicine.

Machine Learning-Driven Innovations in Microfluidics

Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making. Recent advances in health diagnostics, environmental monitoring, and synthetic biology driven by ML are critically examined. This review highlights the transformative potential of ML-enhanced microfluidic systems, offering insights into the future trajectory of this rapidly evolving field.

Data-driven models for microfluidics: A short review

Microfluidic devices have many unique practical applications across a wide range of fields, making it important to develop accurate models of these devices, and many different models have been developed. Existing modeling methods mainly include mechanism derivation and semi-empirical correlations, but both are not universally applicable. In order to achieve a more accurate and general modeling process, the use of data-driven modeling has been studied recently. This review highlights recent advances in the application of data-driven modeling techniques for simulating and designing microfluidic devices. First, it introduces the application of traditional modeling approaches in microfluidics; subsequently, through different database sources, it reviews studies on data-driven modeling in three categories; and finally, it raises some open issues that require further investigation.

AI for biofabrication

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.

Revolutionizing biocatalysis: A review on innovative design and applications of enzyme-immobilized microfluidic devices

Integrating microfluidic devices and enzymatic processes in biocatalysis is a rapidly advancing field with promising applications. This review explores various facets, including applications, scalability, techno-commercial implications, and environmental consequences. Enzyme-embedded microfluidic devices offer advantages such as compact dimensions, rapid heat transfer, and minimal reagent consumption, especially in pharmaceutical optically pure compound synthesis. Addressing scalability challenges involves strategies for uniform flow distribution and consistent residence time. Incorporation with downstream processing and biocatalytic reactions makes the overall process environmentally friendly. The review navigates challenges related to reaction kinetics, cofactor recycling, and techno-commercial aspects, highlighting cost-effectiveness, safety enhancements, and reduced energy consumption. The potential for automation and commercial-grade infrastructure is discussed, considering initial investments and long-term savings. The incorporation of machine learning in enzyme-embedded microfluidic devices advocates a blend of experimental and in-silico methods for optimization. This comprehensive review examines the advancements and challenges associated with these devices, focusing on their integration with enzyme immobilization techniques, the optimization of process parameters, and the techno-commercial considerations crucial for their widespread implementation. Furthermore, this review offers novel insights into strategies for overcoming limitations such as design complexities, laminar flow challenges, enzyme loading optimization, catalyst fouling, and multi-enzyme immobilization, highlighting the potential for sustainable and efficient enzymatic processes in various industries.

Integrating machine learning and biosensors in microfluidic devices: A review

Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research to Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvironments, however, introduces the necessity of locally controlling the variables involved in the phenomena under investigation. For this reason, the literature has deeply explored the possibility of introducing sensing elements to investigate the physical quantities and the biochemical concentration inside microfluidic devices. Biosensors, particularly, are well known for their high accuracy, selectivity, and responsiveness. However, their signals could be challenging to interpret and must be carefully analysed to carry out the correct information. In addition, proper data analysis has been demonstrated even to increase biosensors' mentioned qualities. To this regard, machine learning algorithms are undoubtedly among the most suitable approaches to undertake this job, automatically learning from data and highlighting biosensor signals' characteristics at best. Interestingly, it was also demonstrated to benefit microfluidic devices themselves, in a new paradigm that the literature is starting to name "intelligent microfluidics", ideally closing this benefic interaction among these disciplines. This review aims to demonstrate the advantages of the triad paradigm microfluidics-biosensors-machine learning, which is still little used but has a great perspective. After briefly describing the single entities, the different sections will demonstrate the benefits of the dual interactions, highlighting the applications where the reviewed triad paradigm was employed.

Artificial intelligence performance in testing microfluidics for point-of-care

Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care (POC) diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the random forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (>0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.

Microfluidic technologies for lipid vesicle generation

Encapsulating biological and non-biological materials in lipid vesicles presents significant potential in both industrial and academic settings. When smaller than 100 nm, lipid vesicles and lipid nanoparticles are ideal vehicles for drug delivery, facilitating the delivery of payloads, improving pharmacokinetics, and reducing the off-target effects of therapeutics. When larger than 1 μm, vesicles are useful as model membranes for biophysical studies, as synthetic cell chassis, as bio-inspired supramolecular devices, and as the basis of protocells to explore the origin of life. As applications of lipid vesicles gain prominence in the fields of nanomedicine, biotechnology, and synthetic biology, there is a demand for advanced technologies for their controlled construction, with microfluidic methods at the forefront of these developments. Compared to conventional bulk methods, emerging microfluidic methods offer advantages such as precise size control, increased production throughput, high encapsulation efficiency, user-defined membrane properties (i.e., lipid composition, vesicular architecture, compartmentalisation, membrane asymmetry, etc.), and potential integration with lab-on-chip manipulation and analysis modules. We provide a review of microfluidic lipid vesicle generation technologies, focusing on recent advances and state-of-the-art techniques. Principal technologies are described, and key research milestones are highlighted. The advantages and limitations of each approach are evaluated, and challenges and opportunities for microfluidic engineering of lipid vesicles to underpin a new generation of therapeutics, vaccines, sensors, and bio-inspired technologies are presented.

