- 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.
- Published on 19 April 2023
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.
- Published on 15 February 2023
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.
- Published on 02 February 2023
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.
- Published on 23 December 2022
Research into the movement of packages of bacteria could help better understand the formation of troublesome biofilms.
Biofilms form when microorganisms such as certain types of bacteria adhere to the surface of objects in a moist environment and begin to reproduce resulting in the excretion of a slimy glue-like substance.
These biofilms aren’t just unpleasant and unappealing however, they can be seriously troublesome. For example, in the medical field, the formation of biofilm can reduce the effectiveness of antibiotic treatments. The key to understanding biomass formation lies in understanding how bacteria behave en masse.
A new paper in EPJ E by Heinrich-Heine-Universität, Düsseldorf, Germany, researcher Davide Breoni and his co-authors presents a mathematical model for the motion of bacteria that includes cell division and death, the basic ingredients of the cell cycle.