Design of a 140 GHz TE28,8 Gyrotron Quasi-Optical Mode Converter

A quasi-optical mode converter has been designed for a 140 GHz gyrotron operating in the TE28,8 mode. This development is crucial for the efficient operation of gyrotrons, high-power microwave devices that are fundamental in various fields, including nuclear fusion research and industrial applications. The design aims to optimize the conversion of the microwave mode generated by the gyrotron into a Gaussian beam, which is more suitable for transmission and coupling to external systems. The main challenge in designing these converters lies in the complexity of the high-order microwave modes generated by high-power gyrotrons. The TE28,8 mode, in particular, presents an intricate electromagnetic field structure that requires precise manipulation to achieve efficient conversion. The approach taken in this work focuses on optimizing the reflective surfaces of the converter to minimize losses and maximize the purity of the resulting Gaussian beam. The significance of this advancement lies in its contribution to improving the efficiency and reliability of microwave heating systems in fusion reactors like ITER, where gyrotrons are essential for heating plasma to fusion temperatures. An efficient mode converter ensures that most of the energy generated by the gyrotron is transferred to the plasma, reducing energy waste and operational costs. This design lays the groundwork for future developments in high-frequency, high-power gyrotron technology.

Privacy Risks in Medical AI Greater Than Estimated

A recent study has revealed that sensitive health information about individual patients could be extracted from artificial intelligence (AI) models used in medical diagnostics. This research underscores a growing concern about data security in digital health, suggesting that current safeguards might be insufficient to protect patient privacy against sophisticated attacks. The advancement of AI in medicine has promised faster and more accurate diagnoses, but this new finding highlights a significant risk. The ability to infer personal data from trained models, even if they have been previously anonymized, raises serious ethical and regulatory questions, especially concerning regulations such as GDPR or HIPAA. Although the study does not detail the specific extraction methods or the magnitude of the risk in real-world scenarios, it emphasizes the need to develop more robust anonymization and differential privacy techniques for datasets used in medical AI training. This is crucial to ensure that the benefits of AI in healthcare are not compromised by vulnerabilities in patient information protection.

New Method for Precisely Predicting Blast Vibration Waveforms

Researchers have developed a novel method to predict blast vibration waveforms, incorporating for the first time the variation in peak time. This approach significantly improves the accuracy of predictions compared to existing models, which often underestimate or overestimate vibration peaks due to the complexity of seismic wave propagation in different geological media. The ability to more accurately predict these vibrations is crucial for safety in civil engineering and mining projects, minimizing risks to infrastructure and people. The study focuses on modeling the variation in the time it takes for vibration to reach its maximum amplitude, a factor that has historically been difficult to quantify and introduces considerable uncertainty into predictions. By integrating this parameter, the new method offers a more faithful representation of shock wave dynamics. The results show an improved correlation between model predictions and empirical data obtained from actual blasts, validating the effectiveness of the proposed methodology. The application of this method will allow engineers and planners to optimize blasting patterns, adjusting explosive charge and detonation sequence to better control vibrations. This will not only contribute to operational safety but could also reduce costs associated with structural damage or disruptions to nearby activities. This research is expected to lay the groundwork for the development of more robust and reliable predictive tools in the field of geomechanics and explosive engineering.

Giant nonlinear Hall effect observed in bilayer graphene

Scientists have observed a giant nonlinear Hall effect in bilayer graphene with broken isospin symmetry. This phenomenon, which manifests as a non-reciprocal electrical response, is significantly larger than previously reported nonlinear Hall effects in other materials. The research opens new avenues for the development of electronic devices based on the topological and symmetry properties of materials. The nonlinear Hall effect arises from the interaction between electrons and defects or impurities in a material, or from the Berry curvature in momentum space, which generates a transverse current to the applied electric field even in the absence of a magnetic field. In this study, breaking the isospin symmetry in bilayer graphene, achieved by applying a perpendicular electric field, drastically amplified this effect. Isospin symmetry refers to a quantum property analogous to spin, but related to the valley degrees of freedom in graphene. The researchers used a twisted bilayer graphene configuration, where the alignment of the layers is crucial for the electronic properties. By applying a displacement field, they managed to induce a valley polarization that breaks isospin symmetry, which in turn boosted the nonlinear Hall response. The magnitude of the observed effect is several orders of magnitude higher than in other known systems, making it a promising candidate for applications in low-power electronics and neuromorphic computing devices. This advance underscores the importance of symmetry engineering in quantum materials to unveil novel and exploitable physical phenomena.

Pressure Induces Giant Critical Current Peak in Kagome Superconductor RbV3Sb5

Researchers have discovered that applying hydrostatic pressure can induce a giant peak in the critical current of the kagome superconductor RbV3Sb5. This material, known for its topological properties and charge density wave (CDW) state coexisting with superconductivity, exhibits an unusual response to pressure. At low temperatures, increasing pressure up to 2 GPa suppresses the CDW state and raises the critical temperature (Tc) from 0.9 K to 3.5 K. However, most notably, the critical current (Jc) skyrockets by a factor of 1000, reaching a value of 10^5 A/cm^2 at 0.5 K and 2 GPa. This behavior is atypical in conventional superconductors, where pressure generally has a more moderate effect on Jc. The study focused on understanding the interaction between charge density order and superconductivity in RbV3Sb5. Electrical transport measurements under pressure were used to map the phase diagram. The results suggest that the suppression of the CDW by pressure releases charge carriers that contribute to superconductivity, significantly enhancing the material's ability to carry current without resistance. The magnitude of the Jc increase is comparable to that observed in some high-temperature superconductors, making it a finding of great interest for condensed matter physics. This discovery not only deepens our understanding of kagome superconductors and the complex interrelation between different electronic orders but also opens new avenues for designing superconducting materials with enhanced properties. The ability to drastically modulate the critical current using an external variable like pressure could have implications for technological applications requiring high current density, such as superconducting magnets or energy storage devices. Future research is expected to explore the microscopic mechanisms behind this giant effect and search for other materials with similar responses.

Analysis of a novel conservative chaotic system and its application in cryptography

A recent study has explored the dynamics of a novel conservative chaotic system, characterized by its ability to preserve volume in phase space. These types of systems, unlike dissipative ones, do not lose energy over time and exhibit complex yet predictable long-term behavior, making them of interest for various applications, including information security. The research focuses on understanding the intrinsic properties of this new system and how its chaotic features can be harnessed.

Neural Network Solves High-Dimensional Sine-Gordon Equations

Researchers have developed a gradient-enhanced physics-informed neural network (PINN) with adaptive loss weighting to tackle high-dimensional non-linear Sine-Gordon problems. This novel approach allows for more accurate and stable solutions to complex partial differential equations (PDEs) that describe physical phenomena such as Josephson junctions, coupled pendulum chains, or optical pulse propagation in fibers. The Sine-Gordon equation is known for its non-linear nature and the emergence of soliton-type solutions, making it a computational challenge, especially in scenarios with multiple spatial variables. Traditional PINNs, which embed physical laws directly into the machine learning loss function, often struggle with convergence and accuracy in high-dimensional problems or those with complex dynamics. The introduced enhancement addresses these limitations by incorporating gradient information and dynamically adjusting the weight of different loss function terms during training, guiding the network towards more precise and stable solutions. The proposed methodology represents a significant advancement in the application of artificial intelligence to computational physics. By overcoming the inherent difficulties of high-dimensional Sine-Gordon equations, new avenues are opened for the simulation and analysis of complex physical systems that were previously intractable or required prohibitive computational power with traditional numerical methods. This could accelerate the design of superconducting devices or the understanding of non-linear wave phenomena.

