Nano Convergence最新文献

Oxide semiconductors have emerged as critical channel materials for advanced display and next-generation memory technologies, offering superior electron mobility, excellent uniformity, and low-temperature processability. Despite successful commercialization in display backplanes since 2012, their broader adoption remains limited by an inherent trade-off between carrier mobility and device stability—a fundamental challenge arising from the complex interplay between conduction band dispersion and defect chemistry. The development of strategies to overcome this trade-off has therefore become essential for realizing high-performance oxide semiconductor devices in emerging applications. In this review, a comprehensive data-driven analysis of the mobility-stability trade-off in oxide semiconductor thin-film transistors is presented through large language model-powered extraction of over 1,000 experimental datasets from literature. First, the methodology for systematic data extraction and the quantitative visualization of the mobility-stability relationship are introduced. Then, the critical roles of channel composition, gate insulator selection, and post-deposition annealing temperature in determining device performance are statistically analyzed through kernel density estimation and correlation studies. Next, the temporal evolution of process technologies from 2003 to 2025 is examined, revealing progressive improvements in both mobility and stability through advanced strategies. Afterward, detailed case studies of outlier devices—those successfully transcending the conventional trade-off—are presented, identifying key breakthrough approaches including multi-channel architectures, crystallinity engineering, hybrid gate dielectrics, and interface optimization. The superior performance of atomic layer deposition compared to physical vapor deposition methods is demonstrated through comparative analysis. Finally, the implications of this data-driven framework for accelerating materials development are discussed, and future perspectives on leveraging AI-assisted analysis for semiconductor research are provided.

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Exosome-mimetic lipid nanoparticles (ENPs) are a promising alternative to PEGylated lipid nanoparticles (LNPs) for targeted cancer therapy, offering improved biocompatibility and reduced immune clearance. However, the rational design of these biomimetic particles is challenging due to complex lipid composition requirements. We developed a hybrid algorithm to optimize exosome-mimetic formulations by predicting key nanoparticle properties (size, zeta potential, and polydispersity index). The algorithm was trained on an expanded dataset of 17,800 lipid compositions generated by augmenting experimental and publicly available data using the LipidGAN generative model, incorporating physicochemical modeling and feature extraction. It identified optimal formulations, which were validated in vitro across three cancer cell lines (HeLa, H1975, and MCF-7). Cytotoxicity assays confirmed minimal toxicity (cell viability > 90%), and uptake studies demonstrated efficient, cell-type-specific internalization (91 ~ 95%). These results highlight the potential of artificial intelligence (AI)-driven lipid design to emulate the functionality of natural exosomes and advance the development of safe, effective, and personalized cancer nanomedicines.

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The need for processing complex and temporal datasets has increased with the rise of artificial intelligence. In this context, reservoir computing, which utilizes the short-term memory of the reservoir to map input data into a high-dimensional space, has gathered significant interest. In this study, for the first time, fully CMOS-compatible reservoir computing is demonstrated through gate insulator stack engineering. Integrated on a single wafer, CMOS circuits, Al₂O₃/Si₃N₄ (A/N) devices for both reservoir and leaky integrate-and-fire neuron applications, and Al₂O₃/Si₃N₄/SiO₂ (A/N/O) devices as synaptic devices are verified. Furthermore, the influence of various bias conditions on reservoir performance is analyzed. The proposed co-integrated reservoir computing system efficiently handles temporal data, reducing ~ 53% of network resources with only ~ 0.17%p accuracy drop while being robust to device variations.

