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Topological materials provide a platform that utilizes the geometric characteristics of structured materials to control the flow of waves, enabling unidirectional and protected transmission that is immune to defects or impurities. The topologically designed photonic materials can carry quantum states and electromagnetic energy, benefiting nanolasers or quantum photonic systems. This article reviews recent advances in the topological applications of photonic materials for radiative heat transfer, especially in the near field. When the separation distance between media is considerably smaller than the thermal wavelength, the heat transfer exhibits super-Planckian behavior that surpasses Planck's blackbody predictions. Near-field thermal radiation in subwavelength systems supporting surface modes has various applications, including nanoscale thermal management and energy conversion. Photonic materials and structures that support topological surface states show immense potential for enhancing or suppressing near-field thermal radiation. We present various topological effects, such as periodic and quasi-periodic nanoparticle arrays, Dirac and Weyl semimetal-based materials, structures w

The development of radiation-tolerant materials capable of maintaining structural, electrical, and thermal stability in extreme, radiation-rich environments remains a critical challenge in materials science. In this work, the effects of 60 MeV 35Cl ion irradiation on highly oriented pyrolytic graphite (HOPG) and multilayer reduced graphene oxide (ML-rGO) were investigated. The samples were exposed to fluences of 5.11 x 10^9 and 1.3 x 10^10 ions/cm^2 and characterized by X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and electrical transport measurements. The results show that the irradiation response is strongly influenced by the initial structural organization of the material. In HOPG, ion exposure leads to a progressive loss of crystalline order, evidenced by XRD peak broadening and an increase in the Raman ID/IG ratio, accompanied by a reduction in electrical transport performance. In contrast, ML-rGO exhibits distinct behavior at higher fluences, suggesting partial structural reorganization. The appearance of more defined graphitic features in XRD and Raman analyses, along with changes in surface morphology and el

We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framework leverages ElementBERT, a domain-specific BERT-based natural language processing model trained on 1.29 million abstracts of alloy-related scientific papers, to capture latent knowledge and contextual relationships specific to alloys. These semantic embeddings serve as robust elemental descriptors, consistently outperforming traditional empirical descriptors with significant improvements across multiple downstream tasks. These include predicting mechanical and transformation properties, classifying phase structures, and optimizing materials properties via Bayesian optimization. Applications to titanium alloys, high-entropy alloys, and shape memory alloys demonstrate up to 23% gains in prediction accuracy. Our results show that ElementBERT surpasses general-purpose BERT variants by encoding specialized alloy knowledge. By bridging contextual insights from scientific literature with quantitative inference, our framework accelerates the discovery and optimization of advanced materials, with potential applications extending beyond alloys to

In the context of quantum thermodynamics, quantum batteries have emerged as promising devices for energy storage and manipulation. Over the past decade, substantial progress has been made in understanding the fundamental properties of quantum batteries, with several experimental implementations showing great promise. This Perspective provides an overview of the solid-state materials platforms that could lead to fully operational quantum batteries. After briefly introducing the basic features of quantum batteries, we discuss organic microcavities, where superextensive charging has already been demonstrated experimentally. We then explore other materials, including inorganic nanostructures (such as quantum wells and dots), perovskite systems, and (normal and high-temperature) superconductors. Key achievements in these areas, relevant to the experimental realization of quantum batteries, are highlighted. We also address challenges and future research directions. Despite their enormous potential for energy storage devices, research into advanced materials for quantum batteries is still in its infancy. This paper aims to stimulate interdisciplinarity and convergence among different mate

Terahertz communications are envisioned as a key enabler for 6G networks. The abundant spectrum available in such ultra high frequencies has the potential to increase network capacity to huge data rates. However, they are extremely affected by blockages, to the point of disrupting ongoing communications. In this paper, we elaborate on the relevance of predicting visibility between users and access points (APs) to improve the performance of THz-based networks by minimizing blockages, that is, maximizing network availability, while at the same time keeping a low reconfiguration overhead. We propose a novel approach to address this problem, by combining a neural network (NN) for predicting future user-AP visibility probability, with a probability threshold for AP reselection to avoid unnecessary reconfigurations. Our experimental results demonstrate that current state-of-the-art handover mechanisms based on received signal strength are not adequate for THz communications, since they are ill-suited to handle hard blockages. Our proposed NN-based solution significantly outperforms them, demonstrating the interest of our strategy as a research line.

