Nano Convergence最新文献

To accelerate the widespread adoption of green hydrogen energy, developing cost-effective, sustainable electrocatalysts that can replace precious metals is essential. This study introduces a bifunctional electrocatalyst for overall water splitting, comprising 2D reduced graphene oxide (rGO) integrated with NiMnB nanosheets into a tailored nanohybrid (rGO/Ni_1.5Mn_0.5B). The integration enhances both crystal growth and catalytic activity by promoting rapid nucleation and efficient electron transport. Synthesized via a one-pot hydrothermal process, the rGO/Ni_1.5Mn_0.5B electrode exhibits a crystalline 2D nanosheet-like morphology, ensuring high electroactive surface area and strong contact with electrolyte. In 1.0 M KOH, the catalyst achieves an overpotential of 159 mV for hydrogen evolution reaction (HER) and 170 mV for oxygen evolution reaction (OER), outperforming RuO₂ at 10 mA/cm². DFT calculations reveal that the strong orbital coupling between Ni, Mn, and the rGO matrix enhances metal-support interactions, boosting catalytic performance. The symmetric cell demonstrates overall water splitting cell voltage of 1.49 V at 10 mA/cm² with excellent durability over 20 h under industrial conditions. Additionally, the long-term durability performance was evaluated using time series modelling with a long short-term memory algorithm. With superior electronic, structural, and electrochemical properties, rGO/Ni_1.5Mn_0.5B offers a scalable solution for next-generation industrial water splitting and sustainable hydrogen production.

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A bifunctional, precious-metal-free rGO/NiMnB nanohybrid exhibits enhanced alkaline water splitting performance via covalent metal-support interactions. Synergistic orbital coupling at the Fermi level promotes efficient electron transfer, leading to low overpotentials for HER and OER. DFT analysis supports the observed catalytic activity and stability under industrial conditions.

Superconducting nanowire single-photon detectors (SNSPDs) have emerged as essential devices that push the boundaries of photon detection with unprecedented sensitivity, ultrahigh timing precision, and broad spectral response. Recent advancements in materials engineering, superconducting electronics integration, and cryogenic system design are enabling the evolution of SNSPDs from single-pixel detectors toward scalable arrays and large-format single-photon time tagging cameras. This perspective article surveys the rapidly evolving technological landscape underpinning this transition, focusing on innovative superconducting materials, advanced multiplexed read-out schemes, and emerging cryo-compatible electronics. We highlight how these developments are set to profoundly impact diverse applications, including quantum communication networks, deep-tissue biomedical imaging, single-molecule spectroscopy, remote sensing with unprecedented resolution, and the detection of elusive dark matter signals. By critically discussing both current challenges and promising solutions, we aim to articulate a clear, coherent vision for the next generation of SNSPD-based quantum imaging systems.

Vertical NAND (V-NAND) flash memory has emerged as a promising candidate for neuromorphic computing platforms due to its high density, scalability, and reliability. However, synaptic weights stored in V-NAND cells are highly sensitive to ambient temperature variations, resulting in significant conductance shifts that degrade the inference accuracy of neural networks. To address this challenge, we propose a dynamic pass bias (DPB) control scheme that compensates for temperature-induced weight variations without requiring memory reprogramming or additional hardware overhead. By adaptively adjusting the pass bias applied to unselected word-lines during read operations, the DPB scheme effectively stabilizes the differential conductance representation of weights under thermal fluctuations. In addition, we introduce a temperature-adaptive biasing circuit composed of a single-crystalline silicon MOSFET and V-NAND strings. Exploiting their opposing temperature-dependent resistance characteristics, this passive circuit naturally reduces the pass bias as temperature rises, enabling real-time analog compensation without explicit sensing or digital control logic. Experimental measurements on commercial V-NAND devices fabricated with over 100 WL layers reveal substantial shifts in bit-line currents with increasing temperature. Simulation results based on CIFAR-10 image classification using a VGG-11 network demonstrate that the DPB scheme significantly mitigates accuracy degradation across a wide temperature range. Notably, adjusting pass bias at lower temperatures improves classification accuracy by up to 10.5%p compared to conventional fixed-bias operations. These results highlight the effectiveness of dynamic pass bias control—both digitally and circuit-assisted—as a lightweight and scalable solution for enhancing the temperature resilience of V-NAND flash memory-based neural networks.

