When robotics met fluidics

An artificial intelligence-assisted digital microfluidic system for multistate droplet control

Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high level of variability in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems that are informed by the transient state of droplets and their interactions. However, most related studies focus on droplet localization and shape recognition. In this study, we develop the AI-assisted DMF framework μDropAI for multistate droplet control on the basis of droplet morphology. The semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%; it enables control of droplets without user intervention. The coefficient of variation (CV) of the volumes of split droplets can be limited to 2.74%, which is lower than the CV of traditional dispensed droplets, contributing to an improvement in the precision of volume control for droplet splitting. The proposed system inspires the development of semantic-driven DMF systems that can interface with multimodal large language models (MLLMs) for fully automatic control.

Toward Analysis at the Point of Need: A Digital Microfluidic Approach to Processing Multi-Source Sexual Assault Samples

Forensic case samples collected in sexual assaults typically contain DNA from multiple sources, which complicates short-tandem repeat (STR) profiling. These samples are typically sent to a laboratory to separate the DNA from sperm and non-sperm sources prior to analysis. Here, the automation and miniaturization of these steps using digital microfluidics (DMF) is reported, which may eventually enable processing sexual assault samples outside of the laboratory, at the point of need. When applied to vaginal swab samples collected up to 12 h post-coitus (PC), the new method identifies single-source (male) STR profiles. When applied to samples collected 24-72 h PC, the method identifies mixed STR profiles, suggesting room for improvement and/or potential for data deconvolution. In sum, an automated, miniaturized sample pre-processing method for separating the DNA contained in sexual assault samples is demonstrated. This type of automated processing using DMF, especially when combined with Rapid DNA Analysis, has the potential to be used for processing of sexual assault samples in hospitals, police offices, and other locations outside of the laboratory.

Acoustohydrodynamic micromixers: Basic mixing principles, programmable mixing prospectives, and biomedical applications

Acoustohydrodynamic micromixers offer excellent mixing efficiency, cost-effectiveness, and flexible controllability compared with conventional micromixers. There are two mechanisms in acoustic micromixers: indirect influence by induced streamlines, exemplified by sharp-edge micromixers, and direct influence by acoustic waves, represented by surface acoustic wave micromixers. The former utilizes sharp-edge structures, while the latter employs acoustic wave action to affect both the fluid and its particles. However, traditional micromixers with acoustic bubbles achieve significant mixing performance and numerous programmable mixing platforms provide excellent solutions with wide applicability. This review offers a comprehensive overview of various micromixers, elucidates their underlying principles, and explores their biomedical applications. In addition, advanced programmable micromixing with impressive versatility, convenience, and ability of cross-scale operations is introduced in detail. We believe this review will benefit the researchers in the biomedical field to know the micromixers and find a suitable micromixing method for their various applications.

Towards skin-on-a-chip for screening the dermal absorption of cosmetics

Over the past few decades, there have been increasing global efforts to limit or ban the use of animals for testing cosmetic products. This ambition has been at the heart of international endeavours to develop new in vitro and animal-free approaches for assessing the safety of cosmetics. While several of these new approach methodologies (NAMs) have been approved for assessing different toxicological endpoints in the UK and across the EU, there remains an absence of animal-free methods for screening for dermal absorption; a measure that assesses the degree to which chemical substances can become systemically available through contact with human skin. Here, we identify some of the major technical barriers that have impacted regulatory recognition of an in vitro skin model for this purpose and propose how these could be overcome on-chip using artificial cells engineered from the bottom-up. As part of our future perspective, we suggest how this could be realised using a digital biomanufacturing pipeline that connects the design, microfluidic generation and 3D printing of artificial cells into user-crafted synthetic tissues. We highlight milestone achievements towards this goal, identify future challenges, and suggest how the ability to engineer animal-free skin models could have significant long-term consequences for dermal absorption screening, as well as for other applications.

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.

Multiphase displacement manipulated by micro/nanoparticle suspensions in porous media via microfluidic experiments: From interface science to multiphase flow patterns

Multiphase displacement in porous media can be adjusted by micro/nanoparticle suspensions, which is widespread in many scientific and industrial contexts. Direct visualization of suspension flow dynamics and corresponding multiphase patterns is crucial to understanding displacement mechanisms and eventually optimizing these processes in geological, biological, chemical, and other engineering systems. However, suspension flow inside the opaque realistic porous media makes direct observation challenging. The advances in microfluidic experiments have provided us with alternative methods to observe suspension influence on the interface and multiphase flow behaviors at high temporal and spatial resolutions. Macroscale processes are controlled by microscale interfacial behaviors, which are affected by multiple physical factors, such as particle adsorption, capillarity, and hydrodynamics. These properties exerted on the suspension flow in porous media may lead to interesting interfacial phenomena and new displacement consequences. As an underpinning science, understanding and controlling the suspension transport process from interface to flow patterns in porous media is critical for a lower operating cost to improve resource production while reducing harmful emissions and other environmental impacts. This review summarizes the basic properties of different micro/nanoparticle suspensions and the state-of-the-art microfluidic techniques for displacement research activities in porous media. Various suspension transport behaviors and displacement mechanisms explored by microfluidic experiments are comprehensively reviewed. This review is expected to boost both experimental and theoretical understanding of suspension transport and interfacial interaction processes in porous media. It also brings forward the challenges and opportunities for future research in controlling complex fluid flow in porous media for diverse applications.

