The escalating density of electronic devices and wireless technologies has heightened the need for effective electromagnetic interference (EMI) shielding solutions. Conventional polymer-based composites face persistent limitations due to the insulating nature of polymers and poor compatibility between hydrophilic conductive fillers like MXenes and hydrophobic polymer matrices. Here, we report a strategy to overcome this challenge by biaryl-functionalizing Ti 3 C 2 T x MXene with 1,1′-bi-2-naphthol (BINOL), yielding BINOL-grafted MXene (BIMX) with markedly enhanced dispersibility in a wide range of industrial organic solvents. The resulting BIMX demonstrates excellent compatibility with thermoplastic polyurethane (TPU) and enables the scalable fabrication of nanocomposites through simple solution mixing and blade coating techniques. BIMX/TPU composites exhibit a remarkably low electrical percolation threshold of 0.3 vol% and high electrical conductivity, achieving outstanding EMI shielding effectiveness across X-, Ka-, and W-band frequencies. Structural and spectroscopic analyses confirm the successful grafting and homogeneous dispersion of BIMX within polymer matrices. Furthermore, BIMX is compatible with multiple thermoplastic polymers, enabling versatile form factors for practical applications. This work presents a scalable, solvent-compatible pathway for engineering high-performance, flexible EMI shielding materials, offering a viable solution for next-generation electronics and mobility systems. • BIMX is synthesized by grafting BINOL onto Ti 3 C 2 T x MXene via Pd-catalyzed coordination in aqueous media. • Biaryl-functionalized MXene (BIMX) achieves excellent dispersibility in industrial organic solvents for polymer processing. • BIMX/TPU composites show an ultra-low percolation threshold of 0.3 vol% with high electrical conductivity. • Outstanding EMI shielding performance is achieved across X-, Ka-, and W-band frequencies by conductivity-driven mechanisms.

MXenes, two-dimensional transition metal carbides, nitrides, and carbonitrides, have emerged as versatile materials with remarkable physicochemical properties. However, their vulnerability to delamination or degradation in humid or liquid environments poses challenges for long-term stability. In this study, we present a novel approach to enhance MXene stability by synthesizing electrically conductive frameworks through covalent cross-linking using diisocyanates such as 1,4-phenylene diisocyanate (PDI) and hexamethylene diisocyanate (HDI). The resulting frameworks exhibit well-aligned MXene sheets covalently bonded throughout the film. The frameworks not only retained high electrical conductivity but also exhibited improved tensile strength and elongation at break compared to pristine MXene films. Moreover, the frameworks demonstrated exceptional stability under ultrasonic treatment in water, showing their enhanced structural durability. The chemically cross-linked MXene frameworks exhibited hydrophobicity and resistance to water, which contributed to their prolonged chemical stability as well. EMI shielding performance at Ka-band and X-band frequencies was comparable to pristine MXene films, in which SE T values around 60 dB were retained in oxidative environments over a week these findings open avenues for the development of robust MXene-based materials with enhanced stability for diverse applications, including electromagnetic interference shielding.

The escalating density of electronic devices and wireless technologies has heightened the need for effective electromagnetic interference (EMI) shielding solutions. Conventional polymer-based composites face persistent limitations due to the insulating nature of polymers and poor compatibility between hydrophilic conductive fillers like MXenes and hydrophobic polymer matrices. Here, we report a strategy to overcome this challenge by biaryl-functionalizing Ti 3 C 2 T x MXene with 1,1′-bi-2-naphthol (BINOL), yielding BINOL-grafted MXene (BIMX) with markedly enhanced dispersibility in a wide range of industrial organic solvents. The resulting BIMX demonstrates excellent compatibility with thermoplastic polyurethane (TPU) and enables the scalable fabrication of nanocomposites through simple solution mixing and blade coating techniques. BIMX/TPU composites exhibit a remarkably low electrical percolation threshold of 0.3 vol% and high electrical conductivity, achieving outstanding EMI shielding effectiveness across X-, Ka-, and W-band frequencies. Structural and spectroscopic analyses confirm the successful grafting and homogeneous dispersion of BIMX within polymer matrices. Furthermore, BIMX is compatible with multiple thermoplastic polymers, enabling versatile form factors for practical applications. This work presents a scalable, solvent-compatible pathway for engineering high-performance, flexible EMI shielding materials, offering a viable solution for next-generation electronics and mobility systems. • BIMX is synthesized by grafting BINOL onto Ti 3 C 2 T x MXene via Pd-catalyzed coordination in aqueous media. • Biaryl-functionalized MXene (BIMX) achieves excellent dispersibility in industrial organic solvents for polymer processing. • BIMX/TPU composites show an ultra-low percolation threshold of 0.3 vol% with high electrical conductivity. • Outstanding EMI shielding performance is achieved across X-, Ka-, and W-band frequencies by conductivity-driven mechanisms.

