Nano Convergence最新文献

Ferroelectric memories have undergone a transformative evolution from conventional perovskite-based materials to modern fluorite-structured ferroelectrics, driven by the pursuit of scalable, low-power, and CMOS-compatible non-volatile memory solutions. The observation of ferroelectricity in nanoscale HfO₂-based films has enabled integration with CMOS-compatible processes, providing advantages such as potential scalability, low power consumption, and non-volatility, while facilitating continued scaling and high-density integration. Leveraging established materials infrastructure in the semiconductor industry, hafnia–based ferroelectrics have been incorporated in various memory architectures, including ferroelectric random-access memory (FeRAM), ferroelectric tunnel junctions (FTJs), ferroelectric field-effect transistors (FeFETs), and ferroelectric memcapacitors (FeCAPs). Beyond conventional non-volatile storage, these devices have also emerged as promising building blocks for in-memory computing applications, including neuromorphic systems, hardware security primitives, and associative memory. In this review, we explore the historical development of ferroelectric memories from a materials–device co-design perspective, examine recent advances in device architectures and in-memory computing applications, and discuss the remaining challenges in endurance, retention, variability, and scaling. Finally, we propose future research directions that integrating material innovation, interface engineering, and circuit-level optimization to realize the full potential of ferroelectric memories in next-generation computing platforms.

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Per- and polyfluoroalkyl substances (PFASs) are a category of extremely persistent environmental pollutants. Metal–organic frameworks (MOFs) have appeared as promising adsorbents for PFAS removal due to their large surface area, tunable porosity, and versatile surface chemistry, which are among the numerous treatment technologies available. This review critically evaluates current developments in the design, fabrication, and application of MOF-based (nano)materials for the adsorption of PFAS in aqueous environments. The adsorption efficacies of MOFs (e.g., pore size, surface charge, and functional groups) and PFASs (e.g., chain length, head group functionality, and polarity) are significantly influenced by their physicochemical properties. The selective and efficient removal of PFASs is governed by the interaction mechanisms such as electrostatic attraction, hydrophobic interactions, H-bonding, and Lewis acid–base coordination. In addition, the adsorption efficacy is significantly influenced by water quality conditions, including pH, ionic strength, background ions, and natural organic matter. Functionalized MOFs (e.g., those with amine, fluorinated, or hydrophobic groups) exhibit resilience to interference, although these factors can sometimes hinder their removal. Both experimental and computational studies have provided valuable mechanistic insights into the rational design of MOFs with improved selectivity and capacity. In addition, this review identifies critical challenges and future perspectives, such as the necessity of standard performance testing under realistic water matrices; the development of scalable, stable, and regenerable MOFs; and their integration into life-cycle assessment and toxicity evaluation.

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Complementary logic and memory circuits based on n-type indium gallium zinc oxide (IGZO) and p-type tin monoxide (SnO) thin-film transistors (TFTs) were demonstrated with low-voltage, hysteresis-free operation. Optimization of IGZO channel thickness precisely tuned the inverter switching point to near V_DD/2, achieving a high voltage gain of 146.6 V/V at V_DD = 3 V and ultra-low static power consumption in the nanowatt range. SU-8 passivation effectively suppressed bias-stress-induced degradation in both IGZO and SnO TFTs, enhancing long-term stability and reducing device variation. Using these optimized devices, a 3-stage ring oscillator exhibited stable oscillations, and 6T-SRAM cells achieved tunable static noise margins by adjusting transistor strength ratios. This work represents one of the first implementations of IGZO/SnO-based  6T-SRAM and demonstrates the potential of oxide semiconductor complementary circuits for low-power and reliable system integration, with promising future applicability to non-volatile memory and on-device artificial intelligence hardware.

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Spatiotemporal mapping of thrombus remodeling remains a major unmet challenge due to the lack of diagnostic tools capable of dynamically converting local biochemical activity into quantitative imaging signals. Current clinical methods lack sensitivity and specificity for accurate thrombus staging. Here, we present a programmable MRI nanoplatform enabling enzyme-gated dual-mode contrast switching for dynamic thrombus profiling and guided thrombolysis. The nanoprobe achieves broad-spectrum thrombus targeting by recognizing two complementary biomarkers uniquely expressed at distinct thrombus maturation stages, and integrates gelatin-guided structural reconfiguration with magnetic nanoparticle clustering to modulate MRI contrast. Gelatin modulates the nanoprobe structure, restricting water proton accessibility and promoting internal densification, thereby synchronously suppressing T1-weighted signals and amplifying T2-weighted contrast. Upon activation by thrombus-associated MMP-2/9, the nanoprobe disassembles, reversing its nano-architecture and signal behavior. This smart signal transformation quantitatively correlates with MMP activity, thrombus age, and collagen content, generating stage-dependent T1/T2 ratios. The nanoprobe also enables enzyme-triggered fibrinolytic release, achieving site-specific thrombolysis with minimal hemorrhagic risk. This materials-based strategy translates dynamic microenvironmental remodeling into high-resolution MRI outputs, establishing a programmable framework for precision imaging and therapy. These programmable imaging outputs support data-driven diagnostics, enable clinical treatment stratification, and offer a standardized reference for modeling enzyme-rich pathological environments.

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Colloidal quantum dot-based short-wave infrared (SWIR) photodetectors are often limited by surface traps and high noise levels at room temperature. In this work, we present a low-temperature chemical bath deposition (CBD) strategy to grow a heterojunction passivation layer on HgTe quantum dot (QD) photoactive layers, enabling high-performance SWIR photodetection at room temperature. The CBD process achieves interfacial modification through a dual mechanism: sulfur ions penetrate the HgTe QD surface to form an Hg-S bonded interfacial region while simultaneously reacting with Cd²⁺ in the bath to create a CdS electron-accepting layer, resulting in a compositionally graded CdS/Hg-S/HgTe structure. The resulting interfacial improvement, coupled with energy level modification, facilitates carrier separation and passivates surface defects, thus simultaneously enhancing the responsivity and reducing noise current of photodetectors. As a result, the phototransistor based on the CdS/HgTe photoactive layer demonstrates a high room-temperature specific detectivity of 4.43 × 10¹¹ Jones at 1550 nm and maintains detectivity around 10¹⁰ Jones at extended wavelengths up to 2500 nm. These results underscore the importance of interfacial engineering in colloidal QDs-based photodetectors and demonstrate CBD as a scalable, silicon-compatible passivation approach for achieving cryogen-free SWIR optoelectronic devices.

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In conventional catalytic reactions, the specific surface area of the catalyst plays a pivotal role in determining the reaction rate. Similarly, in piezocatalysis, the modulation of pore structure is equally critical. In this study, BaTiO₃ nanowires were synthesized via a two-step solvothermal process, wherein the pore architecture of the catalyst was engineered by adjusting the reaction solvent and other parameters. This structural optimization not only facilitated the transport of reactants and enhanced the bulk resistivity, but also induced an internal electric field within the catalyst. Consequently, the screening charges generated by the piezoelectric effect were more effectively localized on the sample surface to engage in redox reactions, thereby yielding a highly active piezocatalyst. The optimized catalyst achieved a degradation efficiency of 98% for Rhodamine B (RhB) solution within 10 min, accompanied by a reaction rate constant as high as 0.38 min⁻¹. By integrating experimental observations with finite element simulations, the contribution of the porous structure to the enhanced piezocatalytic activity was elucidated, offering new insights for the future development and application of piezocatalytic materials.

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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