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Robust and scalable materials for oil-water separation are often limited by corrosion, fouling, and mechanical damage. Here, we demonstrate a simple, rapid, and low-temperature surface conversion that transforms commercial 316L stainless-steel meshes into superhydrophilic, underwater-oleophobic filters. A two-step reaction followed by Fe³⁺-tannic acid-phosphate complexation yields a thin nanogranular FeOOH/TA/PO₄ hybrid film that is chemically anchored to the steel surface. The coating imparts rapid wetting behaviour, with a static water contact angle of about 45° and dynamic superhydrophilicity that achieves complete wetting within 0.15 s, enabling high-performance gravity-driven separations. Single-pass treatment of diverse oil-water mixtures delivered oil rejections of 95–99% and water permeate fluxes in the range of 23,000–80,000 L m^–2 h^–1, depending on mesh size. The meshes retained a robust performance over 50 separation cycles, 30 days of brine immersion, and exposures across pH 1–14 with negligible degradation. Abrasion resistance was further validated, and fouled meshes (e.g., with graphene oxide or edible oil) rapidly regained high flux and rejection after simple water rinsing, underscoring intrinsic self-cleaning behaviour. The process uses inexpensive reagents and is readily applicable to large-area mesh surface functionalization, providing a robust pathway to high-throughput, energy-free gravity separations for oily wastewater remediation.

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A simple, scalable two-step conversion of 316L stainless-steel mesh into a superhydrophilic filter coated with a robust protective nanohybrid film, which enables ultrafast oil-water separation, strong abrasion and anti-fouling resistance, corrosion tolerance, and long-term durability. Background illustration was created using Midjourney, a text-to-image AI tool.

Creating a piezoelectric or pyroelectric material from a ferroelectric material requires aligning its ferroelectric domains to achieve a remanent polarization. This complex process involves nucleating/growing domain structures and inducing a macroscopic, non-centrosymmetric symmetry under an applied electric field. For many years, this process has not received significant attention. Typically, DC fields are applied at elevated temperatures to align the domain states, balancing the depolarization and screening fields in a metastable state that is still near equilibrium. In contrast, the pulse poling (PP) strategy uses field pulses much faster than the time it takes for depolarization and bulk screening processes to occur. This instability allows the PP of relaxor ferroelectric (RFE) crystals and textured ceramics to induce a new far-from-equilibrium (FFE) state, which has enhanced properties compared to conventional DC poling. RFE crystals are of interest because it is known that poling them in specific directions creates engineered domain structures that provide giant piezoelectric properties with low hysteretic losses. By applying PP to RFE crystals in the < 001 > direction, significant changes in the electromechanical properties within the FFE state were observed, which opens new high-performance opportunities for these materials in transducer applications. This review outlines the property enhancements with PP and their origins while modeling the properties with a phenomenological thermodynamic approach. The properties are discussed with respect to transducer applications and benchmarked to other traditional poling strategies. Mn: PMN-PIN-PT, Mn: PMN-PZT, and Sm: PMN-PIN-PT are primarily used as model systems to demonstrate enhanced electromechanical performance with PP over other conventional poling strategies. Given the new concepts discussed in this paper, there is also a future research section at the end of the paper to drive the innovation beyond this initial work.

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Recent developments in chainmail catalysis present a promising solution to the longstanding challenge of catalytic deactivation in traditional advanced oxidation processes (AOPs) for water reuse. This strategy involves encapsulating nanocatalysts within protective carbon shells, which significantly improves their stability and enhances the degradation kinetics of micropollutants. The unique architecture of these materials promotes efficient electron transfer between the transition metal core and the surrounding carbon matrix, giving rise to a range of intriguing and unprecedented phenomena. Given its strong potential, this review provides a comprehensive overview of the latest developments in chainmail catalysis, highlighting its advantages over conventional AOPs. This review critically examines experimental results, theoretical insights, and conflicting observations related to the generation of reactive species and degradation mechanisms of micropollutants. The influence of key parameters (such as catalyst structure and activity, carbon shell morphology and thickness, heteroatom doping, surface functionalization, and the composition and redox state of the metal core) on chainmail catalytic performance is also thoroughly analyzed. Furthermore, the review assesses the practical viability of chainmail catalysis in real-world water treatment applications by examining the impact of operating conditions and water matrices on chainmail catalytic performance. Finally, this review outlines key technological challenges and proposes a roadmap to guide future research and the practical implementation of chainmail catalysis for efficient removal of micropollutants.

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