Nano Convergence最新文献

Ischemia-reperfusion injury (IRI) poses significant challenges across various organ systems, including the heart, brain, and kidneys. Exosomes have shown great potentials and applications in mitigating IRI-induced cell and tissue damage through modulating inflammatory responses, enhancing angiogenesis, and promoting tissue repair. Despite these advances, a more systematic understanding of exosomes from different sources and their biotransport is critical for optimizing therapeutic efficacy and accelerating the clinical adoption of exosomes for IRI therapies. Therefore, this review article overviews the administration routes of exosomes from different sources, such as mesenchymal stem cells and other somatic cells, in the context of IRI treatment. Furthermore, this article covers how the delivered exosomes modulate molecular pathways of recipient cells, aiding in the prevention of cell death and the promotions of regeneration in IRI models. In the end, this article discusses the ongoing research efforts and propose future research directions of exosome-based therapies.

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This review reports diverse microfluidic systems utilizing surface-enhanced Raman scattering (SERS) detection for disease diagnosis. Integrating SERS detection technology, providing high-sensitivity detection, and microfluidic technology for manipulating small liquid samples in microdevices has expanded the analytical capabilities previously confined to larger settings. This study explores the principles and uses of various SERS-based microfluidic devices developed over the last two decades. Specifically, we investigate the operational principles of documented SERS-based microfluidic devices, including continuous-flow channels, microarray-embedded microfluidic channels, droplet microfluidic channels, digital droplet channels, and gradient microfluidic channels. We also examine their applications in biomedical diagnostics. In conclusion, we summarize the areas requiring further development to translate these SERS-based microfluidic technologies into practical applications in clinical diagnostics.

Nanomedicine has been extensively explored for therapeutic and diagnostic applications in recent years, owing to its numerous advantages such as controlled release, targeted delivery, and efficient protection of encapsulated agents. Integration of microneedle technologies with nanomedicine has the potential to address current limitations in nanomedicine for drug delivery including relatively low therapeutic efficacy and poor patient compliance and enable theragnostic uses. In this Review, we first summarize representative types of nanomedicine and describe their broad applications. We then outline the current challenges faced by nanomedicine, with a focus on issues related to physical barriers, biological barriers, and patient compliance. Next, we provide an overview of microneedle systems, including their definition, manufacturing strategies, drug release mechanisms, and current advantages and challenges. We also discuss the use of microneedle-mediated nanomedicine systems for therapeutic and diagnostic applications. Finally, we provide a perspective on the current status and future prospects for microneedle-mediated nanomedicine for biomedical applications.

Tunability of interfacial effects between two-dimensional (2D) crystals is crucial not only for understanding the intrinsic properties of each system, but also for designing electronic devices based on ultra-thin heterostructures. A prerequisite of such heterostructure engineering is the availability of 2D crystals with different degrees of interfacial interactions. In this work, we report a controlled epitaxial growth of monolayer TaSe₂ with different structural phases, 1H and 1 T, on a bilayer graphene (BLG) substrate using molecular beam epitaxy, and its impact on the electronic properties of the heterostructures using angle-resolved photoemission spectroscopy. 1H-TaSe₂ exhibits significant charge transfer and band hybridization at the interface, whereas 1 T-TaSe₂ shows weak interactions with the substrate. The distinct interfacial interactions are attributed to the dual effects from the differences of the work functions as well as the relative interlayer distance between TaSe₂ films and BLG substrate. The method demonstrated here provides a viable route towards interface engineering in a variety of transition-metal dichalcogenides that can be applied to future nano-devices with designed electronic properties.

We report an innovative and facile approach to fabricating an ultrasensitive plasmonic paper substrate for surface-enhanced Raman spectroscopy (SERS). The approach exploits the self-assembling capability of poly(styrene-b-2-vinyl pyridine) block copolymers to form a thin film at the air-liquid interface within the single microdroplet scale for the first time and the subsequent in situ growth of silver nanoparticles (AgNPs). The concentration of the block copolymer was found to play an essential role in stabilizing the droplets during the mass transfer phase and formation of silver nanoparticles, thus influencing the SERS signals. SEM analysis of the morphology of the plasmonic paper substrates revealed the formation of spherical AgNPs evenly distributed across the surface of the formed copolymer film with a size distribution of 47.5 nm. The resultant enhancement factor was calculated to be 1.2 × 10⁷, and the detection limit of rhodamine 6G was as low as 48.9 pM. The nanohybridized plasmonic paper was successfully applied to detect two emerging pollutants—sildenafil and flibanserin—with LODs as low as 1.48 nM and 3.45 nM, respectively. Thus, this study offers new prospects for designing an affordable and readily available, yet highly sensitive, paper-based SERS substrate with the potential for development as a lab-on-a-chip device.