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In this study, we demonstrated the nanostructuring of the ferroelectric (FE) phase of Pb(Zr_0.95Ti_0.05)O₃ (PZT-95/5) into a thick film with relaxor-like FE (RFE) characteristics. This transformation results in exceptionally high dielectric breakdown strength (E_DBS) and energy storage density properties. The high kinetic energy from aerosol deposition transformed the bulk PZT-95/5 from a normal FE system into a RFE system by forming a nanostructured grain with nanodomains within a nonpolar matrix. This nanostructure enables easy domain switching, resulting in low remanent polarization. The resulting high density of grain boundaries due to nanograin formation and the nonpolar structure act as barriers to charge flow, resulting in high breakdown strength. Collectively, these effects resulted in a significantly enhanced E_DBS of 5.6 MV/cm and a maximum polarization of 80 µC/cm². These properties, evidenced by slim hysteresis loops, demonstrate that the prepared PZT-95/5 thick film is a superior capacitive material with a high recoverable energy density of 116 J/cm³. Furthermore, the film exhibited reliable fatigue endurance up to 10⁷ cycles and thermal stability from room temperature to 140^°C. The film also exhibited a peak power density of 35 MW/cm³ under a practical electric field of 0.45 MV/cm (180 V) and a fast discharging speed (τ_0.9) of 230 ns. These properties, in addition to the minimal fabrication steps and superior capacitive characteristics, demonstrate the strong potential of the prepared PZT-95/5 thick film for use in next-generation energy storage devices.

Living cells produce nanometer scales of extracellular vesicles (EVs) that have attracted considerable interest due to their transformative effects on diagnostics and therapies for cancer and other diseases. While significant advancements have been made in grasping the physical and chemical foundations of separation techniques for EVs, challenges must be overcome to ensure effective EV purification for diverse life sciences and clinical applications. This review highlights the most significant developments in efficient isolation and purification methods for EVs in transformative medicine. We examine the basic structure of exosomes and how to obtain specimens containing exosomes and EVs from various body fluids. We investigate the principles of physical, chemical, and biological isolation methods of EVs. We systematically evaluate different designs of microfluidics-based EV purification methods. We provide a comprehensive overview of the applications of exosomes in the life sciences and medicine. The precise engineering of EV isolation and purification generates a high yield and purity, offering practical solutions for translational medicine.

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By precisely timed optical excitation of their spin, optical emitters such as semiconductor quantum dots or atoms can be harnessed as sources of linear photonic cluster states. This significantly reduces the required resource overhead to reach fault-tolerant optical quantum computing. Here, we develop an algorithm that analytically tracks the global density matrix through the process of the protocol for generating linear-cluster states by Lindner and Rudolph. From this we derive a model to calculate the entangling gate fidelity and the state fidelity of the generated linear optical cluster states. Our model factors in various sources of error, such as spin decoherence and the finite excited state lifetime. Additionally, we highlight the presence of partial reinitialization of spin coherence with each photon emission, eliminating the hard limitation of coherence time. Our framework provides valuable insight into the cost-to-improvement trade-offs for device design parameters as well as the identification of optimal working points. For a combined state-of-the-art quantum dot with a spin coherence time of ({T}_{2}^{*}=535) ns and an excited state lifetime of (tau =23) ps, we show that a near-unity entangling gate fidelity as well as near-unity state fidelity for 3-photon and 7-photon linear cluster states can be reached.