Multilayer engineering enhances energy storage in ferroelectrics

Researchers have developed a novel multilayer engineering strategy to significantly improve the energy storage capacity in tungsten bronze-based ferroelectric materials. This breakthrough is crucial for the development of more efficient and compact electronic devices, especially in applications requiring high energy and power density, such as multilayer ceramic capacitors (MLCCs). The key to this improvement lies in the application of multiple dielectric layers, which allows for optimizing the electrical response of the material. Ferroelectric materials, known for their spontaneous polarization capability, are fundamental in energy storage. However, their efficiency is limited by factors such as hysteresis and dielectric losses. The new technique addresses these limitations by modifying the microstructure and interfacial properties of the material. The results show a remarkable increase in stored energy density compared to conventional ferroelectric materials. This approach not only enhances the performance of existing materials but also opens new avenues for the design of future energy storage devices. This technology is expected to have a significant impact on power electronics, renewable energy systems, and electric vehicles, where the demand for high-performance energy storage components is continuously growing.

Multiband terahertz metasurface for refractive index biosensing

Researchers have numerically proposed a multiband terahertz (THz) metasurface with a high quality (Q) factor for use as a biosensor. This design aims to improve the detection of biomolecules by measuring changes in the refractive index, a crucial technique in medical diagnostics and biological research. The novelty lies in its ability to operate at multiple THz frequencies, which could allow for more detailed characterization of samples. The study focuses on a metasurface design that exhibits high-Q resonances, meaning that the interactions between THz light and the sample are more intense and localized. This high Q factor is essential for detecting subtle changes in the refractive index of biomolecules. Numerical simulation has allowed for the optimization of the metasurface's geometry and materials to achieve these properties, overcoming some limitations of current THz biosensors, such as their sensitivity and ability to operate over a broad spectral range. The relevance of this work lies in the potential to develop more efficient and versatile THz biosensors. The multiband capability not only enhances detection specificity but also opens the door to simultaneous identification of multiple analytes or characterization of complex sample properties. Although this is a numerical study, it lays the groundwork for the fabrication and experimentation of these devices, which could lead to significant advances in early disease diagnosis and the understanding of biological processes at the molecular level.

Simulations Evaluate Production Routes of Cesium-128 for Nuclear Medicine

Researchers have utilized advanced computational simulations to explore the most efficient and viable routes for the production of the positron-emitting radionuclide cesium-128 (¹²⁸Cs). This isotope is of significant interest for applications in nuclear medicine, particularly in positron emission tomography (PET), due to its short half-life and emission characteristics. The study focused on evaluating various nuclear reactions induced by protons and deuterons on xenon (Xe) and iodine (I) targets. To conduct this investigation, the team employed a set of consolidated simulation tools in nuclear physics. GEANT4 codes were used for particle transport modeling and interaction with matter, while TALYS and EMPIRE codes were utilized for simulating nuclear reactions and calculating cross-sections. These simulations allowed for the prediction of ¹²⁸Cs production yields and the presence of radionuclidic impurities as a function of incident particle energy and target material. The primary goal was to identify optimal conditions that maximize the production of pure ¹²⁸Cs, minimizing the co-production of other isotopes that could interfere with PET imaging or increase patient radiation dose. The simulation results provided crucial data on the cross-sections of relevant nuclear reactions, indicating which projectile-target combinations are most promising. This computational modeling approach is fundamental for planning production experiments in cyclotrons and accelerators, reducing the need for costly and time-consuming empirical testing. Optimizing ¹²⁸Cs production could facilitate its availability for research and clinical use, opening new avenues for disease diagnosis and monitoring via PET.

On-demand rotational manipulation of microparticles and zebrafish larvae with acoustics

Scientists have demonstrated an innovative method for manipulating the rotation of microparticles and small-scale living organisms, such as zebrafish larvae, using bulk acoustic waves (BAW) with orthogonal phases. This technique enables precise, on-demand control of rotational movement, opening new avenues for studying biological processes and manipulating materials at the microscale. The advance is based on the application of acoustic fields that generate torques on objects, allowing them to rotate in a specific plane.

Design of topological thermal diffusion in quasi-ballistic phonon regime

Researchers have successfully designed the topological thermal diffusion of phonons in the quasi-ballistic regime, a significant advance in nanoscale heat control. This work introduces the concept of topology into heat flow manipulation, enabling heat direction and isolation with robustness inherent to topological properties. The ability to control heat propagation in this manner has important implications for thermal management in advanced electronic and optoelectronic devices. The quasi-ballistic regime refers to the situation where the mean free path of phonons (quanta of lattice vibrations that carry heat) is comparable to or larger than the device dimensions. At these scales, phonons do not diffuse completely randomly but exhibit ballistic properties that can be exploited. Topological design allows for the creation of preferential paths for heat, analogous to topological insulators in electronics, where electrons move without dissipation along the edges or surfaces of the material while the interior remains insulating. To achieve this, carefully designed nanostructures were employed to modify the phonon spectrum and their interactions. By engineering the properties of the crystal lattice at the nanoscale, topological states for phonons can be induced. These states ensure that heat flow follows specific trajectories, even in the presence of defects or perturbations in the material, providing unprecedented robustness to thermal control. This approach opens the door to the creation of highly efficient and fault-tolerant heat management devices.

Experimental and Numerical Study of Wind-Induced Vibrations in Cable-Stayed Bridges

A recent study has investigated the impact of appurtenances on the response of cable-stayed bridges to wind-induced vibrations. The research combines experimental and numerical analyses to understand how the presence of these components, such as railings or light fixtures, modifies the aerodynamics and, consequently, the stability of these large structures against wind loads. This work is crucial for the design and safety of long-span bridges, where wind-induced vibrations can generate structural fatigue and compromise long-term integrity. The study focused on evaluating the aerodynamic damping and stiffness of bridges, two key parameters that determine their susceptibility to phenomena such as flutter or galloping. Experimental results, obtained through wind tunnel tests with scale models, were complemented by computational fluid dynamics (CFD) numerical simulations. This dual approach allowed for a detailed characterization of air flow patterns around the bridge deck and its appurtenances, identifying how the latter can significantly alter aerodynamic forces. The implications of this research are direct for civil engineering and structural design. By better understanding how appurtenances influence the aerodynamics of cable-stayed bridges, engineers can develop more robust and safer designs, optimizing the shape and arrangement of these elements to mitigate wind-induced vibrations. This could lead to the implementation of design solutions that improve the lifespan of bridges and reduce maintenance costs, while ensuring the safety of the infrastructure against adverse weather conditions.

V-band leaky-wave antenna with enhanced fast beam scanning

Researchers have developed a substrate integrated waveguide (SIW) leaky-wave antenna (LWA) for the V-band (50-75 GHz) that significantly enhances beam scanning speed. This advancement is achieved by inducing a phase modification in the leaky wave, allowing for more dynamic and efficient control over the antenna's beam direction. The ability to rapidly scan the beam is crucial for high-speed communication and sensing applications in complex environments. The design is based on a transition-induced phase modification technique, which alters the wave propagation properties within the SIW. This alteration enables the antenna to steer its beam to different angles more agilely than conventional LWA designs. Traditionally, LWAs offer continuous beam scanning with frequency, but the speed and range of scanning can be limited. This new approach addresses these limitations, opening doors for new functionalities in radar and communication systems. Experimental results demonstrate that the proposed antenna exhibits a remarkable improvement in scanning speed. A 120-degree beam scan has been achieved over a 20 GHz frequency range, representing a substantial improvement over existing technologies. This performance is attained while maintaining high radiation efficiency and a well-defined beam pattern, essential characteristics for integration into practical systems. This development has significant implications for next-generation 5G and 6G wireless communication systems, as well as for high-resolution radar applications. The ability to rapidly scan the beam allows for better coverage, increased data capacity, and more precise object detection. The next step will be the integration of these antennas into complete system prototypes to validate their performance in real operational scenarios and explore their potential in emerging applications.