Stretchable and self-adhesive conductive hydrogels hold significant importance across a wide spectrum of applications, including human–machine interfaces, wearable devices, and soft robotics. However, integrating multiple properties, such as high stretchability, strong interfacial adhesion, self-healing capability, and sensitivity, into a single material poses significant technical challenges. Herein, we present a multifunctional conductive hydrogel based on poly(acrylic acid) (PAA), dopamine-functionalized pectin (PT-DA), polydopamine-coated reduction graphene oxide (rGO-PDA), and Fe³⁺ as an ionic cross-linker. This hydrogel exhibits a combination of high stretchability (2000%), rapid self-healing (~ 94% recovery in 5 s), and robust self-adhesion to various substrates. Notably, the hydrogel demonstrates a remarkable skin adhesion strength of 85 kPa, surpassing previous skin adhesive hydrogels. Furthermore, incorporating rGO within the hydrogel network creates electric pathways, ensuring excellent conductivity (0.56 S m^–1). Consequently, these conductive hydrogels exhibit strain-sensing properties with a significant increase in gauge factor (GF) of 14.6, covering an extensive detection range of ~ 1000%, fast response (198 ms) and exceptional cycle stability. These multifunctional hydrogels can be seamlessly integrated into motion detection sensors capable of distinguishing between various strong or subtle movements of the human body.

An elementary review on principles of qubits and their prospects for quantum computing is provided. Due to its rapid development, quantum computing has attracted considerable attention as a core technology for the next generation and has demonstrated its potential in simulations of exotic materials, molecular structures, and theoretical computer science. To achieve fully error-corrected quantum computers, building a logical qubit from multiple physical qubits is crucial. The number of physical qubits needed depends on their error rates, making error reduction in physical qubits vital. Numerous efforts to reduce errors are ongoing in both existing and emerging quantum systems. Here, the principle and development of qubits, as well as the current status of the field, are reviewed to provide information to researchers from various fields and give insights into this promising technology.

Tissue engineering and regenerative medicine have made great progress in recent decades, as the fields of bioengineering, materials science, and stem cell biology have converged, allowing tissue engineers to replicate the structure and function of various levels of the vascular tree. Nonetheless, the lack of a fully functional vascular system to efficiently supply oxygen and nutrients has hindered the clinical application of bioengineered tissues for transplantation. To investigate vascular biology, drug transport, disease progression, and vascularization of engineered tissues for regenerative medicine, we have analyzed different approaches for designing microvascular networks to create models. This review discusses recent advances in the field of microvascular tissue engineering, explores potential future challenges, and offers methodological recommendations.

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

Traditional methods for developing new materials are no longer sufficient to meet the needs of the human energy transition. Machine learning (ML) artificial intelligence (AI) and advancements have caused materials scientists to realize that using AI/ML to accelerate the development of new materials for batteries is a powerful potential tool. Although the use of certain fixed properties of materials as descriptors to act as a bridge between the two separate disciplines of AI and materials chemistry has been widely investigated, many of the descriptors lack universality and accuracy due to a lack of understanding of the mechanisms by which AI/ML operates. Therefore, understanding the underlying operational mechanisms and learning logic of AI/ML has become mandatory for materials scientists to develop more accurate descriptors. To address those challenges, this paper reviews previous work on AI, machine learning and materials descriptors and introduces the basic logic of AI and machine learning to help materials developers understand their operational mechanisms. Meanwhile, the paper also compares the accuracy of different descriptors and their advantages and disadvantages and highlights the great potential value of accurate descriptors in AI/machine learning applications for battery research, as well as the challenges of developing accurate material descriptors.

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