Two-dimensional (2D) semiconductors enable atomically thin channels and attractive electrostatics, but practical scaling increasingly hinges on gate-dielectric integration rather than channel performance. A key challenge is forming high-quality dielectrics on chemically inert, dangling-bond-free 2D surfaces while pushing equivalent oxide thickness to the sub-nanometer regime without excessive leakage, traps, or electrical breakdown. This review addresses the materials and process physics that govern dielectric formation in 2D devices, with an emphasis on atomic layer deposition nucleation, surface pretreatment and functionalization, and the use of seed and buffer layers for conformal high-κ oxides. The roles of layered insulators, such as hexagonal boron nitride, are discussed in terms of interface quality, electrostatic scaling limits, and transport limitations. The impact of dielectrics and processing on leakage mechanisms, defect generation, device-to-device variability, and reliability metrics, including time-dependent dielectric breakdown, bias-temperature instability, hysteresis, and threshold-voltage drift, is examined. Finally, we highlight van der Waals dry integration and dielectric transfer approaches that reduce process-induced damage and support wafer-scale uniformity, as well as opportunities for mixed-dimensional and 3D stacked architectures across logic, memory, and emerging functional systems.
We investigated whether the relationship between partisan YouTube channel use and conspiracy mentality depends on like-minded discussion and interest in politics. Using a two-wave panel survey conducted around South Korea's 2024 general election (Wave 1: N = 1,550; Wave 2: N = 908) and lagged regression models controlling for prior conspiracy mentality, partisan YouTube channel use showed no direct association with conspiracy mentality. The association between partisan channel use and conspiracy mentality strengthened as like-minded discussion increased, and this pattern weakened as political interest increased. The three-way pattern emerged for liberal partisan channels but not for conservative partisan channels, indicating ideological asymmetry in the conditional association. Overall, the findings suggest that partisan YouTube channel use relates to conspiracy mentality mainly when users frequently engage in like-minded discussions and have lower interest in politics. The results also highlight that interaction and discussion settings are central on the YouTube platform for understanding when partisan channel use is associated with conspiracy mentality.
Resistive-switching random-access memory (RRAM) has gained a great deal of attention as an emerging memory suitable for massive data storage media and synaptic device applications. For low-power operation capability, eliminating the necessity of current compliance, tunneling oxide can be inserted as a tunneling layer in the conventional RRAM devices. In this work, we have systematically investigated the effects of SiO2 tunneling layer and its formation method on the reliability of a Si3N4-based RRAM device. The tunneling oxide layers were deposited by plasma-enhanced chemical vapor deposition (PECVD) and medium-temperature oxidation (MTO) and compared to a single-layer reference device. The devices with tunneling layers demonstrated reduced state current, and the device prepared by MTO exhibited superior endurance and retention. All of the devices demonstrated space-charge-limited current conduction in the high-resistance state. X-ray photoelectron spectroscopy revealed that the MTO oxide layer was chemically more stable, resulting in a difference in endurance characteristics.
Molybdenum carbide (Mo2C) has emerged as a compelling nonprecious metal electrocatalyst for the hydrogen evolution reaction (HER), owing to its excellent catalytic activity and high stability. However, conventional synthesis strategies involving sacrificial templates or polymer self-assembly often face trade-offs, such as hazardous postprocessing, limited electrical conductivity, or uncontrolled particle growth, leading to diminished active sites. To address these challenges, we present a straightforward carburization strategy using N-doped mesoporous graphene (NMG), which simultaneously functions as the carbon precursor and a conductive matrix. The synthesized Mo2C nanoparticle-embedded NMG (MCNMG) nanohybrid is characterized by a homogeneous distribution of ultrafine beta-Mo2C nanoparticles (<3 nm) confined within the NMG framework. Significantly, the superior structural properties of MCNMG are attributed to a synergistic dual-confinement effect: (1) the physical restriction by the mesoporous architecture and (2) the anchoring effect by pyridinic- and graphitic-N species. These synergistic effects not only suppress particle agglomeration but also mitigate the excessive consumption of the carbon scaffold during carburization, thereby preserving an average pore diameter of similar to 7.0 nm and a large specific surface area of 696 m(2) g(-1), which maximize active site exposure and facilitate efficient charge/mass transport. In electrochemical evaluations, the MCNMG catalyst requires an overpotential of only 169 mV to deliver a current density of - 10 mA cm(-2), accompanied by a Tafel slope of 77 mV dec(-1). Furthermore, it exhibits robust durability over 120 h of continuous operation at high current densities (-50 mA cm(-2)), demonstrating its great potential for practical water electrolysis applications. This work highlights the critical role of anchoring effects in rational hybrid design, offering a pathway to durable, high-performance electrocatalysts for hydrogen production.
Video coding for machines (VCM) is an emerging approach in video compression designed to optimize content for machine analysis tasks. Although VCM was initially developed for machine vision, scalable coding frameworks have been developed to support both machine-driven analysis and human viewing as required. In this work, we focus on scenarios where high-quality encoding of regions of interest (ROIs) for machine vision and low-bitrate encoding of the background (BG) for human vision. At the decoder, severely degraded BG quality in reconstructed frames makes them unsuitable for viewing; therefore, restoring the degraded BGs by leveraging high-quality ROIs is essential. To this end, we propose the Gradient-Guided Diffusion Restoration (GGDR) algorithm, which integrates a pretrained generative diffusion model with content-aware supervision and adaptive refinement mechanisms to restore severely degraded regions robustly while maintaining visual consistency across the entire frame. The GGDR algorithm consists of two key components: (i) a content-aware supervision mechanism that preserves salient features and structural information in the input image, ensuring superior performance even with challenging high-variance inputs and (ii) a refinement block that guides the generation process of the pretrained diffusion model based on a degradation model and structural guidance. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms both qualitatively and quantitatively.
