Nanoscale Horizons最新文献

Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. Despite significant efforts and recent advances in GC treatment, therapeutic efficacy remains suboptimal. In recent years, emerging nanomaterials have demonstrated considerable potential for cancer therapy, primarily due to their ability to function as drug carriers that enable targeted and precise delivery of therapeutic agents to tumour tissues. This not only increases therapeutic efficacy but also reduces side effects. Herein, we present a comprehensive review of the major types of nanoformulations, including liposomes, albumin-based nanoparticles (NPs), polymer-based NPs, inorganic NPs, and cell-derived nanomaterials. We also examine recently reported nanoformulations for various GC treatment strategies, such as chemotherapy, radiotherapy, immunotherapy, gene therapy, phototherapy, and combined therapy. We highlight the design concepts and principles underlying these nanoformulations employed in GC treatment. Additionally, we discuss the challenges associated with nanoformulation-based treatments for GC as well as future prospects in this rapidly evolving field.

The rapid evolution of artificial intelligence (AI) computing demands innovative memory technologies that integrate high-speed processing with energy-efficient data storage. Here, we report a mixed-dimensional photomemory device based on a CsPbBr₃/Al₂O₃/MoS₂ architecture, leveraging perovskite quantum dots (PQDs) as a photoactive floating-gate layer, a tunable Al₂O₃ dielectric, and a 2D MoS₂ channel. Optical and electrical characterization studies, including steady-state and time-resolved photoluminescence (PL), Kelvin probe force microscopy (KPFM), and current–voltage measurements, reveal the interplay of dielectric thickness and interfacial effects in governing charge transfer efficiency. By optimizing the Al₂O₃ thickness to 5.5 nm, we achieve precise control over charge transfer dynamics, enabling an optimal charge transfer rate with minimal optical energy (∼sub-pJ) to store a single positive charge in the PQDs. The device exhibits exceptional optoelectronic performance, including a nearly linear correlation between incident photon number and average photocurrent (I_ph(avg)) over two orders of magnitude, multilevel storage capability, and a memory window with a high on/off ratio. These findings establish a robust platform for next-generation perovskite-based photomemories, offering insights into energy-efficient, high-performance optoelectronic systems for advanced AI chip applications.

With the advantages of ultra-sensitivity and high throughput, nanopore technology has now evolved into a versatile tool for a wide range of practical applications, including genomic sequencing, proteomic analysis, and detection of various infectious and noninfectious diseases using biomarkers. Especially for infectious diseases, the rapid diagnosis of pathogenic microorganisms is a critical prerequisite for pandemic control and treatment. It is well known that the whole-genome sequences of some pandemic viruses have been accomplished to provide a high-resolution view of pathogen surveillance. This article reviews the progress of nanopore sensors towards virus detection and clinical applications, focusing on innovative strategies aimed at enhancing the detection efficiency. Intrinsically, the nanopore allows the single-molecule counting of viruses in nanofluidic channels. Some nucleic acid and protein components of the viruses are also potential target candidates for virus detection. Meanwhile, a variety of molecular probes involving aptamers, nucleic acids, peptides and nanoparticles have been designed to improve the detection sensitivity of target viruses. The stochastic sensing mode of nanopores further simplifies the conventional testing process, focusing on the rapid and qualitative identification of multiplex viruses, making it more feasible for portable, point-of-care diagnostics.

This study developed heterogeneous catalysts composed of ZnO and CeO₂ supported on H-ZSM-5 for the direct conversion of methane (CH₄) and carbon dioxide (CO₂) into acetic acid. The acid–base and electronic properties were modulated through oxide impregnation and reduction, aiming to create active sites capable of simultaneously activating both reactants. The samples were characterized by XRD, N₂ physisorption, HRTEM/EDS, NH₃-TPD, CO₂-TPD, TPR, FTIR, XPS, CO₂-DRIFTS, and TGA, and tested in a batch reactor at 300 °C and 10 bar. The catalyst lifetime was evaluated through stability testing. The zeolite framework was preserved, although its properties were modified, resulting in improved CH₄ and CO₂ activation. The reduced catalyst exhibited a high surface area and an efficient distribution of acidic and basic sites, achieving an acetic acid productivity of 1473.40 µmol g⁻¹ h⁻¹ and a conversion rate of 35.12%. The results surpassed those of previous studies, highlighting the potential of the Zn–Ce/H-ZSM-5 system for biogas valorization and greenhouse gas mitigation.

Cuproptosis relies on intracellular copper accumulation and shows great potential in tumor therapy. However, the high content of glutathione (GSH) in tumor cells limits its effectiveness. Furthermore, the mechanism of immune activation mediated by cuproptosis remains unclear. To address this, we developed a cancer cell membrane-coated Cu₂O nanoparticle (TC) to induce cuproptosis in tumor cells. After entering tumor cells via homologous targeting, the TC released Cu²⁺ in the acidic microenvironment. Cu²⁺ are subsequently reduced to Cu⁺ generating hydroxyl radicals through the Fenton reaction. These results led to the downregulation of GSH and eventually sensitized cuproptosis. Microwave (MW)-induced hyperthermia further amplifies these effects. Experimental results demonstrate that TC + MW effectively induces 4T1 cancer cells’ cuproptosis both in vitro and in vivo, significantly inhibiting 4T1 tumor growth with minimal systemic toxicity. The treatment also triggered tumor immunogenic cell death and sensitized T-cell-mediated anti-tumor immunity. TC offers a promising strategy for effective cancer cuproptosis and immunotherapy.