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Olfactory display systems, designed to replicate the human sense of smell, rely on gas sensors that are fast, selective, and reliable. From this perspective, this review highlights recent progress in sensing materials and integration strategies that enable room-temperature operation, rapid response and recovery, and closed-loop control for realistic odor delivery. Advances are classified into three categories: organic, inorganic, and hybrid systems. Organic materials, including conductive polymers and biomolecules, offer tunable selectivity and lightweight flexibility. Inorganic semiconductors, especially metal oxides, provide high sensitivity and durability, though they typically require elevated temperatures. Hybrid architectures, exemplified by M13 bacteriophage–carbon nanotube composites, merge these strengths to achieve superior performance under ambient conditions. Particular emphasis is placed on sensors for ethylene, hydrogen sulfide, hydrogen, acetone, and nitrogen dioxide—gases critical to food preservation, environmental monitoring, and healthcare. Finally, we discuss persistent challenges, such as selectivity under complex conditions, device miniaturization, and closed-loop integration, and propose strategic research directions toward immersive, real-time olfactory display technologies.

This study investigates the influence of sputtering plasma-induced damage on stochastic characteristics in HfZrO₂ (HZO)-based ferroelectric tunnel junctions (FTJs), with an emphasis on memory and neuromorphic device optimization. Variation of the sputtering plasma power during top electrode deposition introduces distinct levels of trap within the HZO layer. Low-frequency noise (LFN) spectroscopy and temperature-dependent electrical measurements confirm that higher plasma power generates additional shallow-level traps, thereby promoting Poole-Frenkel conduction while simultaneously increasing current noise magnitude. Although the resulting enhancements in on-current density and ferroelectric tunnel electroresistance (TER) ratio are beneficial for high-density memory integration, these conditions also elevate stochastic fluctuations, potentially degrading read margins and long-term endurance. Furthermore, the observed increase in stochasticity negatively affects neuromorphic inference accuracy, particularly after endurance cycling stress. These results demonstrate the critical interplay among plasma process conditions, trap density, and LFN in FTJs. By systematically engineering sputtering process parameters, we optimize the electrical performance with minimized stochastic noise. This approach provides guidelines for the development of next-generation ferroelectric-based memories and neuromorphic systems with consideration of stochasticity, where robust performance and reliability are imperative for large-scale integration.

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Lead-free halide-perovskite memristors have advanced rapidly from initial proof-of-concept junctions to centimeter-scale selector-free crossbar arrays, maintaining full compatibility with CMOS backend processes. In these highly interconnected matrices, surface passivation, strain-relief interfaces, and non-toxic B-site substitutions successfully reduce sneak currents and stabilize resistance states. The Introduction section lays out the structural and functional basis, detailing phase behavior, bandgap tunability, and tolerance-factor-guided crystal design within Ruddlesden–Popper, Dion–Jacobson, vacancy-ordered, and double-perovskite frameworks, each of which is evaluated for its ability to confine filaments and reduce crosstalk in crossbar configurations. The following sections examine the characteristics of charge transport and the dynamics of ion migration, followed by a detailed outline of chemical and mechanical stabilization strategies in response to the high current densities and heat fluxes typical of large-area crossbars. The comparison of solution, vapor, and solid-state synthesis routes focuses on aspects such as film uniformity, grain-boundary control, and compatibility with flexible or heterogeneous substrates, all evaluated against the demanding uniformity requirements of multilevel crossbar programming. The principles of resistive switching and array architecture are elaborated upon, emphasizing the three-dimensional (3D) stacking of selector-integrated vertical nanowires and hybrid photonic-memristive layers as promising approaches to enhance bandwidth and reduce energy consumption per operation. By integrating sustainable chemistry with scalable crossbar engineering, these memories are set to provide ultra-dense, energy-efficient hardware that meets the performance demands of contemporary artificial intelligence accelerators while adhering to new regulations on hazardous materials in electronic devices.

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