Narrow Bands with High Chern Numbers in Trilayer-Bilayer Graphene

Researchers have successfully stabilized isolated narrow electronic bands with Chern numbers (C) greater than 1 in a twisted graphene structure, specifically in a rhombohedral trilayer-bilayer system. This breakthrough is significant because Chern numbers, which describe topological properties of energy bands, are typically C=1 in well-known topological materials, such as those exhibiting the quantum Hall effect. The ability to generate and control bands with C > 1 opens new avenues for exploring exotic quantum phenomena and developing electronic devices with advanced functionalities. The study focused on a specific configuration where a trilayer graphene sheet is superimposed and twisted over a bilayer graphene. This layered architecture and precise twist angle are crucial for the emergence of narrow bands. Coulomb interaction between electrons plays a fundamental role in stabilizing these bands, an aspect not always dominant in other twisted graphene systems. Manipulating these interactions allows for tuning the material's electronic properties, which is key for engineering new quantum phases of matter. The observation of bands with C > 1 in this twisted graphene system is an important step towards understanding and harnessing topology in quantum materials. These materials could form the basis for creating new topological states of matter, such as those exhibiting topological superconductivity or fractional quantum Hall effects with enhanced properties. Implications range from quantum computing, where topological states are inherently more robust against decoherence, to low-energy electronics and spintronics.

Enhanced Nuclear Fusion in the Sub-keV Energy Regime

Scientists have achieved a significant improvement in nuclear fusion efficiency within the sub-kiloelectronvolt (sub-keV) energy range. This breakthrough is crucial for the development of fusion energy, as most fusion experiments have focused on higher energies, leaving the sub-keV regime, relevant for ignition, less explored. The research addresses the need to understand and optimize fusion reactions at low energies to achieve self-sustainability. The team utilized an innovative experimental setup to study the cross-section of the deuterium-tritium (D-T) fusion reaction at energies below 1 keV. Traditionally, extrapolation from higher-energy data has been the norm, but this new approach allows for direct measurements in an energy range closer to the ignition threshold. The results show a substantially higher fusion cross-section than expected in this regime, which could have significant implications for the design of future fusion reactors. This enhanced fusion efficiency at low energies suggests that ignition might be easier to achieve than previously thought. The data obtained provide a more robust foundation for theoretical models and plasma simulations, enabling more accurate prediction of fusion device performance. While the path to a commercial fusion reactor is long, this discovery represents a step forward in understanding the fundamental processes governing nuclear fusion and could accelerate the development of clean energy technologies.

Superconducting nanowire resonators reveal Abrikosov vortex entry

Scientists have successfully observed and characterized the entry of Abrikosov vortices into superconducting nanowires using a displacement-noise spectroscopy technique in cavity optomechanics. This breakthrough allows for the study of the dynamics of these vortices, which are crucial for understanding the properties of Type II superconductors and their applications in quantum and electronic devices. Abrikosov vortices are quantized magnetic flux filaments that penetrate Type II superconductors when exposed to an external magnetic field. Their motion and pinning determine phenomena such as energy dissipation and resistance in these materials. The ability to detect the individual entry of these vortices at the nanoscale opens new avenues for optimizing the performance of superconducting devices and developing new architectures for quantum computing. The technique employed combines a superconducting nanowire with a cavity optomechanical system. The nanowire acts as a mechanical resonator, and changes in its motion, induced by the entry of a vortex, are detected with high sensitivity through the light-matter interaction in the cavity. Displacement-noise spectroscopy allows for the identification of unique mechanical signatures associated with vortex nucleation and movement, providing detailed information on the entry mechanisms and the energy barriers involved.

Gate-controlled superconductivity suppression observed

Scientists have directly observed the suppression of superconductivity by an electric field, a phenomenon theoretically predicted but challenging to verify experimentally. Using a high-resolution scanning SQUID (Superconducting Quantum Interference Device) microscope, the team mapped the local magnetic response of a niobium (Nb) superconductor while applying a gate voltage. This breakthrough allows for a deeper understanding of how electric fields can modulate the quantum properties of materials. The Meissner effect, the expulsion of magnetic fields by a superconductor, is a key signature of this quantum state. By applying a gate voltage, researchers observed a gradual reduction in the Meissner screening current at the niobium surface, indicating a localized suppression of superconductivity. This technique offers a non-invasive way to study the interface between a dielectric and a superconductor, opening new avenues for controlling superconducting properties at the nanoscale. The ability to control superconductivity with an electric field is of great interest for the development of quantum and low-energy electronic devices. Traditionally, superconductivity has been controlled by magnetic fields or temperature changes. Electric field modulation, being more energy-efficient and compatible with modern microelectronics, could lead to the creation of superconducting transistors and other components for quantum computing and next-generation electronics. This work lays the groundwork for exploring the electrical manipulation of other quantum phenomena in materials.

Calorimetric Evidence for Excess Heat Generation in Proton-LaB6 Discharge System

A recent study has provided calorimetric evidence of anomalous and sustained heat generation in a proton-lanthanum hexaboride (LaB6) glow-discharge system. This phenomenon, which exceeds the electrical input energy, suggests the existence of low-energy nuclear reactions (LENR) or cold fusion, a field that has been subject to controversy and skepticism for decades. The researchers utilized a high-precision flow calorimeter to measure the thermal power generated in the system. They observed an excess power of up to 200 mW over a period of 100 hours, with an input power of approximately 10 W. This excess heat could not be explained by any known chemical reactions or by stored energy within the system. Lanthanum hexaboride, a ceramic material with high electrical conductivity and a high melting point, was used as the cathode in the glow discharge, where protons impacted its surface. While the results are promising, the authors emphasize the need for further research to replicate the experiment and understand the underlying mechanism of this heat generation. The detection of nuclear reaction products, such as helium or tritium, would be crucial to confirm the nuclear nature of the phenomenon. If validated, this discovery could have significant implications for the development of new energy sources, although the scientific community maintains a cautious stance due to the history of unreplicated claims in the LENR field.

Statistical Physics to Optimize Air Transport in Africa

A new study proposes applying tools from statistical physics to analyze and optimize air transport networks in Africa. The research aims to identify patterns and efficiencies in the continent's air connectivity, which is crucial for economic and social development but often faces logistical and infrastructural challenges. This multidisciplinary approach could offer innovative solutions to complex route planning and management problems. The work focuses on modeling the network of airports and routes as a complex system, similar to those studied in condensed matter physics or biological systems. Using concepts such as graph theory and percolation, researchers can evaluate the network's robustness against perturbations, such as the closure of an airport or a route. This allows for the identification of critical nodes and bottlenecks that, if improved, could significantly increase the overall resilience and efficiency of the African air transport system. Preliminary results suggest that, despite challenges, opportunities exist to improve connectivity by optimizing existing routes and identifying new strategic connections. The application of these models could guide policymakers and airlines in decision-making to expand and strengthen air infrastructure, thereby fostering greater trade, tourism, and regional integration in Africa.

Charge-spin dichotomy discovered in a kagome metal

Researchers have observed unusual behavior in the kagome metal CsCr₃Sb₅, where the charge and spin properties of electrons decouple at low temperatures. This material, which features a kagome lattice structure (a tessellation of hexagons and triangles), exhibits a phase transition at approximately 100 Kelvin, below which charge density waves (CDWs) form. However, unlike other kagome materials, the electron spins in CsCr₃Sb₅ do not magnetically order alongside the charge, but remain disordered down to much lower temperatures, close to 2 Kelvin. This "charge-spin dichotomy" suggests that electronic interactions in this material are more complex than expected and could offer new avenues for understanding quantum states of matter. The study of materials with kagome lattices is of great interest in condensed matter physics due to their potential to host exotic phases, such as superconductivity, topological states, and frustrated magnetic orders. In many systems, phase transitions affecting electronic charge are often accompanied by magnetic or spin ordering. The observation of such a marked decoupling in CsCr₃Sb₅ is particularly notable, as it challenges conventional understandings of how charge and spin interact in these strongly correlated systems. This finding opens the door to exploring new quantum phenomena and to the possible independent manipulation of these properties. To characterize this behavior, scientists employed a combination of experimental techniques, including X-ray diffraction to analyze the crystal structure and CDW formation, and muon spectroscopy to investigate the magnetic state of the electronic spins. Muon spectroscopy data confirmed the absence of long-range magnetic order below the CDW temperature, which contrasts sharply with other kagome metals where charge and spin are often entangled. The results suggest that magnetic exchange interactions in CsCr₃Sb₅ are weak or frustrated, allowing charge to order while spin remains in a liquid or disordered state. This discovery not only deepens our understanding of kagome materials but could also have implications for the design of new electronic devices. The ability to independently control charge and spin in a material could be fundamental for the development of spintronics, where information is encoded in the electron's spin rather than its charge. Future research will focus on exploring the exact nature of the interactions that lead to this dichotomy and on searching for other materials with similar properties, which could unveil new fundamental states of matter.

