- Published on 14 November 2023
In hydrodynamics, a lift phenomenon arises when a force acts on an object perpendicularly to its initial motion. In everyday life, we are familiar with this effect allowing for instance planes to take off or soccer balls to follow bent trajectories. For such big and fast objects, inertia combines with symmetry breaking (wing shape profile or ball rotation) to give rise to lift. However, lift forces are also at play at low Reynolds numbers, i.e. for small objects or slow flows where fluid viscosity dominates over inertia.
- Published on 03 November 2023
Guest Editors: Jean-Marc Di Meglio, Aritra Ghosh, Orlando Guzmán, P. Lakshmi Praveen.
This Topical issue focusses on soft matter materials and devices. Soft matter can be defined by a large response to weak external perturbations, with physical properties governed by local internal dynamics. It may contain ordered structures at mesoscopic scales, but appear disordered at molecular scales. Because of this, the ordered and disordered aspects of soft matter can be combined to manufacture innovative and original functional materials and devices. The most ambitious course of action is then to develop multi-functional materials and devices that exploit two or more rheological, electrical, electronic, photo-physical, quantum chemical or opto-electronic responses of soft matter. Solving grand challenges for new generations of functional materials demands. The aim of this Topical Issue is to provide an inspiring platform for the dissemination of the latest developments and discoveries in the design, simulation, modeling, synthesis, construction and characterization of soft matter structures for functional materials and devices. The emphasis of this Topical issue is on physical methodologies and characterizations to resolve multi-disciplinary issues.
- Published on 30 October 2023
Guest Editors: Luca Biferale, Michele Buzzicotti and Massimo Cencini.
The collection addresses open problems, challenges, and benchmarks for data-driven and equation-informed tools for data assimilation, prediction, (subgrid-scale) modeling, inpainting, classification, and (optimal) control of Eulerian and Lagrangian problems in complex flows.
The goal is to move from proof-of-concept to quantitative benchmarks and grand challenges, including scaling of algorithms and complexity of datasets.
The original research papers, presented in a colloquium format, focus on the latest experimental, theoretical, or computational advances and address the interpretability, superiority, and usability of data-driven tools when applied to realistic fluid dynamics problems in engineering, geophysics, biophysics, and other fields. Key topics covered include: (i) Modeling and controlling complex flows with data-driven methods. (ii) Prediction and data-assimilation of multiscale flows. (iii) Reconstruction, super-resolution of fluid flows with data-driven and physics-informed tools. (iv) Optimization of navigation and other tasks in complex flows. (v) Animal behavior in flows.
- Published on 09 October 2023
Combining a bulky chain with a stable polymer can enhance liquid crystal performance
From laptop screens to navigation systems, liquid crystals are ubiquitous in modern life. These materials flow like liquids, but their molecules align with one another in a way that resembles the orientational order of a crystal. Electrically switching between different molecular orientations – or phases – in a liquid crystal changes how the material transmits light, hence their use/utility in visual displays.
In a study published in EPJ E, Ashok Kumar, of Jawaharlal Nehru Technological University, Kakinada, India, and his colleagues now report on a new design that adds a bulky chemical side chain to a polymer liquid crystal. The approach combines components that, separately, avoid optical degradation and enhance thermal stability; and could lead to improved switching speeds.
- Published on 01 September 2023
Using an electrical analogy, researchers show how a distribution of hydrophobic charges draws proteins into parallel alignment in a macromolecule assembly
Through a nuanced balance of electrical and hydrophobic forces, biological molecules self-assemble into the large functional structures that maintain life’s vital functions. Understanding how proteins self-assemble requires knowledge of both forces. But while predicting the electrical interactions of individual proteins is simple, deriving their hydrophobic ones is less straightforward. In a study published in EPJ E, Angel Mozo-Villarias, of the Autonomous University of Barcelona, Spain, and his colleagues develop a formulation for how proteins align into membrane-like structures based on hydrophobic interactions. The model could help to predict the configuration of macromolecular assemblies at any scale, providing a useful tool for novel materials and drug discovery research.