The nanoscale spatial arrangement of T cell receptor (TCR) ligands critically influences their activation potential in CD8⁺ T cells, yet a comprehensive understanding of the molecular landscape induced by engagement with native peptide-MHC class I (pMHC-I) remains incomplete. Using DNA origami nanomaterials, we precisely organize pMHC-I molecules into defined spatial configurations to systematically investigate the roles of valencies, inter-ligand spacings, geometric patterns, and molecular flexibility in regulating T cell function. We find that reducing the inter-ligand spacing to ∼7.5 nm enhances T cell activation by up to eightfold compared to a wider spacing (∼22.5 nm), and that as few as six pMHC-I molecules are sufficient to elicit a robust response. Notably, the geometry of pMHC-I presentation emerges as a key determinant of signaling strength, with hexagonal arrangements proving most effective. In contrast, the introduction of flexible linkers into pMHC-I impairs TCR triggering. Together, these findings define spatial parameters that govern pMHC-I–TCR interactions at the T cell interface and provide design principles for engineering next-generation T cell-based immunotherapies.

The primary aim of our study was to address the problem of transcriptomic data complexity by introducing a novel transcriptomic response index (TRI), compressing the entire transcriptomic space into a single variable, and linking it with the inhaled multiwalled carbon nanotubes (MWCNTs) properties. This methodology allows us to predict fold change values of thousands of differentially expressed genes (DEGs) using a single variable and a single quantitative structure–activity relationship (QSAR) model. In the context of this work, TRI compressed 5167 DEGs into a single variable, explaining 99.9% of the entire transcriptomic space. Further TRI was linked to the properties of inhaled MWCNTs using a nano-QSAR model with statistics R² = 0.83, Q_CV² = 0.8, and Q² = 0.78, which show a high level of goodness-of-fit, robustness, and predictability of the obtained model. By training a nano-QSAR model on fold changes of thousands of DEGs using a single variable, our study significantly contributes not only to new approach methodologies (NAMs) focused on reducing animal testing but also decreases the amount of computational resources needed for work with complex transcriptomic data. Developed during this work, the software called ChemBioML Platform (https://chembioml.com) offers researchers a powerful free-to-use tool for training regulatory acceptable machine learning (ML) models without a strong background in programming. The ChemBioML Platform integrates the ML capabilities of Python with the advanced graphical interface of unreal engine 5, creating a bridge between scientific research and the game development industry.

Surface photocatalysis holds significant promise for converting solar energy into chemical fuels and addressing environmental challenges. While ab initio calculations provide critical insights into the thermodynamic and kinetic aspects of catalytic reactions, applying these methods to surface photocatalysis remains challenging. In this work, we discuss the key challenges that need to be addressed when using ab initio calculations to understand surface photocatalytic processes, the reasons behind these challenges, and the potential directions and opportunities for overcoming them in the future.

Two-photon spontaneous emission (TPSE) is a second-order quantum process with promising applications in quantum optics that remains largely unexplored in molecular systems, which are usually very inefficient emitters. In this work, we model the first molecular two-photon emitters and establish the design rules, highlighting their differences from those governing two-photon absorbers. Using both time-dependent density functional theory and Pariser–Parr–Pople calculations, we calculate TPSE in three π-conjugated molecules and identify a dominant pathway. To overcome the inherently low TPSE rates in vacuum, we propose plasmonic nanoparticle-on-mirror cavities, engineered for degenerate TPSE. Our simulations reveal over 10 orders of magnitude enhancement and radiative efficiencies exceeding 50%. Notably, for nitro-substituted phenylene vinylene in an optimized nanocone-on-mirror structure, the two-photon emission rate surpasses that of vacuum one-photon emission from a unit dipole. These findings open new avenues for efficient and molecular-based on-demand sources of entangled photon pairs.

Computation has consistently served as a significant indicator and direction of social development, and volume, speed, and accuracy are critical factors during development. To accelerate this computational process, various advanced technologies and constantly optimized computational methods have been developed, such as upgrading chip design and proposing quantum and photonic computing. Recently, DNA computing, as a unique computational model distinct from traditional methods, offers remarkable advantages and addresses problems that are difficult to solve with conventional computing. By designing DNA molecules and utilizing their spontaneous reactions, specific types of complex problems can be solved, such as combinatorial optimization, traveling salesman, Sudoku and other nondeterministic polynomial time (NP) problems. Based on the spontaneity of reactions, this type of computation exhibits high parallelism, making DNA computing a viable solution for high-complexity problems. This review presents an overview of the theoretical foundations of DNA computing and summarizes three distinct advantages to over traditional computing: high parallelism, efficient storage, and low energy consumption. Furthermore, based on these advantages, we assess the current state of development in two critical branches of DNA computing: DNA circuit and DNA information storage, and provide unique insights for the future development of DNA computing.