Recent advances in microfluidic technology have shown the importance of precise temperature control in a wide range of biological applications. This perspective review presents a comprehensive overview of state-of-the-art microfluidic platforms that utilize thermal modulation for various applications, such as rapid nucleic acid amplification, targeted hyperthermia for cancer therapy, and efficient cellular lysis. We detail various heating mechanisms-including nanoparticle-driven induction, photothermal conversion, and electrothermal approaches (both external and on-chip)-and discuss how they are integrated within lab-on-a-chip systems. In parallel, advanced multi-modal sensing methods within microfluidics, ranging from conventional integrated sensors to cutting-edge quantum-based techniques using nanodiamond nitrogen-vacancy centers and suspended microchannel resonators, are highlighted. By integrating advanced multi-modal sensing capabilities into these microfluidic platforms, a broader range of applications are enabled, including single-cell analysis, metabolic profiling, and scalable diagnostics. Looking ahead, overcoming challenges in system integration, scalability, and cost-effectiveness will be essential to harnessing their full potential. Future developments in this field are expected to drive the evolution of lab-on-a-chip technologies, ultimately enabling breakthroughs in precision medicine and high-throughput biomedical applications.

Silicon-based microfluidics enable the creation of highly complex, three-dimensional fluid networks. These comprise scalable channel sizes and monolithically integrated functionalities available from complementary-metal-oxide-semiconductor technology. On this versatile, solid-state platform, advanced manufacturing techniques exist that allow the channel walls to be directly electrified with one or multiple pairs of electrodes along the fluid-carrying channel. The electrodes have ideal electrostatic geometries, yielding homogeneous electric field distributions across the entire cross section of the microfluidic channel. As these are located directly at the channel, only low supply voltages are needed to achieve suitable field strengths. Furthermore, a controlled supply of charge carriers to the microfluidic channel is feasible. These configurations may serve numerous applications, including highly efficient mechanisms to manipulate droplets, cells, and molecular compounds, perform pico-injection or poration, trigger and control chemical reactions, or realize electrochemical and capacitive sensing modalities. In this perspective, we describe the generic design and fabrication of these electrodes and discuss their miniaturization and scaling properties. Furthermore, we forecast novel use cases and discuss challenges in the context of the most interesting applications.

Remote health monitoring has the potential to enable individuals to take control of their own health and well-being and to facilitate a transition toward preventative and personalized healthcare. Sweat can be sampled non-invasively and contains a wealth of information about the metabolic state of an individual, making it an excellent candidate for remote health monitoring. An accurate, rapid, and low-cost biofluid characterization technique is required to enable the widespread use of remote health monitoring. We previously introduced microfluidic impedance spectroscopy for the detection of electrolyte concentration in fluids, whereby a novel device architecture, measurement method, and analysis technique were presented for the characterization of cationic species. The purely electrical nature of this measurement technique removes the intermediate steps inherent in common rival technologies such as optical and electrochemical sensing, offering a range of advantages. In this work, we investigate the effect of temperature on microfluidic impedance spectroscopy of ionic species commonly present in biofluids. We find that the impedance spectra and concentration determination are temperature-dependent; remote health monitoring devices must be calibrated appropriately as they are likely to experience temperature fluctuations. Importantly, we demonstrate the ability of the method to measure the concentration of anionic species alongside that of cationic species, enabling the detection of chloride and lactate, which are useful biomarkers for hydration, cystic fibrosis, fatigue, sepsis, and hypoperfusion. We show that the presence of neutral species does not impair accurate determination of ionic concentration, thus, demonstrating the suitability of microfluidic impedance spectroscopy for non-invasive biofluid characterization.

Acoustic holography offers the ability to generate designed acoustic fields, enhancing the versatility of acoustic micromanipulation. However, the quality of the generated holograms depends on the nature of the iterative algorithm that is utilized, where the iterative angular spectrum approach (IASA) has been the standard method to date. Here, we introduce a novel approach that categorically improves IASA performance, where we apply the principles of simulated annealing for the generation of high-quality acoustic holograms. We utilize this to realize significant improvements in hologram quality via simulations, fabricated holograms, experimental particle patterning, and high-resolution 2D hydrophone scans. Comparing holograms produced from IASA and/or simulated annealing, we demonstrate that the use of simulated annealing in acoustic holography results in sharper reconstructions and improved hologram outputs across a range of evaluation metrics.

To address the growing need for accurate lung models, particularly in light of respiratory diseases, lung cancer, and the COVID-19 pandemic, lung-on-a-chip technology is emerging as a powerful alternative. Lung-on-a-chip devices utilize microfluidics to create three-dimensional models that closely mimic key physiological features of the human lung, such as the air-liquid interface, mechanical forces associated with respiration, and fluid dynamics. This review provides a comprehensive overview of the fundamental components of lung-on-a-chip systems, the diverse fabrication methods used to construct these complex models, and a summary of their wide range of applications in disease modeling and aerosol deposition studies. Despite existing challenges, lung-on-a-chip models hold immense potential for advancing personalized medicine, drug development, and disease prevention, offering a transformative approach to respiratory health research.