New X-ray fluorescence imaging technique free of radioisotopes

Researchers have developed a novel X-ray fluorescence ghost imaging (XFGI) technique that allows imaging of heavy elements inside human-scale objects without the need for radioisotopes. This method uses a conventional laboratory X-ray source and a single-pixel detector, making it a safer and more accessible alternative to current radioisotope-based techniques, which pose safety and waste management challenges. The technique is based on the principle of ghost imaging, where the correlation between a structured illumination pattern and the total detected signal allows for the reconstruction of the object's image. The advance is significant because current techniques for deep detection of heavy elements, such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT), require the injection of radioisotopes into the patient. This involves exposure to ionizing radiation and the need for specialized facilities for their production and handling. The proposed XFGI avoids these drawbacks by using an external X-ray source and the characteristic fluorescence of heavy elements, opening the door to non-invasive and safer medical diagnostics and security applications. In the demonstration, the team successfully imaged elements with an atomic number Z greater than 50, such as gadolinium (Gd) and iodine (I), embedded in a 10 cm thick soft tissue phantom. The spatial resolution achieved was 1.5 mm, with a radiation dose comparable to that of a standard computed tomography (CT) scan. This level of detail and penetration capability are crucial for biomedical applications, such as detecting contrast-enhanced tumors or characterizing metallic implants, without the risks associated with radioisotopes. The next step will be to optimize the technique to further reduce the dose and improve image acquisition speed, bringing it closer to clinical application.

Coexistence of high-temperature superconductivity and antiferromagnetism in a cuprate

A recent study has revealed the coexistence of high-temperature superconductivity and antiferromagnetic order in a cuprate, a type of material known for its unusual superconducting properties. This finding is significant because these two phases have traditionally been thought to compete, with antiferromagnetism suppressing superconductivity. The observation was made in a cuprate with multiple hole Fermi pockets, a feature that could be key to understanding this coexistence. Cuprates are ceramic materials that exhibit superconductivity at much higher temperatures than conventional superconductors, though still below room temperature. The exact nature of their superconductivity, and its relationship with other electronic phases such as antiferromagnetism, remains one of the major unresolved problems in condensed matter physics. This work provides a new perspective by demonstrating that, under certain conditions, these phases can coexist rather than being mutually exclusive. The study focused on characterizing the electronic and magnetic properties of the material, using techniques that allowed probing the electronic band structure and magnetic order at a microscopic level. The presence of multiple hole Fermi pockets suggests a complexity in the Fermi surface that could facilitate the interaction between magnetic fluctuations and Cooper pairs, the charge carriers in superconductors. This result challenges previous models that predicted a strict separation between superconducting and antiferromagnetic phase regions in the cuprate phase diagram. The implications of this discovery are profound for the understanding of high-temperature superconductivity. If coexistence is a more general feature than previously thought, it could open new avenues for designing materials with improved superconducting properties. Future research will focus on exploring the conditions under which this coexistence is stable and whether it can be manipulated to optimize superconducting properties in these complex systems.

Targeted Polar Entropy Regulation Improves Energy Storage in Capacitors

Researchers have developed a new method to enhance the energy storage density in multilayer ceramic capacitors, a crucial advancement for power electronics. The study, published in Nature, focuses on regulating polar entropy, a concept describing the disorder of electric dipoles within a ferroelectric material. By controlling this disorder, scientists significantly increased the amount of energy these devices can store and release efficiently. The key to this success lies in engineering tungsten bronze materials, specifically strontium barium niobate (SBN), with a multilayer structure. Through a composition modulation process, internal interfaces were created that act as barriers to the propagation of ferroelectric domains. These barriers allow for a higher density of reversible dipoles, which translates into increased energy storage capacity. The precise control of polar entropy at these interfaces reduces energy losses during charging and discharging. The results demonstrate an energy density of 115 joules per cubic centimeter (J/cm³) with an efficiency of 90% at 500 MV/m, a value remarkably superior to conventional ceramic capacitors. This breakthrough has significant implications for applications requiring high power density and miniaturization, such as power converters for electric vehicles, pulsed power storage devices, and next-generation consumer electronics. The ability to regulate polar entropy offers a new pathway for designing high-performance dielectric materials.

Electromagnetic Wave Interference in Microorganism Inactivation on Reflective Surfaces

A recent study investigated the impact of electromagnetic wave interference on the effectiveness of microorganism inactivation on reflective surfaces. The research focused on how the interaction between incident and reflected waves can modify the distribution of electromagnetic energy, directly influencing the ability of these waves to eliminate pathogens. This phenomenon is crucial for optimizing the design of disinfection systems that employ electromagnetic radiation, such as ultraviolet (UV-C) light, in environments with highly reflective surfaces. Traditionally, microbial inactivation by electromagnetic waves has been modeled assuming a uniform or predictable energy distribution. However, in the presence of reflective surfaces, complex interference patterns are generated, which can create areas with significantly higher or lower field intensities than the average. These local variations in radiation intensity can lead to inefficient disinfection in some areas (low-intensity zones) and suboptimal energy use in others (high-intensity zones). Understanding and controlling these patterns is essential to ensure complete and energy-efficient disinfection. The findings of this research have direct implications for the development of more advanced disinfection technologies. By considering wave interference, it is possible to design systems that manipulate radiation propagation to maximize microorganism exposure to lethal doses, even in complex geometries or with reflective materials. This could lead to more compact, faster, and lower-energy disinfection devices, applicable in hospital, industrial, or even water and air purification settings. Optimizing these systems requires precise modeling of the wave-surface-microorganism interaction.

Isogeny-based Post-Quantum Proxy Signature for IoT

Researchers have developed a new post-quantum proxy signature scheme specifically designed for Internet of Things (IoT) environments. This system is based on supersingular isogeny Diffie-Hellman (SIDH) elliptic curve cryptography, one of the most promising proposals for cryptographic security against quantum computer attacks. The proxy signature allows an original signer to delegate their signing capability to a proxy signer, who can then generate signatures on behalf of the original, a crucial functionality for managing devices and data in distributed, resource-constrained IoT networks. The main novelty of this work lies in adapting post-quantum security principles to the specificities of IoT, where devices often have significant constraints in terms of computational power, memory, and energy. The proposed scheme addresses these limitations by offering a balance between robust security and operational efficiency. The system's security is based on the computational difficulty of solving the supersingular elliptic curve isogeny problem, a problem believed to be intractable even for future quantum computers. This contrasts with current cryptographic algorithms, such as RSA or ECC, which are vulnerable to Shor's quantum algorithms. The implementation of this type of cryptography in IoT devices represents a significant step forward in ensuring data confidentiality and integrity in a future where quantum computing is a reality. The ability to securely and efficiently delegate signatures is vital for scenarios such as sensor authentication, device firmware updates, or transaction management in smart device networks. This advance contributes to building a more resilient IoT infrastructure prepared for the cryptographic challenges of the post-quantum era.

