EPJ B Highlight - Uncovering the magnetic responses of anisotropic semimetals
- Details
- Published on 14 July 2025
Calculations show that magnetic fields can alter the responses of anisotropic 2D semimetals to electric fields and temperature gradients – but only when applied perpendicular to the material’s plane
For solid-state physicists, graphene has become a posterchild of 2D semimetals: materials whose electronic structures fall between those of a metal and a semiconductor. Owing to the honeycomb structure of its carbon atoms, graphene hosts an orderly arrangement of Dirac cones – pairs of opposite-facing, cone-shaped energy bands that touch at a single point. Immediately surrounding such a point, electron energy varies linearly with momentum, just like for massless particles such as photons – leading to exotic and often useful electronic properties.
Through a new paper published in EPJ B, Ipsita Mandal at the Shiv Nadar Institution of Eminence, India, presents fresh calculations of how these properties vary in the presence of magnetic fields, particularly when 2D semimetals are structurally distorted. Her results show that these materials’ electrical and thermal responses are affected only when the magnetic field is oriented perpendicular to the 2D plane. This finding offers deeper insight into the electronic behaviour of semimetals – potentially broadening their already wide range of technological applications.
EPJ Plus Highlight - Hybrid algorithm uncovers robust scar states for quantum computing
- Details
- Published on 14 July 2025
An algorithm that merges classical and quantum computing resources could help reveal robust quantum states hidden within chaotic, noisy systems
Since today’s quantum computing architectures are inherently noisy, they still struggle to generate large amounts of entanglement between qubits. One promising solution could be to target quantum scar states, which can emerge in complex, many-body systems. These states are unusually simple compared with their chaotic surroundings, and they may offer a more robust way to store quantum information – making them especially attractive for building stable quantum logic gates.
Through new research published in EPJ Plus, an international team led by Gabriele Cenedese at the University of Insubria, Italy, demonstrates how the limited entanglement in noisy quantum computers could be transformed into an advantage, making it easier to identify scar states within chaotic quantum systems. Involving a specialised hybrid algorithm, the team’s approach could help pave the way toward more scalable quantum architectures – reducing the need for complex and costly error-correction techniques.
EPJ ST Highlight - Data-Driven Insights into Inkjet Droplet Formation
- Details
- Published on 09 July 2025
High-speed image analysis shows how the control parameters of inkjet printers are linked to the shapes of the ink droplets they produce – helping researchers to optimise the printing process.
Inkjet printing has become a cornerstone of high-tech microfabrication, underpinning applications ranging from microchip production and drug delivery to DNA sequencing and tissue engineering. In these fields, precision is paramount - the ability to reliably place picolitre-sized droplets with exact morphology determines the success of both medical treatments and microelectronic device fabrication.
Despite advances in computational fluid dynamics (CFD), simulating and controlling droplet formation in real-world conditions remains a challenge due to the complexity of two-phase flows and the vast number of operational parameters involved. To address this, researchers are turning to data-driven approaches as a complementary or alternative strategy. These methods can reduce reliance on time-consuming simulations and enable real-time analysis and decision-making in manufacturing environments.
In a new study published in EPJ Special Topics (EPJ ST), researchers at CIMNE/UPC present a comprehensive data-driven investigation of droplet morphology in inkjet printing. The team, led by Pavel Ryzhakov, began by performing extensive controlled droplet-generation experiments using a piezoelectric inkjet dispenser. Each droplet was captured via high-speed imaging, yielding a rich dataset of raw images and extracted geometrical features.