The micromechanical measurement field has struggled to establish repeatable techniques because the deforming stresses can be difficult to model. A recent numerical study [Lu et al., J. Fluid Mech. 962, A26 (2023)] showed that viscoelastic capsules flowing through a cross-slot can achieve a quasi-steady strain near the extensional flow stagnation point that is equal to the equilibrium static strain, thereby implying that the capsule's elastic behavior can be captured in continuous device operation. However, no experimental microfluidic cross-slot studies have reported quasi-steady strains for suspended cells or particles to our knowledge. Here, we demonstrate experimentally the conditions necessary for the cross-slot microfluidic device to replicate a uniaxial creep test at the microscale and at relatively high throughput. By using large dimension cross-slots relative to the microparticle diameter, our cross-slot implementation creates an extensional flow region that is large enough for agarose hydrogel microparticles to achieve a strain plateau while dwelling near the stagnation point. This strain plateau will be key for accurately and precisely measuring viscoelastic properties of small microscale biological objects. We propose an analytical mechanical model to extract linear viscoelastic mechanical properties from observed particle strain histories. Particle image velocimetry measurements of the unperturbed velocity field is used to estimate where in the device particles experienced extensional flow and where the mechanical model might be applied to extract mechanical property measurements. Finally, we provide recommendations for applying the cross-slot microscale creep experiment to other biomaterials and criteria to identify particles that likely achieved a quasi-steady strain state.

Despite the widespread application of microfluidic chips in research fields, such as cell biology, molecular biology, chemistry, and life sciences, the process of designing new chips for specific applications remains complex and time-consuming, often relying on experts. To accelerate the development of high-performance and high-throughput microfluidic chips, this paper proposes an automated Deterministic Lateral Displacement (DLD) chip design algorithm based on reinforcement learning. The design algorithm proposed in this paper treats the throughput and sorting efficiency of DLD chips as key optimization objectives, achieving multi-objective optimization. The algorithm integrates existing research results from our team, enabling rapid evaluation and scoring of DLD chip design parameters. Using this comprehensive performance evaluation system and deep Q-network technology, our algorithm can balance optimal separation efficiency and high throughput in the automated design process of DLD chips. Additionally, the quick execution capability of this algorithm effectively guides engineers in developing high-performance and high-throughput chips during the design phase.

Droplet microfluidics has emerged as a versatile and powerful tool for various analytical applications, including single-cell studies, synthetic biology, directed evolution, and diagnostics. Initially, access to droplet microfluidics was predominantly limited to specialized technology labs. However, the landscape is shifting with the increasing availability of commercialized droplet manipulation technologies, thereby expanding its use to non-specialized labs. Although these commercial solutions offer robust platforms, their adaptability is often constrained compared to in-house developed devices. Consequently, both within the industry and academia, significant efforts are being made to further enhance the robustness and automation of droplet-based platforms, not only to facilitate technology transfer to non-expert laboratories but also to reduce experimental failures. This Perspective article provides an overview of recent advancements aimed at increasing the robustness and accessibility of systems enabling complex droplet manipulations. The discussion encompasses diverse aspects such as droplet generation, reagent addition, splitting, washing, incubation, sorting, and dispensing. Moreover, alternative techniques like double emulsions and hydrogel capsules, minimizing or eliminating the need for microfluidic operations by the end user, are explored. These developments are foreseen to facilitate the integration of intricate droplet manipulations by non-expert users in their workflows, thereby fostering broader and faster adoption across scientific domains.

Thanks to their softness, biocompatibility, porosity, and ready availability, hydrogels are commonly used in microfluidic assays and organ-on-chip devices as a matrix for cells. They not only provide a supporting scaffold for the differentiating cells and the developing organoids, but also serve as the medium for transmitting oxygen, nutrients, various chemical factors, and mechanical stimuli to the cells. From a bioengineering viewpoint, the transmission of forces from fluid perfusion to the cells through the hydrogel is critical to the proper function and development of the cell colony. In this paper, we develop a poroelastic model to represent the fluid flow through a hydrogel containing a biological cell modeled as a hyperelastic inclusion. In geometries representing shear and normal flows that occur frequently in microfluidic experiments, we use finite-element simulations to examine how the perfusion engenders interstitial flow in the gel and displaces and deforms the embedded cell. The results show that pressure is the most important stress component in moving and deforming the cell, and the model predicts the velocity in the gel and stress transmitted to the cell that is comparable to in vitro and in vivo data. This work provides a computational tool to design the geometry and flow conditions to achieve optimal flow and stress fields inside the hydrogels and around the cell.

The gut-brain axis (GBA) connects the gastrointestinal tract and the central nervous system (CNS) via the peripheral nervous system and humoral (e.g., circulatory and lymphatic system) routes. The GBA comprises a sophisticated interaction between various mammalian cells, gut microbiota, and systemic factors. This interaction shapes homeostatic and pathophysiological processes and plays an important role in the etiology of many disorders including neuropsychiatric conditions. However, studying the underlying processes of GBA in vivo, where numerous confounding factors exist, is challenging. Furthermore, conventional in vitro models fall short of capturing the GBA anatomy and physiology. Microfluidic platforms with integrated sensors and actuators are uniquely positioned to enhance in vitro models by representing the anatomical layout of cells and allowing to monitor and modulate the biological processes with high spatiotemporal resolution. Here, we first briefly describe microfluidic technologies and their utility in modeling the CNS, vagus nerve, gut epithelial barrier, blood-brain barrier, and their interactions. We then discuss the challenges and opportunities for each model, including the use of induced pluripotent stem cells and incorporation of sensors and actuator modalities to enhance the capabilities of these models. We conclude by envisioning research directions that can help in making the microfluidics-based GBA models better-suited to provide mechanistic insight into pathophysiological processes and screening therapeutics.