A robot-assisted acoustofluidic end effector

Liquid manipulation is the foundation of most laboratory processes. For macroscale liquid handling, both do-it-yourself and commercial robotic systems are available; however, for microscale, reagents are expensive and sample preparation is difficult. Over the last decade, lab-on-a-chip (LOC) systems have come to serve for microscale liquid manipulation; however, lacking automation and multi-functionality. Despite their potential synergies, each has grown separately and no suitable interface yet exists to link macro-level robotics with micro-level LOC or microfluidic devices. Here, we present a robot-assisted acoustofluidic end effector (RAEE) system, comprising a robotic arm and an acoustofluidic end effector, that combines robotics and microfluidic functionalities. We further carried out fluid pumping, particle and zebrafish embryo trapping, and mobile mixing of complex viscous liquids. Finally, we pre-programmed the RAEE to perform automated mixing of viscous liquids in well plates, illustrating its versatility for the automatic execution of chemical processes.

Optimization of Electrode Patterns for an ITO-Based Digital Microfluidic through the Finite Element Simulation

Indium tin oxide (ITO)-based digital microfluidics (DMF) with unique optical and electrical properties are promising in the development of integrated, automatic and portable analytical systems. The fabrication technique using laser direct etching (LDE) on ITO glass has the advantages of being rapid, low cost and convenient. However, the fabrication resolution of LDE limits the minimum line width for patterns on ITO glasses, leading to a related wider lead wire for the actuating electrodes of DMF compared with photolithography. Therefore, the lead wire of electrodes could affect the droplet motion on the digital microfluidic chip due to the increased contact line with the droplet. Herein, we developed a finite element model of a DMF with improved efficiency to investigate the effect of the lead wire. An optimized electrode pattern was then designed based on a theoretical analysis and validated by a simulation, which significantly decreased the deformation of the droplets down to 0.012 mm. The performance of the optimized electrode was also verified in an experiment. The proposed simulation method could be further extended to other DMF systems or applications to provide an efficient approach for the design and optimization of DMF chips.

An Electromechanical Lab-on-a-Chip Platform for Colorimetric Detection of Serum Creatinine

Chronic kidney disease (CKD) is a high-cost disease that affects approximately one in ten people globally, progresses rapidly, results in kidney failure or dialysis, and triggers other diseases. Although clinically used serum creatinine tests are used to evaluate kidney functions, these tests are not suitable for frequent and regular control at-home settings that obstruct the regular monitoring of kidney functions, improving CKD management with early intervention. This study introduced a new electromechanical lab-on-a-chip platform for point-of-care detection of serum creatinine levels using colorimetric enzyme-linked immunosorbent assay (ELISA). The platform was composed of a chip containing microreservoirs, a stirring bar coated with creatinine-specific antibodies, and a phone to detect color generated via ELISA protocols to evaluate creatinine levels. An electromechanical system was used to move the stirring bar to different microreservoirs and stir it inside them to capture and detect serum creatinine in the sample. The presented platform allowed automated analysis of creatinine in ∼50 min down to ∼1 and ∼2 mg/dL in phosphate-buffered saline (PBS) and fetal bovine serum (FBS), respectively. Phone camera measurements in hue, saturation, value (HSV) space showed sensitive analysis compared to a benchtop spectrophotometer that could allow low-cost analysis at point-of-care.

Next-generation engineered microsystems for cell biology: a systems-level roadmap

Engineered microsystems for in vitro studies of cultured cells are evolving from simple 2D platforms to 3D architectures and organoid cultures. Despite advances in reproducing ever more sophisticated biology in these systems, there remain foundational challenges in re-creating key aspects of tissue composition, architecture, and mechanics that are critical to recapitulating in vivo processes. Against the backdrop of current progress in 3D fabrication methods, we evaluate the key requirements for the next generation of cellular platforms. We postulate that these future platforms - apart from building tissue-like structures - will need to have the ability to readily sense and autonomously modulate tissue responses over time, as occurs in natural microenvironments. Such interactive robotic platforms that report and guide cellular events will enable us to probe a previously inaccessible class of questions in cell biology.

