Francesco Del Giudice

Rheology plays a critical role across science and engineering, affecting sectors from healthcare to energy. In the UK, rheological research supports innovation in both academia and industry. This review, coordinated by the British Society of Rheology (BSR), provides an updated overview of current research directions across the UK and beyond.

Droplet microfluidics enables the generation of highly monodisperse picoliter droplets and underpins a wide range of applications, from single-cell analysis to materials synthesis. Despite the maturity of the field, the design and operation of droplet microfluidic systems still largely rely on trial-and-error approaches, driven by the absence of universal predictive models and exacerbated by device-to-device and lab-to-lab variability. In recent years, machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools to address these challenges by extracting structure–property–process relationships from experimental and numerical data. This Perspective provides a critical overview of recent advances in AI-enabled droplet microfluidics, with a particular focus on three interconnected themes of crucial interest in microfluidic applications: prediction of droplet properties, read out of droplet characteristics from experimental images, and closed-loop optimization of device operation and design. We discuss how data-driven models have enabled accurate prediction of droplet size, frequency, and regime across different geometries, how computer vision approaches have transformed high-throughput droplet analysis, and how Bayesian optimization and autonomous frameworks are moving the field toward automated microfluidic platforms. Finally, we highlight current limitations, including data sparsity, generalizability, and the treatment of complex fluids, and outline key opportunities for future research aimed at establishing robust, interpretable, and broadly applicable ML and AI tools for droplet microfluidics.

Physiological proteins such as fibrinogen and albumin are key biomarkers in diseases like chronic obstructive pulmonary disease (COPD), cardiovascular conditions, and sepsis. Studying the rheology of these proteins not only enhances clinical understanding but also provides a valuable framework to test polymer physics theories, particularly those describing flexible polymers in biologically relevant conditions. However, experimental characterization, especially of elasticity, remains limited due to measurement challenges. In this study, we employ the μ-rheometer microfluidic rheometer to systematically investigate blood plasma with varying fibrinogen concentrations and bovine serum albumin solutions. We found that the protein data can either be described by models for suspension of rigid spheres or by models for flexible polymers. We further demonstrate the clinical utility of these rheological parameters by applying them as rheological biomarkers to patients with acute exacerbation of COPD (AECOPD) in a UK tertiary hospital. Our study effectively connects fundamental polymer physics concepts with clinical diagnostics, underscoring the potential of protein rheology research to enhance healthcare monitoring and therapeutic strategies.

Microfluidic encapsulation enables the isolation of particles or cells within picolitre-sized droplets, forming the foundation for a wide range of applications in biology, medicine, and materials science. From droplet-based single-cell ribonucleic acid (RNA) sequencing to long-term three-dimensional cell culture in microgels, the ability to efficiently encapsulate one or more objects per droplet is essential for throughput and downstream analysis. However, random encapsulation governed by Poisson statistics results in high fractions of empty droplets and undesired multiple occupancy, limiting the efficiency of current workflows between 20% and 36%, depending on the initial loading. Viscoelastic encapsulation provides a passive and scalable alternative to active sorting or inertial ordering approaches. By exploiting elastic and shear-thinning effects, objects self-organize into equally spaced trains along the channel centerline, enabling frequency-matched droplet formation. Despite its promise, only two studies have explored this phenomenon, with limitations including large droplet sizes, filament formation, and confinement constraints that restrict applicability to small biological objects. In this work, we introduce improved microfluidic platforms for viscoelastic-controlled encapsulation and co-encapsulation, demonstrating single-encapsulation efficiency up to 74% and co-encapsulation efficiency up to 46%. We design new device geometries, develop optimized hyaluronic acid formulations, and demonstrate high-efficiency encapsulation of both rigid particles and live Tetraselmis suecica algae. Our platform achieves encapsulation efficiencies well above Poisson statistics, reduced multi-occupancy, and droplet generation frequencies up to 2 kHz. These results establish viscoelastic encapsulation as a practical solution for high-throughput single-cell workflows, offering a robust path forward for droplet-based diagnostics and analytical platforms.

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.

Objects flowing in microfluidic devices can self-organise in ordered structures thanks to the hydrodynamic interactions mediated by either inertial or viscoelastic forces. Such structures have been found to be crucial to enhancing microfluidic applications such as single encapsulation, co-encapsulation, and material synthesis. However, while inertial ordering has been investigated in more detail, studies on viscoelastic ordering are much more limited. In this perspective, we report the recent advancements in viscoelastic ordering while also discussing the open questions and challenges related to this field. We also include a brief description of both experimental and numerical protocols that can be employed to investigate viscoelastic ordering.

We study the impact of fluid viscoelasticity on red blood cell morphology and aggregation and provide the first experimental evidence of cell ordering.

Photochemical cross-linking is a key step for manufacturing microgels in numerous applications, including drug delivery, tissue engineering, material production, and wound healing. Existing photochemical cross-linking techniques in microfluidic devices rely on UV curing, which can cause cell and DNA damage. We address this challenge by developing a microfluidic workflow for producing microgels using visible light-driven photochemical cross-linking of aqueous droplets dispersed in a continuous oil phase. We report a proof-of-concept to construct microgels from the protein Bovine Serum Albumin (BSA) with [Ru(bpy)(3)](2+) mediated cross-linking. By controlling the capillary number of the continuous and dispersed phases, the volumetric flow rate, and the photochemical reaction time within the microfluidic tubing, we demonstrate the construction of protein microgels with controllable and uniform dimensions. Our technique can, in principle, be applied to a wide range of different proteins with biological and responsive properties. This work therefore bridges the gap between hydrogel manufacturing using visible light and microfluidic microgel templating, facilitating numerous biomedical applications.

Machine learning has recently been introduced in the context of droplet microfluidics to simplify the process of droplet formation, which is usually controlled by a variety of parameters. However, the studies introduced so far have mainly focused on droplet size control using water and mineral oil in microfluidic devices fabricated using soft lithography or rapid prototyping. This approach negated the applicability of machine learning results to other types of fluids more relevant to biomedical applications, while also preventing users that do not have access to microfluidic fabrication facilities to take advantage of previous findings. There are a number of different algorithms that could be used as part of a data driven approach, and no clear comparison has been previously offered among multiple machine learning architectures with respect to the predictions of flow rate values and generation rate. We here employed machine learning to predict the experimental parameters required for droplet generation in three commercialized microfluidic flow-focusing devices using phosphate buffer saline and biocompatible fluorinated oil as dispersed and continuous liquid phases, respectively. We compared three different machine learning architectures and established the one leading to more accurate predictions. We also compared the predictions with a new set of experiments performed at a different day to account for experimental variability. Finally, we provided a proof of concept related to algae encapsulation and designed a simple app that can be used to generate accurate predictions for a given droplet size and generation rate across the three commercial devices.

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.