MXenes, two-dimensional transition metal carbides, nitrides, and carbonitrides, have emerged as versatile materials with remarkable physicochemical properties. However, their vulnerability to delamination or degradation in humid or liquid environments poses challenges for long-term stability. In this study, we present a novel approach to enhance MXene stability by synthesizing electrically conductive frameworks through covalent cross-linking using diisocyanates such as 1,4-phenylene diisocyanate (PDI) and hexamethylene diisocyanate (HDI). The resulting frameworks exhibit well-aligned MXene sheets covalently bonded throughout the film. The frameworks not only retained high electrical conductivity but also exhibited improved tensile strength and elongation at break compared to pristine MXene films. Moreover, the frameworks demonstrated exceptional stability under ultrasonic treatment in water, showing their enhanced structural durability. The chemically cross-linked MXene frameworks exhibited hydrophobicity and resistance to water, which contributed to their prolonged chemical stability as well. EMI shielding performance at Ka-band and X-band frequencies was comparable to pristine MXene films, in which SE T values around 60 dB were retained in oxidative environments over a week these findings open avenues for the development of robust MXene-based materials with enhanced stability for diverse applications, including electromagnetic interference shielding.

This paper proposes an over-the-air aggregation framework for federated learning (FL) in broadband wireless networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to improve communication efficiency of FL by reducing the number of channel uses required for the model convergence as well as avoiding the signaling overhead incurred by power scale coordination among edge devices. We analyze the convergence to a stationary point without convexity assumption on the objective function. The analysis result reveals that the utilization of BS dataset improves the convergence rate and the update distortion caused by the limited power budget is a crucial factor hindering the model convergence. To facilitate the convergence, we develop an optimized power control method by solving the distortion minimization problem without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reducing the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy. Furthermore, we identify some scenarios where the compression of local update can be helpful to reduce communication resources for model training.

This paper investigates a communication-constrained unmanned aerial vehicle (UAV) pickup and delivery system for continuous multi-period operations. To ensure real-time control updates between UAVs and the ground server, a minimum communication rate requirement is imposed throughout each mission. The objective is to minimize the average mission completion time of multiple rotary-wing UAVs while satisfying mobility, payload, safety, and communication constraints. The resulting mixed-integer nonlinear programming problem, involving binary pickup/drop-off decisions, trajectories, and variable time-slot durations, is mathematically intractable. To address this, a successive convex approximation framework combined with a penalty convex–concave procedure is developed, enabling iterative convex reformulation and convergence to a near-optimal binary-feasible solution. Simulation results demonstrate that the proposed algorithm efficiently generates collision-free trajectories and adaptive flight paths that maintain reliable communication links, outperforming baseline strategies in terms of completion time and coordination efficiency under communication constraints.