- Published on 21 July 2023
Experiments reveal that under the right conditions, the elasticity of colloidal suspensions will peak at a certain value, which depends both on the deformation applied to the material and the strength of attraction between its solid particles.
The behaviours of colloidal materials – where tiny solid particles are suspended in fluid – depend strongly on how the particles interact with each other. Through new research published in EPJ E, a team led by Pascal Hébraud at the University of Strasbourg, France, show how under certain conditions, the elasticity of silica-based colloids subjected to oscillating flows will peak at a certain value. Their discovery could lead to improved techniques for manipulating the behaviour of colloidal materials, used in fields as wide-ranging as materials science, food technology, construction, and nanotechnology.
- Published on 17 July 2023
Experiments with state-of-the-art scattering instruments reveal an absence of specific patterns in the x-rays scattered by nanocomposite materials. With the help of advanced simulation techniques, a new study suggests that attractive interactions between nanoparticles with diverse shapes and sizes are most likely responsible for this behaviour.
Small-angle scattering of x-rays and neutrons is a useful tool for studying molecular and nanoparticle structures. So far, however, experiments have revealed a surprising lack of nanoparticle structure in certain nanocomposite materials – whose molecular skeletons are reinforced with nanoparticles previously treated with polymer adsorption. In a new approach detailed in EPJ E, Anne-Caroline Genix and Julian Oberdisse at the University of Montpellier, France, show that these patterns can only be produced through attractive interactions between nanoparticles with a diverse array of shapes and sizes. The duo’s results highlight the rapidly improving capabilities of small-angle scattering instruments, and could also help researchers to improve their techniques for studying nanocomposites – with applications in areas including miniaturised electronics, biological tissue engineering, and strong, lightweight materials for aircraft.
- Published on 30 June 2023
New research looks at navigation strategies for deformable microswimmers in a viscous fluid faced with drifts, strains, and other deformations.
A deformable microswimmer is a small-scale organism or artificial structure that uses sinusoidal body undulations to propel itself through a fluid environment.
The term applies to organisms like bacteria which navigate through fluids using whip-like tails called flagella, sperm cells propelling themselves through the female reproductive system, and even nematodes, tiny worms that move through water or soil with undulations. Microswimmers can also describe tiny microrobots constructed from soft-materials designed to respond to stimuli and perform tasks like drug delivery on a micro-scale.
That means the study of microswimmers has applications in a vast array of scientific fields, from biology to fundamental physics to nanorobotics.
In a new paper in EPJ E by Jérémie Bec, a researcher at CNRS and Centre Inria d’Université Côte d’Azur and his colleagues attempt to find an optimal navigation policy for microswimmers, crucial for enhancing their performance, functionality, and versatility for applications such as targeted drug delivery.
- Published on 17 May 2023
Depending on the friction and external bias forces they experience, self-propelled Brownian particles will take on one of four possible states of motion. The discovery could help researchers to draw deeper insights into the behaviours of these unique systems in nature and technology.
Active Brownian motion describes particles which can propel themselves forwards, while still being subjected to random Brownian motions as they are jostled around by their neighbouring particles. Through new analysis published in EPJ E, Meng Su at Northwestern Polytechnical University in China, together with Benjamin Lindner at Humboldt University of Berlin, Germany, have discovered that these motions can be accurately described using four distinct mathematical patterns.
- Published on 08 May 2023
While neural networks can help to improve the accuracy of fluid flow simulations, new research shows how their accuracy is limited unless the right approach is taken. By embedding fluid properties into neural networks, simulation accuracy can improve by orders of magnitude.
The Lattice Boltzmann Method (LBM) is a simulation technique used to describe the dynamics of fluids. Recently, there has been an increasing interest in employing neural networks for computational modelling of fluids. The results of a collaboration between researchers from Eindhoven University of Technology and Los Alamos National Laboratory, published in EPJ E, show how neural networks can be embedded into a LBM framework to model collisions between fluid particles. The team found that it is essential to embed the correct physical properties into the neural network architecture to preserve accuracy. These discoveries could deepen researchers’ understanding of how to model fluid flows.