Integrated Air-Coupled Ultrasonic Transducer with Preamplification for Structural Monitoring

Researchers have developed an integrated air-coupled ultrasonic transducer that incorporates signal preamplification. This device is designed for structural health monitoring (SHM) of materials, offering a compact and efficient solution for non-contact defect detection. The integration of preamplification within the same package as the transducer significantly improves the signal-to-noise ratio, a critical factor in applications where sound attenuation in air is considerable. The main advance lies in this transducer's ability to emit and receive ultrasonic waves through the air, eliminating the need for liquid couplants or physical contact with the structure under inspection. This is particularly useful for evaluating composite materials, metals, and other structures in environments where contact methods are unfeasible or impractical. The optimized design of the transducer, coupled with low-noise preamplification, allows for the detection of small anomalies with greater sensitivity and reliability. This development has significant implications for predictive maintenance in various industries, such as aeronautics, automotive, and civil engineering. Continuous and non-invasive monitoring of structural integrity can prevent catastrophic failures, reduce inspection costs, and extend the lifespan of critical components. The miniaturization and energy efficiency of this transducer pave the way for its implementation in autonomous SHM systems, such as drones or inspection robots.

Pseudo point nodal superconducting gap in spin-triplet UTe2

Researchers have discovered the existence of a pseudo point nodal superconducting gap in the compound UTe2, an exotic material exhibiting spin-triplet superconductivity. This finding is crucial for understanding the fundamental nature of unconventional superconductivity and could have significant implications for the development of fault-tolerant quantum computing. Spin-triplet superconductivity, unlike the more common spin-singlet superconductivity, involves Cooper pairs of electrons with parallel spins. This configuration is of particular interest because it is predicted to support exotic topological states, such as Majorana fermions, which are their own antiparticles and are considered promising candidates for topological qubits. UTe2 is one of the few known materials to exhibit this rare form of superconductivity. The study focused on characterizing the structure of the superconducting gap, which is the minimum energy required to break a Cooper pair. The observation of pseudo point nodal points, regions in momentum space where the gap vanishes, is a distinctive signature of certain types of unconventional superconductivity and provides vital information about the symmetry of Cooper pairs in UTe2. This knowledge is a fundamental step towards understanding and manipulating the material's topological properties.

Driven-dissipative superconductivity in moiré heterostructures without attraction

Researchers have proposed a new protocol to induce superconductivity in two-dimensional moiré heterostructures, without relying on traditional attractive interactions. This method, termed driven-dissipative preparation, aims to establish superconductivity as a stationary state. The key lies in a bilayer moiré platform where the layer degree of freedom acts as a pseudospin, allowing the pseudospin structure required for pairing to be implemented through optically induced spatial operations. The scheme requires local dissipation, which naturally arises from weakly dispersive bosonic modes present in the heterostructure. In contrast, in the opposite regime of collective dissipation, the same platform exhibits an early-time superradiant burst. This approach represents an alternative to conventional superconductivity mechanisms, which generally rely on enhancing attractive interactions between electrons. The results of this research establish driven-dissipative moiré heterostructures as a promising platform for preparing superconductivity. Furthermore, the study reveals an unexpected connection between steady-state pairing and transient superradiance. This finding opens new avenues for exploring quantum phenomena and developing superconducting materials with properties controlled by light and dissipation.

Classical, AI, and Quantum Algorithms for the Maximum Clique Problem

The maximum clique problem is a fundamental challenge in graph theory and computer science, with broad applications in fields such as social network analysis, bioinformatics, and optimization. It involves finding the largest complete subgraph (the "clique") within a given graph. This problem is known for its computational complexity, being NP-complete, which means no efficient algorithm is known that can solve it in polynomial time for all cases. Traditionally, various classical algorithms have been developed to tackle this problem, ranging from exhaustive methods to heuristics and approximation algorithms. However, the combinatorial explosion inherent in large graphs limits the applicability of these solutions. The emergence of artificial intelligence (AI), particularly machine learning, has opened new avenues for developing more adaptive and efficient algorithms capable of handling the complexity of modern graph data. Recently, quantum computing has emerged as a promising frontier for solving NP-complete problems. Quantum algorithms like the Quantum Approximate Optimization Algorithm (QAOA) or approaches based on Quantum Annealing are being explored to find maximum cliques. Although current quantum computers are still limited in size and reliability, they offer the potential for exponential speedup in the future, surpassing the capabilities of classical and AI algorithms for particularly difficult problem instances. Current research focuses on how to integrate and compare these three methodologies to optimize the search for maximum cliques.

Magnetic spin and orbital Hall effects in centrosymmetric ferromagnets

Researchers have discovered the existence of magnetic spin Hall (SHE) and orbital Hall (OHE) effects in centrosymmetric ferromagnets. These effects, which involve the generation of spin or orbital angular momentum currents in response to an electric field, were previously thought to be exclusive to non-centrosymmetric materials or those with very strong spin-orbit interactions. This finding challenges conventional understanding and opens new avenues for manipulating spin and orbital currents in spintronic and orbitronic devices. Traditionally, spin and orbital Hall effects are associated with the breaking of inversion symmetry or strong spin-orbit interaction, which couples the electron's spin to its orbital motion. However, this study demonstrates that the presence of magnetic order in centrosymmetric materials is sufficient to induce these phenomena. This is because inversion symmetry is locally broken for spin-polarized electrons, even if the overall crystal structure maintains centrosymmetry. The research focused on ferromagnetic materials possessing this combination of properties. The implication of this discovery is significant for the development of new technologies. The ability to generate and manipulate spin and orbital currents in centrosymmetric ferromagnets simplifies device design, as these materials are often easier to fabricate and possess desirable magnetic properties. This could lead to advancements in magnetic memories, low-power logic, and sensors, by enabling more efficient control over spin and orbital angular momentum transport without relying on complex alloys or multilayer structures.

From stars to molecules: AI guides device-agnostic super-resolution imaging

A research team has developed a new super-resolution imaging method that uses artificial intelligence (AI) to enhance the quality of images obtained with various types of microscopes. This breakthrough allows overcoming the diffraction limit, a fundamental optical barrier that restricts the resolution of the finest details that can be observed. The technique, termed "device-agnostic," means it can be applied to a wide range of optical instruments, from astronomical telescopes to fluorescence microscopes, without requiring specific hardware modifications. The method is based on a deep learning algorithm trained with a diverse dataset of low-resolution images and their corresponding high-resolution versions. Once trained, the algorithm can infer and reconstruct fine details lost in the original images due to optical limitations. This is particularly useful in fields where obtaining high-resolution images is crucial but difficult, such as cell biology, materials science, and astrophysics. For instance, in microscopy, it allows visualizing subcellular structures with unprecedented clarity, while in astronomy, it could improve the sharpness of observations of distant celestial objects. The versatility of this approach lies in its ability to adapt to different optical systems and noise types, distinguishing it from other super-resolution techniques that often require very specific experimental setups or the addition of complex optical components. By being "device-agnostic," the AI acts as a universal post-processor, democratizing access to super-resolution imaging for laboratories with standard equipment. This development promises to accelerate discoveries across various disciplines by providing a powerful and flexible tool for visualizing structures at nanometer scales and beyond.

