2023 Impact factor 2.6
Special Topics

EPJ E Highlight – Toward a Fast-Switching Liquid Crystal

Chiral molecules organize into a liquid crystal Credit: Kumar et al. 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.

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EPJ E Highlight – How hydrophobicity shapes protein assemblies

Hydrophobic dipoles align in parallel. Credit: Angel Mozo-Villarías et al. 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.

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EPJ E Highlight - Exploring the elasticity of colloidal suspensions

Particles in a colloidal suspension.
Author: Zephyris, CC BY-SA 3.0 https://creativecommons.org/ licenses/by-sa/3.0, via Wikimedia Commons

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.

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EPJ E Highlight - Measuring nanocomposite structures with neutron and x-ray scattering

Simulating diversity in nanoparticle sizes

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.

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EPJ E Highlight - Steering ‘microswimmers’ through choppy waters

A diagram of microswimmers immersed in a non-steady flow following five possible navigation strategies. Credit: J. Bec (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.

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EPJ E Highlight - Active Brownian particles have four distinct states of motion

Switching between locked and running states

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.

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EPJ E Highlight - Improving fluid simulations with embedded neural networks

Simulating flows in a complex fluid

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.

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EPJ E Highlight - Training models with a structured data curriculum

Building a structured curriculum of data

By carefully structuring the data used to train models of complex systems by leveraging physics and information theory, researchers can significantly improve the quality of their predictions, without relying on additional principles from machine learning in situations where less information about the system is available.

Researchers are now increasingly driven to identify and model the intricate mathematical patterns found in complex natural systems, where the interactions of many simple parts and subsystems can give rise to deeply intricate mathematical patterns. Today, machine learning is the most widely used technique to model these systems. Through new analysis in EPJ E, a research team at Université Paris-Saclay shows how a ‘curriculum learning’ approach, which carefully structures the data used to train models, can significantly improve their results, without relying on additional machine learning principles.

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EPJ E Highlight - Shear ultrasound shaking lowers friction between solids

Measuring responses to shear ultrasound vibrations

A simple new experiment shows how tiny ultrasound shaking at the interfaces between two objects will lower the friction between them – and in some cases, can induce sudden, large jerky motions

When high-frequency shaking occurs at an interface between two solids, recent experiments have revealed that the frictional forces between the objects can be weakened. Through a simple new experiment detailed in EPJ E, Julien Léopoldès at Université Gustave Eiffel, Marne la Vallée (formerly at ESPCI Paris) has discovered that mechanical vibrations also enhance structural aging in these systems, and can sometimes trigger sudden, jerking motions. The results could lead to a better understanding of how buildings are weakened by ambient vibrations, and may also help geologists to draw new insights into the mechanisms responsible for triggering earthquakes and landslides.

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EPJ E Highlight - Machine learning could help kites and gliders to harvest wind energy

Powering a ship with a kite

Using trial-and-error, machine learning algorithms could enable flying wind harvesters to dynamically adjust their orientations, allowing them to account for unpredictable turbulence and improve their performances.

Airborne wind energy (AWE) is a lightweight technology which uses flying devices including kites and gliders to harvest power from the atmosphere. To maximise the energy they extract, these devices need to precisely control their orientations to account for turbulence in Earth’s atmosphere. Through new research published in EPJ E, Antonio Celani and colleagues at the Abdus Salam International Center for Theoretical Physics, Italy, demonstrate how a Reinforcement Learning algorithm could significantly boost the ability of AWE devices to account for turbulence.

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Managing Editors
Sandrine Karpe and Vijala Kiruvanayagam (EDP Sciences) and Sabine Lehr (Springer-Verlag)
Dear Isabelle,
Many thanks for all the hard work. Many thanks indeed!

Peter M.A. Sloot, University of Amsterdam, The Netherlands
Editor EPJ Special Topics 222/6, 2013

ISSN: 1951-6355 (Print Edition)
ISSN: 1951-6401 (Electronic Edition)

© EDP Sciences and Springer-Verlag