Nano Convergence最新文献

Recent development of controlling quantum systems has enabled us to utilize the systems for quantum computing, communication, and sensing. In particular, quantum sensing has attracted attention to a broad community of science and technology, as it could surpass classical limitations in measuring physical quantities such as electric and magnetic field with unprecedented precision. Among various physical platforms for quantum sensing, trapped-ion based system possesses several advantages—atomic size, outstanding quantum coherence, and quantum properties. In this review, we introduce previous research efforts to utilize the trapped-ion system for reaching ultimate sensitivity and discuss future perspective and research directions in this emerging field.

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The upcoming generation of functional electronics in the era of artificial intelligence, and IoT requires extensive data storage and processing, necessitating further device miniaturization. Conventional Si CMOS technology is struggling to enhance integration density beyond a certain limit to uphold Moore’s law, primarily due to performance degradation at smaller dimensions caused by various physical effects, including surface scattering, quantum tunneling, and other short-channel effects. The two-dimensional materials have emerged as highly promising alternatives, which exhibit excellent electrical and mechanical properties at atomically thin thicknesses and show exceptional potential for future CMOS technology. This review article presents the chronological progress made in the development of two-dimensional materials-based CMOS devices with comprehensively discussing the advancements made in material production, device development, associated challenges, and the strategies to address these issues. The future prospects for the use of two-dimensional materials in functional CMOS circuitry are outlooked, highlighting key opportunities and challenges toward industrial adaptation.

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The interest in highly sensitive sensors is rapidly increasing for detecting very tiny signals for Internet of Things devices. Here, we achieve ultra-sensitive correlated breathable sensors based on freestanding VO₂ membranes. We fabricate the membranes by growing VO₂ films onto sacrificial Sr₃Al₂O₆ layer grown on SrTiO₃, selectively dissolving the Sr₃Al₂O₆ in water, and then rendering freestanding VO₂ membrane on nanomesh. The nanomeshes are extremely flexible, sweat permeable, and readily skin-adhesive. The resistance of the VO₂ membranes is reversibly tuned by human’s tiny mechanical stimuli and breath stimuli. The stimuli modulate the Peierls dimerization of one-dimensional V−V chains in the VO₂ lattice which concomitantly controls the electron correlation and hence resistivity. Since our breathable sensors operate based on quantum-mechanical correlation effects, their sensitivity is 1−2 orders of magnitude higher than conventional tactile and respiratory sensors based on other materials. Thus, the freestanding membranes of correlated oxides on epidermal nanomeshes are multifunctional platforms for developing ultra-sensitive correlated breathable sensors.

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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.