Colorimetric Sensing with Gold Nanoparticles on Electrowetting-Based Digital Microfluidics

Digital microfluidic (DMF) has been a unique tool for manipulating micro-droplets with high flexibility and accuracy. To extend the application of DMF for automatic and in-site detection, it is promising to introduce colorimetric sensing based on gold nanoparticles (AuNPs), which have advantages including high sensitivity, label-free, biocompatibility, and easy surface modification. However, there is still a lack of studies for investigating the movement and stability of AuNPs for in-site detection on the electrowetting-based digital microfluidics. Herein, to demonstrate the ability of DMF for colorimetric sensing with AuNPs, we investigated the electrowetting property of the AuNPs droplets on the hydrophobic interface of the DMF chip and examined the stability of the AuNPs on DMF as well as the influence of evaporation to the colorimetric sensing. As a result, we found that the electrowetting of AuNPs fits to a modified Young–Lippmann equation, which suggests that a higher voltage is required to actuate AuNPs droplets compared with actuating water droplets. Moreover, the stability of AuNPs was maintained during the processing of electrowetting. We also proved that the evaporation of droplets has a limited influence on the detections that last several minutes. Finally, a model experiment for the detection of Hg²⁺ was carried out with similar results to the detections in bulk solution. The proposed method can be further extended to a wide range of AuNPs-based detection for label-free, automatic, and low-cost detection of small molecules, biomarkers, and metal ions.

Tumor-Induced Inflammatory Cytokines and the Emerging Diagnostic Devices for Cancer Detection and Prognosis

Chronic inflammation generated by the tumor microenvironment is known to drive cancer initiation, proliferation, progression, metastasis, and therapeutic resistance. The tumor microenvironment promotes the secretion of diverse cytokines, in different types and stages of cancers. These cytokines may inhibit tumor development but alternatively may contribute to chronic inflammation that supports tumor growth in both autocrine and paracrine manners and have been linked to poor cancer outcomes. Such distinct sets of cytokines from the tumor microenvironment can be detected in the circulation and are thus potentially useful as biomarkers to detect cancers, predict disease outcomes and manage therapeutic choices. Indeed, analyses of circulating cytokines in combination with cancer-specific biomarkers have been proposed to simplify and improve cancer detection and prognosis, especially from minimally-invasive liquid biopsies, such as blood. Additionally, the cytokine signaling signatures of the peripheral immune cells, even from patients with localized tumors, are recently found altered in cancer, and may also prove applicable as cancer biomarkers. Here we review cytokines induced by the tumor microenvironment, their roles in various stages of cancer development, and their potential use in diagnostics and prognostics. We further discuss the established and emerging diagnostic approaches that can be used to detect cancers from liquid biopsies, and additionally the technological advancement required for their use in clinical settings.

Nobel Turing Challenge: creating the engine for scientific discovery

Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the "science of science" needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by "AI Scientists" may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond.

AbCellera's success is unprecedented: what have we learned?

The search for antibody therapeutic candidates is a timely and important challenge well-suited to lab on a chip approaches. Vancouver-based AbCellera Biologics Inc. developed a microfluidic antibody screening platform, ancillary technologies, and a service-based drug discovery business model that has proved a tremendous success. We take the opportunity here to reflect on what enabled this success. We consider the common lab on a chip motivations that were part of their success, and those that were not.

Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions

Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI's role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories' self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials' synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.

Artificial intelligence and automation in computer aided synthesis planning

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.

Elevating Chemistry Research with a Modern Electronics Toolkit

With the rapid development of high technology, chemical science is not as it used to be a century ago. Many chemists acquire and utilize skills that are well beyond the traditional definition of chemistry. The digital age has transformed chemistry laboratories. One aspect of this transformation is the progressing implementation of electronics and computer science in chemistry research. In the past decade, numerous chemistry-oriented studies have benefited from the implementation of electronic modules, including microcontroller boards (MCBs), single-board computers (SBCs), professional grade control and data acquisition systems, as well as field-programmable gate arrays (FPGAs). In particular, MCBs and SBCs provide good value for money. The application areas for electronic modules in chemistry research include construction of simple detection systems based on spectrophotometry and spectrofluorometry principles, customizing laboratory devices for automation of common laboratory practices, control of reaction systems (batch- and flow-based), extraction systems, chromatographic and electrophoretic systems, microfluidic systems (classical and nonclassical), custom-built polymerase chain reaction devices, gas-phase analyte detection systems, chemical robots and drones, construction of FPGA-based imaging systems, and the Internet-of-Chemical-Things. The technology is easy to handle, and many chemists have managed to train themselves in its implementation. The only major obstacle in its implementation is probably one's imagination.

High-throughput screening by droplet microfluidics: perspective into key challenges and future prospects

In two decades of development, impressive strides have been made for automating basic laboratory operations in droplet-based microfluidics, allowing the emergence of a new form of high-throughput screening and experimentation in nanoliter to femtoliter volumes. Despite advancements in droplet storage, manipulation, and analysis, the field has not yet been widely adapted for many high-throughput screening (HTS) applications. Broad adoption and commercial development of these techniques require robust implementation of strategies for the stable storage, chemical containment, generation of libraries, sample tracking, and chemical analysis of these small samples. We discuss these challenges for implementing droplet HTS and highlight key strategies that have begun to address these concerns. Recent advances in the field leave us optimistic about the future prospects of this rapidly developing technology.