This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo fine-tuned with the ap

Two-dimensional (2D) magnetism in atomically thin van der Waals (vdW) monolayers and heterostructures has attracted significant attention due to its promising potential for next-generation spintronic and quantum technologies. A key factor in stabilizing long-range magnetic order in these systems is magnetic anisotropy, which plays a crucial role in overcoming the limitations imposed by the Mermin-Wagner theorem. This review provides a comprehensive theoretical and experimental overview of the importance of magnetic anisotropy in enabling intrinsic 2D magnetism and shaping the electronic, magnetic, and topological properties of 2D vdW materials. We begin by summarizing the fundamental mechanisms that determine magnetic anisotropy, emphasizing the contributions from strong ligand spin-orbit coupling of ligand atoms and unquenched orbital magnetic moments. We then examine a range of material engineering approaches, including alloying, doping, electrostatic gating, strain, and pressure, that have been employed to effectively tune magnetic anisotropy in these materials. Finally, we discuss open challenges and promising future directions in this rapidly advancing field. By presenting a b

Symbiotic radio (SR), a novel energy- and spectrum-sharing paradigm of backscatter communications (BC), has been deemed a promising solution for ambient Internet of Things (A-IoT), enabling ultra-low power consumption and massive connectivity. However, A-IoT nodes utilizing BC suffer from low transmission rates, which may limit the applications of SR in A-IoT scenarios with data transmission requirements. To address this issue, in this article, we introduce hybrid active-passive communications (HAPC) into SR by exploiting tradeoffs between transmission rate and power consumption. We first present an overview of novel BC paradigms including ambient BC and SR. Then, a novel HAPC-enabled SR is proposed to enhance the transmission rate of A-IoT nodes. Furthermore, within this paradigm, we investigate the resource allocation scheme and present preliminary research results. Simulation results show that the transmission rate of A-IoT nodes in the proposed HAPC-enabled SR surpasses that in traditional SR. Finally, we discuss open issues related to HAPC-enabled SR.

This letter investigates channel estimation for ultra-massive multiple-input multiple-output (MIMO) communications. We propose a joint low-rank and sparse Bayesian estimation (LRSBE) algorithm for spatial non-stationary ultra-massive channels by exploiting the low-rankness and sparsity in the beam domain. Specifically, the channel estimation integrates sparse Bayesian learning and soft-threshold gradient descent within the expectation-maximization framework. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art alternatives under different signal-to-noise ratio conditions in terms of estimation accuracy and overall complexity.

Integrated communications and localization (ICAL) will play an important part in future sixth generation (6G) networks for the realization of Internet of Everything (IoE) to support both global communications and seamless localization. Massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems have great potential in providing wide coverage with enhanced gains, and thus are strong candidates for realizing ubiquitous ICAL. In this paper, we develop a wideband massive MIMO LEO satellite system to simultaneously support wireless communications and localization operations in the downlink. In particular, we first characterize the signal propagation properties and derive a localization performance bound. Based on these analyses, we focus on the hybrid analog/digital precoding design to achieve high communication capability and localization precision. Numerical results demonstrate that the proposed ICAL scheme supports both the wireless communication and localization operations for typical system setups.

Prior to the era of artificial intelligence and big data, wireless communications primarily followed a conventional research route involving problem analysis, model building and calibration, algorithm design and tuning, and holistic and empirical verification. However, this methodology often encountered limitations when dealing with large-scale and complex problems and managing dynamic and massive data, resulting in inefficiencies and limited performance of traditional communication systems and methods. As such, wireless communications have embraced the revolutionary impact of artificial intelligence and machine learning, giving birth to more adaptive, efficient, and intelligent systems and algorithms. This technological shift opens a road to intelligent information transmission and processing. This overview article discusses the typical roles of machine learning in intelligent wireless communications, as well as its features, challenges, and practical considerations.