The surge in wireless devices and data traffic volume necessitates more efficient transmission methods. Multicasting has garnered consistent attention as a means to fulfill the increasing demand for more efficient data transmission methods. Nevertheless, leveraging multicast wireless networks for spatio-temporally asynchronous data requests poses challenges. In this context, this paper introduces a new multicast mechanism called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">set-up based merged multicast (SMMC)</i> to minimize the delivery time of the requested file in wireless networks by considering the uncertainties inherent in wireless channels. The proposed mechanism comprises two phases. The first phase involves gathering asynchronous requests for a file from users experiencing diverse channel conditions. During this phase, packets of the requested file are transmitted individually in unicast mode within a specified set-up time. Following this, the second phase initiates multicast transmission, which sequentially handles the remaining packets of the file in multicast mode. In the proposed mechanism, we optimize the set-up time and transmission rates of both unicast and multicast modes to minimize the expected file delivery time by jointly taking into account the statistical characteristics of wireless channels, users' locations, and file popularity. Additionally, we also delve into a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fine-tuned SMMC</i> (FT-SMMC) by utilizing posterior information on the multicast group size and further improve the performance. Extensive simulations demonstrate that the proposed multicast transmission achieves substantial reductions in delivery time, and this performance gain is further enhanced by optimizing set-up time and transmission rates in diverse wireless network scenarios

Electromagnetic Radiation Shielding In article number 2401264, Quyen Vu Thi, Daewon Sohn, Vinh Xuan Truong, and co-workers discover that poly (acrylic acid) hydrogels, when combined with multi-walled carbon nanotubes, can form robustly porous composite hydrogels with excellent electromagnetic interference (EMI) shielding. The outstanding shielding efficiency, (> 99.99%) under extremely acidic environments, paves toward applications of 3D hierarchical network materials for EMI applications in both land and aqueous environments.

Federated Learning (FL) enables training models across distributed devices while preserving data privacy by avoiding raw data sharing. However, it suffers from significant communication overhead. Event-Triggered FL (ETFL) addresses this issue by allowing devices to transmit updates only when substantial changes occur in the model. Nevertheless, this approach may result in imbalanced communication, where some devices communicate more frequently than others, leading to uneven model performance and slower overall convergence. To address this, we propose a new threshold-based method that dynamically adjusts each device’s communication frequency. Our method ensures balanced communication across devices and reduces the time required for each training iteration, ultimately accelerating convergence time. Furthermore, we analyze how a device’s communication affects the difference between its local model and the global model. Through extensive experiments, we demonstrate that the proposed method significantly reduces communication imbalance and achieves faster convergence compared to existing approaches. This result highlights the importance of balancing communication in federated learning to improve overall performance and ensure fairness across devices.

The conventional FL methods face critical challenges in realistic wireless edge networks, where training data is both limited and heterogeneous, often leading to unstable training and poor generalization. To address these challenges in a principled manner, we propose a novel wireless FL framework grounded in Bayesian inference. By virtue of the Bayesian approach, our framework captures model uncertainty by maintaining distributions over local weights and performs distribution-level aggregation of local distributions into a global distribution. This mitigates local overfitting and client drift, thereby enabling more reliable inference. Nevertheless, adopting Bayesian FL increases communication overhead due to the need to transmit richer model information and fundamentally alters the aggregation process beyond simple averaging. As a result, conventional Over-the-Air Computation (AirComp), widely used to improve communication efficiency in standard FL, is no longer directly applicable. To overcome this limitation, we design a dedicated AirComp scheme tailored to Bayesian FL, which efficiently aggregates local posterior distributions at the distribution level by exploiting the superposition property of wireless channels. In addition, we derive an optimal transmit power control strategy, grounded in rigorous convergence analysis, to accelerate training under power constraints. Our analysis explicitly accounts for practical wireless impairments such as fading and noise, and provides theoretical guarantees for convergence. Extensive simulations validate the proposed framework, demonstrating significant improvements in test accuracy and calibration performance over conventional FL methods, particularly in data-scarce and heterogeneous environments.

One of the causes of the recent EESS fire in Korea was found to be a lack of management of the operating environment. In particular, the new and renewable energy-connected EESS installed in mountainous and coastal areas is operated in an environment where condensation is likely to occur due to the large difference in weather and exposure to a large amount of dust. Insulation is destroyed between the cell and the module enclosure, which can lead to a fire due to repeated condensation and drying in the battery module. To prevent this, attention is required to external environmental factors such as temperature and humidity in the installation space for the stable operation of EESS. Therefore, this paper proposes environmental requirements for the stable operation and management of EESS by identifying major items of environmental factors that may cause damage in the operation and management of EESS and seeking ways to minimize them.