Nano Convergence最新文献

Acute lung injury (ALI) is characterized by severe inflammation in lung tissue, excessive immune response and impaired lung function. In hospitalized high-risk patients and cases of secondary infection due to surgical contamination, it can lead to higher mortality rates and require immediate intervention. Currently, clinical treatments are limited in symptomatic therapy as mechanical ventilation and corticosteroids, having insufficient efficacy in mitigating the cause of progression to severe illness. Here we report a pulmonary targeting lung-homing nanoliposome (LHN) designed to attenuate excessive Neutrophil Extracellular Trap formation (NETosis) through sivelestat and DNase-1, coupled with an anti-inflammatory effect mediated by 25-hydroxycholesterol (25-HC), offering a promising intervention for the acute phase of ALI. Through intratracheal delivery, we intend prompt and constant action within the lungs to effectively prevent excessive NETosis. Isolated neutrophils from blood samples of severe ARDS patients demonstrated significant anti-NETosis effects, as well as reduced proinflammatory cytokine secretion. Furthermore, in a murine model of LPS-induced ALI, we confirmed improvements in lung histopathology, and early respiratory function. Also, attenuation of systemic inflammatory response syndrome (SIRS), with notable reductions in NETosis and neutrophil trafficking was investigated. This presents a targeted therapeutic approach that can be applied in early stages of high-risk patients to prevent severe pulmonary disease progression.

Metal-oxide thin-film semiconductors have been highlighted as next-generation space semiconductors owing to their excellent radiation hardness based on their dimensional advantages of very low thickness and insensitivity to crystal structure. However, thin-film transistors (TFTs) do not exhibit intrinsic radiation hardness owing to the chemical reactions at the interface exposed to ambient air. In this study, significantly enhanced radiation hardness of Al₂O₃-passivated ZnO TFTs against high-energy protons with energies of up to 100 MeV is obtained owing to the passivation layer blocking interactions with external reactants, thereby maintaining the chemical stability of the thin-film semiconductor. These results highlight the potential of passivated metal-oxide thin films for developing reliable radiation-hardened semiconductor devices that can be used in harsh space environments. In addition, the relationship between low-frequency noise and defects due to oxygen vacancies was revealed, which can be utilized to improve device reliability.

Electrochemical water splitting, which encompasses the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), offers a promising route for sustainable hydrogen production. The development of efficient and cost-effective electrocatalysts is crucial for advancing this technology, especially given the reliance on expensive transition metals, such as Pt and Ir, in traditional catalysts. This review highlights recent advances in the design and optimization of electrocatalysts, focusing on density functional theory (DFT) as a key tool for understanding and improving catalytic performance in the HER and OER. We begin by exploring DFT-based approaches for evaluating catalytic activity under both acidic and alkaline conditions. The review then shifts to a material-oriented perspective, showcasing key catalyst materials and the theoretical strategies employed to enhance their performance. In addition, we discuss scaling relationships that exist between binding energies and electronic structures through the use of charge-density analysis and d-band theory. Advanced concepts, such as the effects of adsorbate coverage, solvation, and applied potential on catalytic behavior, are also discussed. We finally focus on integrating machine learning (ML) with DFT to enable high-throughput screening and accelerate the discovery of novel water-splitting catalysts. This comprehensive review underscores the pivotal role that DFT plays in advancing electrocatalyst design and highlights its potential for shaping the future of sustainable hydrogen production.

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Ferroelectric capacitive memories (FCMs) utilize ferroelectric polarization to modulate device capacitance for data storage, providing a new technological pathway to achieve two-terminal non-destructive-read ferroelectric memory. In contrast to the conventional resistive memories, the unique capacitive operation mechanism of FCMs transfers the memory reading and in-memory computing to charge domain, offering ultra-high energy efficiency, better compatibility to large-scale array, and negligible read disturbance. In recent years, extensive research has been conducted on FCMs. Various device designs were proposed and experimentally demonstrated with progressively enhanced performance, showing remarkable potential of the novel technology. This article summarizes several typical FCM devices by introducing their mechanisms, comparing their performance, and discussing their limitations. We further investigate the capacitive crossbar array operation and review the recent progress in the FCM integration and array-level demonstrations. In addition, we present the computing-in-memory applications of the FCMs to realize ultra-low-power machine learning acceleration for future computing systems.

The intriguing way the receptors in biological skin encode the tactile data has inspired the development of electronic skins (e-skin) with brain-inspired or neuromorphic computing. Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. This is particularly attractive when one considers the huge data produced by large number of sensors expected in a large area e-skin such as the whole-body skin of a robot. This underlines the need for biological skin like processing in the e-skin. Herein, we present multi-gate field-effect transistors (v-FET) having capacitively coupled floating gate (FG) to mimic some of the neural functions. The v-FETs are obtained by deterministic assembly of ZnO nanowires on a flexible substrate using contactless dielectrophoresis method, followed metallization using conventional microfabrication steps. The spatial summation of two presynaptic inputs (applied at multiple control gates) of the transistor confirm their neuron-like response. The temporal summation (such as paired-pulse facilitation) by presented v-FETs further confirm their neuron-like mimicking with one presynaptic input. The temporal and spatial summation functions, demonstrated by the v-FET presented here, could open interesting new avenues for development of neuromorphic electronic skin (v-skin) with possibility of biological-skin like distributed computing.

Two-dimensional halide perovskites are attracting attention due to their structural diversity, improved stability, and enhanced quantum efficiency compared to their three-dimensional counterparts. In particular, Dion-Jacobson (DJ) phase perovskites exhibit superior structural stability compared to Ruddlesden-Popper phase perovskites. The inherent quantum well structure of layered perovskites leads to highly anisotropic charge transport and optical properties. Therefore, controlling the preferred crystal orientation (parallel or perpendicular) is crucial for optimizing device performance. This work presents a rational strategy to control parallel and perpendicular crystal growth in C₆N₂H₁₆PbI₄ (4AMPPbI₄)-based DJ phase perovskite thin films. We demonstrate that crystal orientation depends on crystal growth rates, which can be controlled by varying the solvent composition, antisolvent, and annealing temperature. Direct and inverse photoelectron spectroscopy reveals that the electronic structure of 4AMPPbI₄, including its work function, ionization energy, and electron affinity, is orientation-dependent. Different orientations significantly affect carrier transport as confirmed by single-carrier devices. This study highlights the critical role of crystal orientation in DJ phase perovskites for designing high-performance optoelectronic devices.

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The advancement in high-performance computing technologies, including quantum and aerospace systems, necessitates components that operate efficiently at cryogenic temperatures. In this study, we demonstrate a hafnia-based ferroelectric tunnel junction (FTJ) that achieves a record-high tunneling electroresistance (TER) ratio of over 200,000 and decade-long retention characteristics. By introducing asymmetric oxygen vacancies through the strategic use of indium oxide (InO_x) layer, we enhance the TER ratio without increasing off-current, addressing the longstanding issue of low on-current in hafnia-based FTJs. Unlike prior approaches that led to leakage currents, our method optimizes tunneling behavior by leveraging the differential oxygen dissociation energy between InO_x and hafnium zirconium oxide (HZO). This results in asymmetric modulation of the tunnel barrier, enhancing electron tunneling in one polarization state while maintaining stability in the opposite state. Furthermore, we explore the intrinsic characteristics of the FTJ at cryogenic temperatures, where reduced thermal energy minimizes leakage currents and allows the maximization of device performance. These findings establish a new benchmark for TER in hafnia-based FTJs and provide valuable insights for the integration of these devices into advanced cryogenic memory systems.

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