Deep learning enabled label-free microfluidic droplet classification for single cell functional assays

Droplet-based microfluidics techniques coupled to microscopy allow for the characterization of cells at the single-cell scale. However, such techniques generate substantial amounts of data and microscopy images that must be analyzed. Droplets on these images usually need to be classified depending on the number of cells they contain. This verification, when visually carried out by the experimenter image-per-image, is time-consuming and impractical for analysis of many assays or when an assay yields many putative droplets of interest. Machine learning models have already been developed to classify cell-containing droplets within microscopy images, but not in the context of assays in which non-cellular structures are present inside the droplet in addition to cells. Here we develop a deep learning model using the neural network ResNet-50 that can be applied to functional droplet-based microfluidic assays to classify droplets according to the number of cells they contain with >90% accuracy in a very short time. This model performs high accuracy classification of droplets containing both cells with non-cellular structures and cells alone and can accommodate several different cell types, for generalization to a broader array of droplet-based microfluidics applications.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Discussion: Embracing microfluidics to advance environmental science and technology

Microfluidics, also called lab-on-a-chip, represents an emerging research platform that permits more precise and manipulation of samples at the microscale or even down to the nanoscale (nanofluidic) including picoliter droplets, microparticles, and microbes within miniaturized and highly integrated devices. This groundbreaking technology has made significant strides across multiple disciplines by providing an unprecedented view of physical, chemical, and biological events, fostering a holistic and an in-depth understanding of complex systems. The application of microfluidics to address the challenges in environmental science is likely to contribute to our better understanding, however, it's not yet fully developed. To raise researchers' interest, this discussion first delineates the valuable and underutilized environmental applications of microfluidic technology, ranging from environmental surveillance to acting as microreactors for investigating interfacial dynamic processes, and facilitating high-throughput bioassays. We highlight, with examples, how rationally designed microfluidic devices lead to new insights into the advancement of environmental science and technology. We then critically review the key challenges that hinder the practical adoption of microfluidic technologies. Specifically, we discuss the extent to which microfluidics accurately reflect realistic environmental scenarios, outline the areas to be improved, and propose strategies to overcome bottlenecks that impede the broad application of microfluidics. We also envision new opportunities and future research directions, aiming to provide guidelines for the broader utilization of microfluidics in environmental studies.

Advancing Microfluidic Immunity Testing Systems: New Trends for Microbial Pathogen Detection

Pathogenic microorganisms play a crucial role in the global disease burden due to their ability to cause various diseases and spread through multiple transmission routes. Immunity tests identify antigens related to these pathogens, thereby confirming past infections and monitoring the host's immune response. Traditional pathogen detection methods, including enzyme-linked immunosorbent assays (ELISAs) and chemiluminescent immunoassays (CLIAs), are often labor-intensive, slow, and reliant on sophisticated equipment and skilled personnel, which can be limiting in resource-poor settings. In contrast, the development of microfluidic technologies presents a promising alternative, offering automation, miniaturization, and cost efficiency. These advanced methods are poised to replace traditional assays by streamlining processes and enabling rapid, high-throughput immunity testing for pathogens. This review highlights the latest advancements in microfluidic systems designed for rapid and high-throughput immunity testing, incorporating immunosensors, single molecule arrays (Simoas), a lateral flow assay (LFA), and smartphone integration. It focuses on key pathogenic microorganisms such as SARS-CoV-2, influenza, and the ZIKA virus (ZIKV). Additionally, the review discusses the challenges, commercialization prospects, and future directions to advance microfluidic systems for infectious disease detection.

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.

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.

Design automation of microfluidic single and double emulsion droplets with machine learning

Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.

Microfluidic based continuous enzyme immobilization: A comprehensive review

Conventional techniques for enzyme immobilization suffer from suboptimal activity recovery due to insufficient enzyme loading and inadequate stability. Furthermore, these techniques are time-consuming and involve multiple steps which limit the applicability of immobilized enzymes. In contrast, the use of microfluidic devices for enzyme immobilization has garnered significant attention due to its ability to precisely control immobilization parameters, resulting in highly active immobilized enzymes. This approach offers several advantages, including reduced time and energy consumption, enhanced mass-heat transfer, and improved control over the mixing process. It maintains the superior structural configuration in immobilized form which ultimately affects the overall efficiency. The present review article comprehensively explains the design, construction, and various methods employed for enzyme immobilization using microfluidic devices. The immobilized enzymes prepared using these techniques demonstrated excellent catalytic activity, remarkable stability, and outstanding recyclability. Moreover, they have found applications in diverse areas such as biosensors, biotransformation, and bioremediation. The review article also discusses potential future developments and foresees significant challenges associated with enzyme immobilization using microfluidics, along with potential remedies. The development of this advanced technology not only paves the way for novel and innovative approaches to enzyme immobilization but also allows for the straightforward scalability of microfluidic-based techniques from an industrial standpoint.