Comment on six papers published by M.A. El-Hakiem and his co-workers in International Communications in Heat and Mass Transfer, Journal of Magnetism and Magnetic Materials and Heat and Mass Transfer

We propose a low-power mobile low earth orbit (LEO) satellite communication architecture, employing double reconfigurable intelligent surfaces (RIS) to enhance energy efficiency and signal performance. With a distance between RISs that satisfies the far-field requirement, this architecture positions one small RIS each in the near-field of the satellite's antenna and the user on the ground. Moreover, we develop a path loss model for the double-RIS communication link, considering the near-field and far-field effects. Further, with the help of dual-stage beamforming, the proposed system maximizes the signal power and minimizes power consumption. Simulation results show that the proposed architecture can reduce the power consumption with 40 dB in the uplink, with a small $0.25^2$ $\text{m}^2$ RIS near the user, to communicate in energy-constrained LEO satellite communication circumstances.

This review presents recent breakthroughs in the realm of nonlinear Hall effects, emphasizing central theoretical foundations and recent experimental progress. We elucidate the quantum origin of the second-order Hall response, focusing on the Berry curvature dipole, which may arise in inversion symmetry broken systems. The theoretical framework also reveals the impact of disorder scattering effects on the nonlinear response. We further discuss the possibility of obtaining nonlinear Hall responses beyond the second order. We examine symmetry-based indicators essential for the manifestation of nonlinear Hall effects in time-reversal symmetric crystals, setting the stage for a detailed exploration of theoretical models and candidate materials predicted to exhibit sizable and tunable Berry curvature dipole. We summarize groundbreaking experimental reports on measuring both intrinsic and extrinsic nonlinear Hall effects across diverse material classes. Finally, we highlight some of the other intriguing nonlinear effects, including nonlinear planar Hall, nonlinear anomalous Hall, and nonlinear spin and valley Hall effects. We conclude with an outlook on pivotal open questions and challen

Berry curvature physics and quantum geometric effects have been instrumental in advancing topological condensed matter physics in recent decades. Although Landau level-based flat bands and conventional 3D solids have been pivotal in exploring rich topological phenomena, they are constrained by their limited ability to undergo dynamic tuning. In stark contrast, moiré systems have risen as a versatile platform for engineering bands and manipulating the distribution of Berry curvature in momentum space. These moiré systems not only harbor tunable topological bands, modifiable through a plethora of parameters, but also provide unprecedented access to large length scales and low energy scales. Furthermore, they offer unique opportunities stemming from the symmetry-breaking mechanisms and electron correlations associated with the underlying flat bands that are beyond the reach of conventional crystalline solids. A diverse array of tools, encompassing quantum electron transport in both linear and non-linear response regimes and optical excitation techniques, provide direct avenues for investigating Berry physics. This review navigates the evolving landscape of tunable moiré materials, hig

Advances in scaling down heterostructures and having an improved interface quality together with atomically-thin two-dimensional materials suggest a novel approach to systematically design materials. A given material can be transformed through proximity effects whereby it acquires properties of its neighbors, for example, becoming superconducting, magnetic, topologically nontrivial, or with an enhanced spin-orbit coupling. Such proximity effects not only complement the conventional methods of designing materials by doping or functionalization, but can also overcome their various limitations. In proximitized materials it is possible to realize properties that are not present in any constituent region of the considered heterostructure. While the focus is on magnetic and spin-orbit proximity effects with their applications in spintronics, the outlined principles provide also a broader framework for employing other proximity effects to tailor materials and realize novel phenomena.

Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime. However, resource allocation for semantic communications still remains unexplored, which is a critical issue in guaranteeing the semantic transmission reliability and the communication efficiency. To fill this gap, we investigate the spectral efficiency in the semantic domain and rethink the semantic-aware resource allocation issue. Specifically, taking text semantic communication as an example, the semantic spectral efficiency (S-SE) is defined for the first time, and is used to optimize resource allocation in terms of channel assignment and the number of transmitted semantic symbols. Additionally, for fair comparison of semantic and conventional communication systems, a transform method is developed to convert the conventional bit-based spectral efficiency to the S-SE. Simulation results demonstrate the validity and feasibility of the proposed resource allocation method, as well as the superiority of semantic communications in terms of the S-SE.

Background Since at least 400 C.E., when the Mayans first used layered clays to make dyes, people have been harnessing the properties of layered materials. This gradually developed into scientific research, leading to the elucidation of the laminar structure of layered materials, detailed understanding of their properties, and eventually experiments to exfoliate or delaminate them into individual, atomically thin nanosheets. This culminated in the discovery of graphene, resulting in a new explosion of interest in two-dimensional materials. Layered materials consist of two-dimensional platelets weakly stacked to form three-dimensional structures. The archetypal example is graphite, which consists of stacked graphene monolayers. However, there are many others: from MoS 2 and layered clays to more exotic examples such as MoO 3 , GaTe, and Bi 2 Se 3 . These materials display a wide range of electronic, optical, mechanical, and electrochemical properties. Over the past decade, a number of methods have been developed to exfoliate layered materials in order to produce monolayer nanosheets. Such exfoliation creates extremely high-aspect-ratio nanosheets with enormous surface area, which are ideal for applications that require surface activity. More importantly, however, the two-dimensional confinement of electrons upon exfoliation leads to unprecedented optical and electrical properties. Advances An important advance has been the discovery that layered crystals can be exfoliated in liquids. There are a number of methods to do this that involve oxidation, ion intercalation/exchange, or surface passivation by solvents. However, all result in liquid dispersions containing large quantities of nanosheets. This brings considerable advantages: Liquid exfoliation allows the formation of thin films and composites, is potentially scaleable, and may facilitate processing by using standard technologies such as reel-to-reel manufacturing. Although much work has focused on liquid exfoliation of graphene, such processes have also been demonstrated for a host of other materials, including MoS 2 and related structures, layered oxides, and clays. The resultant liquid dispersions have been formed into films, hybrids, and composites for a range of applications. Outlook There is little doubt that the main advances are in the future. Multifunctional composites based on metal and polymer matrices will be developed that will result in enhanced mechanical, electrical, and barrier properties. Applications in energy generation and storage will abound, with layered materials appearing as electrodes or active elements in devices such as displays, solar cells, and batteries. Particularly important will be the use of MoS 2 for water splitting and metal oxides as hydrogen evolution catalysts. In addition, two-dimensional materials will find important roles in printed electronics as dielectrics, optoelectronic devices, and transistors. To achieve this, much needs to be done. Production rates need to be increased dramatically, the degree of exfoliation improved, and methods to control nanosheet properties developed. The range of layered materials that can be exfoliated must be expanded, even as methods for chemical modification must be developed. Success in these areas will lead to a family of materials that will dominate nanomaterials science in the 21st century.

Electron-electron ($e$-$e$) and electron-phonon ($e$-ph) interactions are challenging to describe in correlated materials, where their joint effects govern unconventional transport, phase transitions, and superconductivity. Here we combine first-principles $e$-ph calculations with dynamical mean field theory (DMFT) as a step toward a unified description of $e$-$e$ and $e$-ph interactions in correlated materials. We compute the $e$-ph self-energy using the DMFT electron Green's function, and combine it with the $e$-$e$ self-energy from DMFT to obtain a Green's function including both interactions. This approach captures the renormalization of quasiparticle dispersion and spectral weight on equal footing. Using our method, we study the $e$-ph and $e$-$e$ contributions to the resistivity and spectral functions in the correlated metal Sr$_2$RuO$_4$. In this material, our results show that $e$-$e$ interactions dominate transport and spectral broadening in the temperature range we study (50$-$310~K), while $e$-ph interactions are relatively weak and account for only $\sim$10\% of the experimental resistivity. We also compute effective scattering rates, and find that the $e$-$e$ interacti