Sail membrane optomechanical accelerometers for new physics searches

Researchers have developed a new type of optomechanical accelerometer based on silicon nitride (Si$_3$N$_4$) membranes with a "sail-like" geometry that significantly enhances their performance. These devices combine a low resonant frequency with a high $Q \times \text{mass}$ product, crucial characteristics for detecting minute accelerations. The design, optimized using Bayesian techniques, allows for a reduction in resonant frequency by an order of magnitude while maintaining the quality of the $Q \times \text{mass}$ product, representing a significant advance in the dissipation engineering of these resonators. The developed accelerometers, with a centimeter scale, operate at kilohertz frequencies, achieving quality factors $Q \sim 10^7$ and $Q \times \text{mass} \sim 10 \text{ g}$ products. By vertically integrating a 7 kHz device with a nanoribbon, scientists achieved a monolithic cavity optomechanical accelerometer. This sensor exhibits a room temperature thermal noise of $40 \text{ n}g_0/\sqrt{\text{Hz}}$, which is sufficient to resolve ambient vibrations of the order of $μg_0/\sqrt{\text{Hz}}$ over a 4 kHz bandwidth, with a displacement imprecision of $10^{-14} \text{ m}/\sqrt{\text{Hz}}$. The key to this improvement lies in the optimization of the membrane geometry, transitioning from conventional strained resonators to "trampoline-like" structures with a sail shape. This design enables more efficient dissipation dilution, leading to higher sensitivity and stability. The ability of these accelerometers to detect extremely small accelerations makes them promising candidates for various applications. In the future, the creation of cryogenic arrays of these sail membranes could open new avenues for the search for "new physics" phenomena beyond the Standard Model and for distributed quantum sensing experiments. The combination of high sensitivity, low noise, and potential for scalability makes these devices an attractive platform for fundamental and technological research in quantum metrology.

Physics World's 2026 Instrumentation & Vacuum Briefing is now available

Physics World has released its annual "Instrumentation & Vacuum Briefing" for 2026, a free-to-access document covering the latest innovations in scientific instrumentation. The report highlights advancements in various fields, including the application of International System (SI) units, the development of quantum sensors, compact particle acceleration, and significant improvements in radiotherapy techniques. This compendium offers an overview of emerging trends and key technologies expected to shape research and industry in the coming years. The inclusion of quantum sensors underscores the increasing relevance of quantum mechanics in the design of high-precision devices, while compact particle accelerators promise new applications in medicine and materials science. Improvements in radiotherapy, meanwhile, reflect continuous progress in applying physics for more effective and less invasive medical treatments.

New Algorithm Inspired by Slime Mold Optimizes Transport on Graphs

Researchers have developed a new algorithm to solve the distributed optimal transport problem on graphs, drawing inspiration from the behavior of the slime mold, *Physarum polycephalum*. This single-celled organism is known for its ability to find efficient paths between food sources, forming networks of tubes that minimize transport costs. The algorithm mimics this biological process, iteratively adjusting flows in a network to achieve an optimal configuration. Optimal transport seeks the most efficient way to move resources between multiple sources and destinations, minimizing a total cost. It is a fundamental challenge in logistics, communication networks, and other fields. Traditional methods often require centralized computation and can be inefficient for very large or dynamic graphs. The *Physarum*-based approach offers a distributed solution, where each node in the network makes local decisions that, collectively, lead to a globally optimal solution. The algorithm operates by simulating a flow of "nutrients" through the edges of the graph. The conductance of each edge is adjusted based on the flow passing through it, analogous to how *Physarum* slime mold thickens tubes that carry more nutrients. This iterative process converges towards a flow distribution that minimizes the total transport cost. Results show that this method can be competitive with existing algorithms, especially in scenarios where decentralization and adaptability are crucial. This breakthrough has significant implications for the design of robust and efficient networks, from urban planning and energy distribution to route optimization in transportation and communication networks. The ability to solve these types of problems in a distributed manner opens the door to more resilient and scalable systems that can dynamically adapt to changes in demand or network topology, without relying on centralized control.

Bacterial turbulence drives interfacial waves and shape dynamics in phase-separated droplets

Researchers have discovered that the activity of bacterial colonies can induce turbulence, which in turn generates waves at the interface of immiscible liquid droplets. This phenomenon, observed in two-phase liquid systems, reveals a mechanism by which biological energy at the microscale can influence fluid dynamics and the morphology of soft structures. The study opens new avenues for understanding how biological systems interact with their physical environment at the level of complex fluids. The team used droplets composed of two immiscible liquids, one aqueous and one oily, into which active bacteria were introduced. The collective motility of the bacteria in the aqueous phase generated turbulent flows. These flows not only agitated the liquid but also exerted forces on the interface between the two phases, leading to the formation of waves and dynamic changes in the droplet's shape. The magnitude and pattern of these waves depended on bacterial density and the viscoelastic properties of the fluids. This finding is relevant to fields such as biophysics and soft materials engineering. Understanding how biological activity can shape interfaces and generate dynamic patterns in fluid systems is crucial for designing new active materials, optimizing bioremediation processes, or even modeling the formation of complex biological structures. The results suggest that microorganism-induced turbulence could be a key factor in the self-organization of biological systems at mesoscopic scales.

Dynamics of Laser-Induced Optical Switching in Silicon and GaAs

Researchers have explored the dynamics of laser-induced optical switching in semiconductors such as silicon (Si) and gallium arsenide (GaAs). The study focused on how spatially resolved charge carrier transport and density-dependent optical losses influence this process. These findings are crucial for understanding and optimizing high-speed photonic devices, which are fundamental in telecommunications and optical computing. Optical switching relies on modulating a material's optical properties using a control light pulse. In semiconductors, this involves generating charge carriers (electrons and holes) that alter the material's refractive index and absorption. The work has detailed how the diffusion of these carriers from the illuminated region and how free-carrier absorption, which increases with density, affect switching efficiency and speed. Traditionally, these effects have been modeled in a simplified manner, but this study underscores the need for a more detailed approach. Through an analysis incorporating transport models and density-dependent optical losses, scientists have achieved a more precise description of the observed phenomena. They have demonstrated that ignoring these factors can lead to a significant underestimation of switching times and suboptimal device optimization. The results provide a basis for designing faster and more efficient optical modulators, paving the way for future innovations in integrated photonics and optoelectronics.

New Macroscopic Theory Explains Vibrational Strong Coupling Effects

Researchers have developed a new theory describing the effects of vibrational strong coupling (VSC) in macroscopic systems. This phenomenon, where molecular vibrations hybridize with a photonic mode of an optical cavity, has been intensely studied due to its promising applications in modifying chemical and physical properties of materials. The new theory offers a unified framework to understand how VSC can influence chemical reactivity and conductivity, addressing the controversy over whether these effects are purely quantum or can be explained with a classical model. VSC arises when molecular vibrational transitions strongly interact with cavity photons, forming hybrid states known as vibrational polaritons. These polaritons possess characteristics of both matter and light, giving them unique properties. Until now, understanding how these hybrid states affect macroscopic properties, such as reaction rate or conductivity, has been incomplete. The proposed theory suggests that VSC effects can be explained through a macroscopic condensation of these polaritons, a concept analogous to Bose-Einstein condensation or superfluidity, but applied to a matter-light system. The key implication of this work is that effects observed under VSC, which have often been attributed to complex quantum phenomena, could have a more direct explanation at the macroscopic scale. This not only simplifies the interpretation of many experiments but also opens new avenues for designing materials with optimized properties. By better understanding the principles underlying this condensation, scientists could develop more efficient strategies for manipulating the chemistry and physics of materials through optical cavity engineering.

Composition-engineered dielectric resonator antennas for 5G/6G

Researchers have developed new dielectric resonator antennas (DRAs) based on composition-optimized strontium-barium titanate (SBT), demonstrating superior performance in 5G and 6G frequency bands. These antennas, utilizing the Sr1−xBaxTiO3 material, offer high efficiency and wide bandwidth, crucial characteristics for next-generation wireless communications. The breakthrough lies in the ability to fine-tune the material's composition to achieve specific dielectric properties, enabling easier miniaturization and integration into modern devices. The study focused on engineering the composition of SBT, varying the strontium-to-barium ratio to adjust the dielectric constant and quality factor. Experimental results show that antennas fabricated with this approach exhibit significantly improved radiation efficiency and a greater capacity to handle the high frequencies required by 5G and 6G networks. This development addresses the critical need for more compact and efficient antenna components that can operate in millimeter-wave bands, where signal losses and design challenges are more significant. Optimizing the dielectric properties of Sr1−xBaxTiO3 allows these DRAs to overcome the limitations of conventional antennas in terms of size and performance. By offering a solution that combines high efficiency, wide bandwidth, and a reduced form factor, this work lays the groundwork for the implementation of more advanced communication devices. These antennas are expected to facilitate the deployment of more robust 5G/6G infrastructures and the creation of new applications that rely on high-speed, low-latency wireless connectivity.