Versatility and stability optimization of flow-focusing droplet generators via quality metric-driven design automation

Droplet generation is a fundamental component of droplet microfluidics, compartmentalizing biological or chemical systems within a water-in-oil emulsion. As adoption of droplet microfluidics expands beyond expert labs or integrated devices, quality metrics are needed to contextualize the performance capabilities, improving the reproducibility and efficiency of operation. Here, we present two quality metrics for droplet generation: performance versatility, the operating range of a single device, and stability, the distance of a single operating point from a regime change. Both metrics were characterized in silico and validated experimentally using machine learning and rapid prototyping. These metrics were integrated into a design automation workflow, DAFD 2.0, which provides users with droplet generators of a desired performance that are versatile or flow stable. Versatile droplet generators with stable operating points accelerate the development of sophisticated devices by facilitating integration of other microfluidic components and improving the accuracy of design automation tools.

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.

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable and fundamentally interesting, because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for the realization of new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc oxide superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data are limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows, which should enable the discovery of exceptional molecules and materials.

Computational Modeling as a Tool to Drive the Development of a Novel, Chemical Device for Monitoring the Injured Brain and Body

Real-time measurement of dynamic changes, occurring in the brain and other parts of the body, is useful for the detection and tracked progression of disease and injury. Chemical monitoring of such phenomena exists but is not commonplace, due to the penetrative nature of devices, the lack of continuous measurement, and the inflammatory responses that require pharmacological treatment to alleviate. Soft, flexible devices that more closely match the moduli and shape of monitored tissue and allow for surface microdialysis provide a viable alternative. Here, we show that computational modeling can be used to aid the development of such devices and highlight the considerations when developing a chemical monitoring probe in this way. These models pave the way for the development of a new class of chemical monitoring devices for monitoring neurotrauma, organs, and skin.

Organ-on-a-chip meets artificial intelligence in drug evaluation

Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

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.

Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems

A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems. The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of "on demand" treatments. In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.

Label-free microfluidic cell sorting and detection for rapid blood analysis

Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.

Closed-loop Control Systems for Pumps used in Portable Analytical Systems

The demand for accurate control of the flowrate/pressure in chemical analytical systems has given rise to the adoption of mechatronic approaches in analytical instruments. A mechatronic device is a synergistic system which combines mechanical, electronic, computer and control components. In the development of portable analytical devices, considering the instrument as a mechatronic system can be useful to mitigate compromises made to decrease space, weight, or power consumption. Fluid handling is important for reliability, however, commonly utilized platforms such as syringe and peristaltic pumps are typically characterized by flow/pressure fluctuations and slow responses. Closed loop control systems have been used effectively to decrease the difference between desired and realized fluidic output. This review discusses the way control systems have been implemented for enhanced fluidic control, categorized by pump type. Advanced control strategies used to enhance the transient and the steady state responses are discussed, along with examples of their implementation in portable analytical systems. The review is concluded with the outlook that the challenge in adequately expressing the complexity and dynamics of the fluidic network as a mathematical model has yielded a trend towards the adoption of experimentally informed models and machine learning approaches.

Viscoelastic Particle Encapsulation Using a Hyaluronic Acid Solution in a T-Junction Microfluidic Device

The encapsulation of particles and cells in droplets is highly relevant in biomedical engineering as well as in material science. So far, however, the majority of the studies in this area have focused on the encapsulation of particles or cells suspended in Newtonian liquids. We here studied the particle encapsulation phenomenon in a T-junction microfluidic device, using a non-Newtonian viscoelastic hyaluronic acid solution in phosphate buffer saline as suspending liquid for the particles. We first studied the non-Newtonian droplet formation mechanism, finding that the data for the normalised droplet length scaled as the Newtonian ones. We then performed viscoelastic encapsulation experiments, where we exploited the fact that particles self-assembled in equally-spaced structures before approaching the encapsulation area, to then identify some experimental conditions for which the single encapsulation efficiency was larger than the stochastic limit predicted by the Poisson statistics.

Integrating machine learning and digital microfluidics for screening experimental conditions

Digital microfluidics (DMF) has the signatures of an ideal liquid handling platform - as shown through almost two decades of automated biological and chemical assays. However, in the current state of DMF, we are still limited by the number of parallel biological or chemical assays that can be performed on DMF. Here, we report a new approach that leverages design-of-experiment and numerical methodologies to accelerate experimental optimization on DMF. The integration of the one-factor-at-a-time (OFAT) experimental technique with machine learning algorithms provides a set of recommended optimal conditions without the need to perform a large set of experiments. We applied our approach towards optimizing the radiochemistry synthesis yield given the large number of variables that affect the yield. We believe that this work is the first to combine such techniques which can be readily applied to any other assays that contain many parameters and levels on DMF.

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.

ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.

Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator

Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to generate an arbitrary concentration gradient. Machine-learning techniques and interpolation algorithms were implemented to help researchers instantly analyze the current concentration profile of the gradient generator with different inlet configurations. The proposed method has a 93.71% accuracy rate with a 300× acceleration effect compared to the conventional finite element analysis. In addition, our method shows the potential application of the design automation and computer-aided design of microfluidics by leveraging both artificial neural networks and computer science algorithms.