Sensory-driven neck–limb coordination in gait transitions

Researchers have uncovered neck–limb coordination mechanisms that are fundamental for gait transitions in mammals, such as the shift from walking to trotting or galloping. This study focuses on how sensory information, particularly neck proprioception, influences rhythmic locomotion and adaptation to different speeds and types of movement. The findings suggest that the central nervous system integrates signals from multiple sources to orchestrate these complex and fluid changes in movement patterns. The work addresses a fundamental question in neuroscience and biomechanics: how animals adjust their movement patterns to optimize efficiency and stability at different speeds. Traditionally, much attention has been paid to central pattern generators (CPGs) in the spinal cord for rhythmic locomotion. However, this study highlights the importance of sensory feedback, especially from the neck, in modulating these CPGs and determining gait transitions. The research employs advanced techniques to observe and manipulate neuronal and muscular activity, providing a detailed insight into sensorimotor interactions. Key results demonstrate that manipulating neck proprioceptive signals can induce or suppress gait transitions, even when other parameters like treadmill speed are kept constant. This indicates that the neck is not merely a passive stabilizer but an active center of sensory processing that contributes to decisions about locomotion patterns. The coordination between head and trunk movement, mediated by the neck, appears to be a critical factor for stability and energy efficiency during transitions. These findings open new avenues for understanding and potentially treating movement disorders in humans, as well as for designing more agile and adaptable biomimetic robots.

New Fault Diagnosis Method for Train Bogie Motors

Researchers have developed a novel multimodal method for diagnosing faults in train bogie motors, combining physics-inspired regularization with an enhanced convolutional neural network (ConvNeXt) architecture. This advancement is crucial for the safety and efficiency of railway transportation, as it enables the detection of anomalies in traction motors, critical components that operate under demanding conditions and are prone to complex, multifactorial failures. The proposed method addresses the limitations of traditional approaches, which often lack the ability to effectively integrate data from multiple sources or to capture the inherent complexity of physical systems. By incorporating physical principles into the regularization process, the model not only improves its generalization capability but also imbues the neural network with an intrinsic understanding of motor behavior. This results in more accurate and robust diagnostics, even in scenarios with incomplete or noisy data. The enhanced ConvNeXt architecture, adapted to process multimodal data (such as vibration, current, and temperature signals), allows for more efficient extraction of relevant features. The integration of physics-inspired regularization acts as a bridge between deep learning and physical models, optimizing the detection of subtle patterns that indicate the onset of a fault. Preliminary results show a significant improvement in diagnostic accuracy and reliability compared to existing methods. This development has direct implications for predictive maintenance in the railway industry, enabling earlier interventions and reducing unplanned downtime. The ability to predict and locate faults with greater anticipation and precision not only optimizes operational costs but also raises safety standards for passengers and cargo. Future research is expected to explore the application of this approach to other complex mechanical systems and its validation in large-scale operational environments.

Machine Learning Enhances Defect Calculation in Amorphous Silicon Dioxide

Researchers have developed a new machine learning-based method to accurately calculate the formation energies of oxygen vacancies in amorphous silicon dioxide (SiO₂). This breakthrough is crucial because oxygen vacancies are fundamental atomic defects that affect the electrical and optical properties of this ubiquitous material in electronics. The traditional approach, based on density functional theory (DFT), is computationally very expensive for large and complex amorphous systems, limiting the understanding of these defects. The team trained a machine learning model to predict vacancy formation energies using a database of high-fidelity DFT calculations. This model, termed a "machine learning Hamiltonian," allows for the simulation of much larger systems with greater structural diversity than those accessible with direct DFT. The key lies in its ability to capture complex atomic interactions and local variations in the amorphous structure, which are difficult to model with classical methods. Results show that the machine learning method not only accurately reproduces DFT-obtained formation energies for known configurations but also allows for the exploration of a much broader configuration space. This has revealed a significantly wider distribution of oxygen vacancy formation energies than previously thought, with direct implications for the stability and functionality of SiO₂-based devices. The computational efficiency of the new method is orders of magnitude superior to DFT, paving the way for large-scale simulations. This advance is fundamental for materials engineering, as a detailed understanding of defects in SiO₂ is essential for optimizing the fabrication of transistors, memories, and other microelectronic components. The ability to accurately predict how defects affect material properties will enable the design of devices with enhanced performance and reliability. The next steps include applying this method to other types of defects and amorphous materials, as well as exploring its impact on the dynamic properties of these systems.

New Star-Patterned Antenna Enhances Circular Polarization and Gain

Researchers have developed a novel microstrip antenna that utilizes a star-patterned frequency selective surface (FSS) to significantly improve circular polarization, gain, and impedance matching. This innovative design addresses the limitations of conventional antennas in applications requiring robust circular polarization, such as satellite communications, radar systems, and RFID technology. The integration of the FSS with a multi-step notched antenna allows for more precise control over the emitted and received electromagnetic wave characteristics. The key to this advancement lies in the FSS configuration, which acts as a spatial filter for electromagnetic waves. The star pattern not only contributes to better impedance matching, reducing reflection losses, but also plays a crucial role in converting linear to circular polarization. This approach enables the antenna to maintain optimal performance over a wider frequency range and with higher efficiency, which is essential for communication systems operating in complex environments or with high bandwidth requirements. The results obtained with this antenna demonstrate notable improvements in axial ratio (AR) and gain. A low axial ratio is indicative of pure circular polarization, minimizing signal fading due to misalignment between transmitting and receiving antennas. The increased gain, in turn, translates to extended communication range and enhanced signal reliability. This development represents a step forward in the design of compact, high-performance antennas, with the potential to impact various wireless technologies.

Analysis of Energy Transfer Mechanism in Particle Dampers

Researchers have conducted a comprehensive analysis and experimental verification of the energy transfer mechanism in particle dampers, devices used for vibration reduction. These dampers operate by dissipating vibrational energy through inelastic collisions between particles contained within a cavity, as well as by friction. The study's objective was to better understand how energy is transferred and dissipated within these systems, which is crucial for optimizing their design and performance in various engineering applications. The study focused on characterizing key parameters influencing the effectiveness of particle dampers, such as particle size, shape, and material, cavity geometry, and input vibration characteristics. Through a combination of theoretical modeling and controlled experiments, scientists were able to quantify the relative contribution of collisions and friction to energy dissipation. The results provide a solid foundation for predicting the behavior of these dampers and for developing more efficient designs that can mitigate vibrations in mechanical, aerospace, and civil structures. This advance has significant implications for fields where vibration control is critical, from protecting sensitive equipment to improving comfort and safety in vehicles and buildings. A detailed understanding of energy transfer mechanisms will enable engineers to design particle dampers with greater precision, adapting them to specific frequency and amplitude ranges of vibration. Future research is expected to explore the application of these principles to new materials and configurations, further expanding the scope of this technology.

New Silicone Composites for Broad-Spectrum Electromagnetic Shielding

Researchers have developed a novel silicone-based composite material that offers significantly improved electromagnetic interference (EMI) shielding performance across a wide frequency range, from 8.2 GHz to 18 GHz. This breakthrough is crucial for protecting electronic devices from interference and for applications in high electromagnetic radiation environments. The material combines a silicone matrix with CaCu₃Ti₄O₁₂ (CCTO), CoFe₂O₄ (CFO) particles, and aluminum (Al) powder, leveraging the dielectric and magnetic properties of the ceramic oxides along with the high conductivity of the metal. The study focused on optimizing the composition to maximize shielding effectiveness. It was observed that the addition of Al to the CCTO/CFO/silicone composite drastically increases the reflectivity and electrical conductivity of the material, which is fundamental for electromagnetic wave attenuation. The primary shielding mechanism in these composites is reflection, where incident waves bounce off the material's surface due to the presence of free charges and magnetic dipoles. However, significant absorption also occurs, where wave energy is dissipated as heat within the material. Experimental results showed that the composite with an optimal proportion of Al achieves a total shielding effectiveness (SET) of up to 43.2 dB at 18 GHz. This means the material can attenuate the power of an electromagnetic wave by more than 99.99%. This performance surpasses many existing shielding materials and is comparable to other advanced composites, but with the advantage of silicone's flexibility and lightness. The ability to tune dielectric and magnetic properties by combining CCTO and CFO, along with Al's high conductivity, allows for fine-tuning of shielding performance. This development paves the way for creating more efficient and versatile EMI shields for a variety of applications, including consumer electronics, 5G and 6G telecommunications, aerospace, and defense industries. The flexibility of the silicone matrix allows for the fabrication of lightweight shields adaptable to different geometries, which is a significant advantage over traditional rigid metallic shields. Future research could explore the integration of these composites into more complex structures or the optimization of interfaces between fillers to further enhance wave scattering and absorption.

New ultra-high gain DC/DC converter with low voltage and current stresses

Researchers have developed a new direct current to direct current (DC/DC) converter based on a CI-type (Impedance Converter) circuit, notable for its ability to offer ultra-high voltage gain. This innovative design addresses a common limitation in traditional DC/DC converters, which often require multiple stages or complex components to achieve high voltage conversion ratios, increasing losses and device size. The proposed architecture allows for efficient conversion of low input voltages to significantly higher output voltages, which is crucial for various electronic applications. One of the most remarkable features of this new converter is its ability to operate with low voltages and currents across its key components. This translates into a significant reduction in electrical stress on semiconductors and other circuit elements, which not only improves the reliability and lifespan of the device but also minimizes energy losses associated with switching and conduction. Mitigating these high voltages and currents is a constant challenge in the design of high-gain converters, and overcoming it represents an important advance in the efficiency and robustness of these systems. The design focuses on an optimized topology that uses a reduced number of components, contributing to lower cost and a more compact size compared to existing solutions of similar performance. The implementation of this converter could have a considerable impact on fields such as power electronics, renewable energy systems (where it is necessary to boost voltage from solar panels or wind turbines), and electric vehicles, where energy conversion efficiency is a critical factor. Next steps will include thorough experimental validation and parameter optimization for specific applications, as well as evaluating its performance under variable load conditions.

Optimized Single-Axis MEMS Capacitive Accelerometer Using Wet Etching

Researchers have developed a single-axis MEMS (Micro-Electro-Mechanical Systems) capacitive accelerometer, optimized to enhance noise stability. This advancement focuses on applying wet etching techniques in the device's fabrication, allowing for greater precision in defining micrometric structures and, consequently, a significant reduction in noise sources that affect acceleration measurement. The design incorporates a differential capacitive architecture that is intrinsically robust against temperature variations and other environmental disturbances, which is crucial for high-precision applications. The fabrication process utilizes wet etching, a technique that offers greater selectivity and control over the geometry of structures compared to conventional dry etching methods. This translates into a reduction of residual stresses and surface defects, factors that directly contribute to the intrinsic noise of the sensor. The optimization of the geometric design of the electrodes and suspensions, along with improved etching uniformity, has enabled the achievement of superior sensitivity and noise stability compared to devices manufactured with standard processes. Experimental results demonstrate that the optimized accelerometer exhibits improved noise stability, with a significantly reduced standard deviation of the output signal. This performance makes it suitable for applications requiring high-fidelity acceleration measurements, such as inertial navigation systems, structural monitoring, and vibration control in industrial and aerospace environments. The ability to reproducibly and cost-effectively manufacture these devices using MEMS processes opens new avenues for integrating high-precision sensors into a wide range of systems.

Extensile and contractile biological tissue models could be indistinguishable

A new theoretical study has revealed that, under certain conditions, the mechanical properties of extensile and contractile biological tissues could be indistinguishable. This ambiguity arises when analyzing density fluctuations in these materials, which could have significant implications for understanding biological processes such as embryonic development or wound healing, where distinguishing between these behaviors is crucial. Active biological tissues, such as those comprising muscles or epithelia, exhibit complex mechanical behaviors driven by molecular motors that consume energy. These motors can generate forces that extend the tissue (extensile behavior) or contract it (contractile behavior). Traditionally, it has been assumed that these two classes of materials behave fundamentally differently and can be easily distinguished by measuring their elastic properties or their response to external perturbations. However, the current research, based on mean-field models, suggests that this distinction might not be as clear-cut as previously thought. The authors have shown that the signature of density fluctuations, a key measure of the material's response to small variations, can be identical for extensile and contractile tissues in certain regions of the parameter space. This means that experimental measurements of these fluctuations alone might not be sufficient to determine whether a biological tissue is predominantly extending or contracting, posing a challenge for the characterization of these systems. This finding underscores the need to develop more sophisticated characterization methods that can unravel the underlying nature of active forces in biological tissues.

Programmable Ising Machine Achieves High-Speed Combinatorial Optimization

Researchers have developed an FPGA-based Ising machine that tackles combinatorial optimization problems with unprecedented speed and efficiency. This device is capable of solving complex problems in a natively sparse manner, meaning it can handle data structures where most elements are zero, a common characteristic in many real-world optimization challenges. The FPGA-based architecture allows for significant reconfigurability and parallelization, overcoming the limitations of general-purpose solutions and approaching the performance of specialized hardware accelerators. Combinatorial optimization is fundamental in fields ranging from logistics and planning to drug design and artificial intelligence. Problems such as the traveling salesman problem or resource allocation are NP-hard, meaning their solution time grows exponentially with problem size for classical algorithms. Ising machines, which model these problems as finding the minimum energy state of a spin system, offer a promising avenue for finding approximate solutions efficiently. This particular advance is distinguished by its ability to process sparsity natively, which reduces computational complexity and resource consumption. The system demonstrates remarkable performance compared to other platforms. Its design allows for an efficient implementation of simulated annealing algorithms and other physics-inspired optimization methods. By exploiting the sparse nature of many real-world problems, the programmable Ising machine can allocate its computational resources more effectively, avoiding unnecessary calculations and accelerating the convergence process towards optimal or near-optimal solutions. This approach opens new possibilities for addressing problems that were previously intractable due to their scale or complexity.

Modeling of Underwater Acoustic Beams Using Piezoelectric Transducers

A recent study investigated the influence of surface geometry on underwater acoustic beam shaping using piezoelectric transducers. The research combined experimental and computational evaluations to understand how variations in the transducer's surface shape affect the directionality and intensity of sound propagated in water. This work is fundamental for optimizing the design of sonar systems and other underwater communication and detection applications. The researchers employed piezoelectric transducers, devices that convert electrical energy into acoustic energy and vice versa, to generate sound waves. Different geometric configurations of the transducer surfaces, from flat to complex curves, were analyzed to determine their impact on beam shaping capability. Experimental results were complemented by detailed computational simulations, which allowed for modeling acoustic wave propagation and predicting beam behavior under various conditions. The findings demonstrate that surface geometry plays a critical role in the efficiency and precision of acoustic beam modeling. Optimizing these geometries can lead to greater sound focusing, reduced dispersion, and an improved signal-to-noise ratio in underwater environments. This has direct implications for the development of more advanced technologies in areas such as seafloor mapping, submerged object detection, and high-speed underwater communications.