Small Methods最新文献

Aqueous Zn-metal batteries (AZBs) are thought as highly prospective candidates for large-scale energy-storage systems because of their abundant natural resources, low cost, high safety, and environmentally friendly. Nevertheless, the key problems of AZBs are the uncontrollable zinc dendrites growth and water-induced erosion faced by zinc anodes. Therefore, reducing the hydrophilicity of zinc anode and introducing the zincophilic sites are the availably strategy. Herein, 3D highly-conductive host is developed to inhibit Zn dendrites growth, which have a porous structure consisting of graphene and carbon nanotubes embedded with a zincophilic nucleation sites of Zn Prussian blue analogs (ZnHCF@3D-GC). The inner ZnHCF possess minimized nucleation barriers, which can serve as favorable nucleation sites, and 3D host provide a buffer interspace to allow for even more high-capacity Zn plating. Additionally, density functional theory results show that ZnHCF exhibits a strong Zn binding energy and high adsorption energy of Zn (002) plane, which can guide Zn horizontal deposition in the 3D host. As a result, the assembled symmetrical cell is able to stabilize 900 cycles at an ultrahigh current density of 100 mA cm^-2. Zn-ZnHCF@3D-GC//MnO₂ and Zn-ZnHCF@3D-GC//ZnHCF full cells can be stably cycled 1000 cycles at 2.0 A g^-1.

The dynamics of liquids upon impact with an object exhibit distinctive behaviors influenced by physical parameters such as surface tension and viscosity, which can be determined by analyzing a liquid's dynamic response. However, measuring these parameters typically requires different tools, a complicated setup, increased space, and higher costs. To streamline this process, a liquid dynamic sensor capable of simultaneously extracting surface tension and viscosity via a single-step measurement is proposed. The proposed measurement method uses a superhydrophobic sensor comprising three electrode pairs, which are fabricated using laser-induced graphene on polydimethylsiloxane. The sensor monitors time-series resistance changes triggered by liquid impact dynamics. The results show that time-series liquid dynamics on the sensor surface vary with the liquid's surface tension and viscosity, allowing for the differentiation of these properties. By implementing an echo state network algorithm, surface tension and viscosity are successfully estimated simultaneously. In addition, the system demonstrates reliable generalization capability, accurately estimating the properties of unknown liquids, which confirms the proposed sensor's robustness for simultaneous measurement of liquid physical parameters.

Surface and interface engineering of catalysts from atomic level to macroscale exhibit good performance in regulating conversion, selectivity, and stability. Electrospinning offers such multiscale flexibility in tuning surface and interface structures and compositions for the design of fiber catalysts. This review presents an overview on the surface and interface engineering of electrospun nanofibers for heterogeneous catalysts designing. First, the building strategies for regulating catalytic performance on surface and interface at different scales are introduced. Then, typical research achievements of surface and interface regulation strategies of nanofiber catalysts in different scales are summarized, including atomic vacancy and doping at microscale, heterojunction interfaces at mesoscale, and surfaces/interfaces with special wettability at macroscale. The typical catalytic reactions are introduced that involve classical small molecule hydrogenation, oxygen evolution reaction, and pollutant photocatalytic degradation, as well as the recently emerging CO₂ reduction reaction and nitrate/nitrite reduction. Finally, the challenges and future tendency on surface and interface engineering of electrospun nanofiber catalysts are highlighted.

Muscle regeneration is a vital biological process that is crucial for maintaining muscle function and integrity, particularly for the treatment of muscle diseases such as sarcopenia and muscular dystrophy. Generally, muscular tissues can self-repair and regenerate under various conditions, including acute or chronic injuries, aging, and genetic mutation. However, regeneration becomes challenging beyond a certain threshold, particularly in severe muscle injuries or progressive diseases. In recent years, liposome-based nanotechnologies have shown potential as promising therapeutic strategies for muscle regeneration. Liposomes offer an adaptable platform for targeted drug delivery due to their cell membrane-like structure and excellent biocompatibility. They can enhance drug solubility, stability, and targeted delivery while minimizing systemic side effects by different mechanisms. This review summarizes recent advancements, discusses current applications and mechanisms, and highlights challenges and future directions for possible clinical translation of liposome-based nanomaterials in the treatment of muscle diseases. It is hoped this review offers new insights into the development of liposome-enabled nanomedicine to address current limitations.

In recent decades, polymeric graphitic carbon nitride (g-C₃N₄) has garnered significant attention as a class of metal-free semiconductor photocatalysts. However, inherent limitations such as inadequate visible light absorption, low specific surface area, moderate charge transfer efficiency, and poor crystallinity restrict its application. To address the constraints, three novel donor-acceptor type covalent heptazine frameworks (CHFs) are constructed through a bottom-up approach by intergrating heptazine and triazine, which are the fundamental active moieties of g-C₃N₄, with diverse donor spacers. Compared to g-C₃N₄, noteworthy enhancements in photocatalytic activity and hydrogen evolution efficiency are attributed to the increased specific surface areas, broadened visible-light absorption, and accelerated photogenerated charge transfer within the CHFs. Notably, high crystallinity shows a profound influence on the photocatalytic efficiency of the synthesized CHFs. Among the CHFs, highly crystalline CHF-3 stands out to present the highest hydrogen evolution rate of 15284 µmol g^-1 h^-1 under visible-light irradiation (420-780 nm) with ascorbic acid as the hole sacrificial agent. This remarkable achievement represents a 144-fold improvement over g-C₃N₄ and a noteworthy sevenfold enhancement compared to the low-crystalline CHF-3. These results not only offer valuable insights for the design of efficient heptazine-based CHF photocatalysts but also contribute toward the advancement of heptazine-based functional materials.

In a crowded environment, macromolecules occupy a significant proportion volume of cells to repulse other molecules in H₂O-rich phase domains. These H₂O-rich phase domains have been found to significantly influence material transportation and biochemical reactions. However, the accurate quantification of the size of these domains remains a challenge. Here, formulas are set up to calculate the anomalous diffusion exponent (α), the concentration threshold (c_p), and the radius of the H₂O-rich phase domain (r₀) to characterize the crowded solutions. Fitting coefficient (R²) of the r₀ fitted curves are 0.9989 for PEG-8k Da and 0.9901 for PEG-20k Da, respectively, which confirms the formulas to be suitable for quantifying the crowding degree. The values of α, r₀, and c_p of three different cell lysates is are calculated using these formulas. The r₀ values of the cytosol from eukaryotic cells are 1.22 µm for HEK-293T and 1.46 µm for S. Cerevisiae, respectively, which are smaller than that (2.13 µm) from prokaryotic cells (E. coli). This may be due to the more complex components, with higher molecular weight but lower concentration in the eukaryotic cells. This method for quantifying the H₂O-rich phase in a crowded solution helps to have a deeper understanding of the biochemical mechanism inside cells.

Accurate prediction of cancer drug responses is crucial for personalized therapy. Single-cell RNA sequencing (scRNA-seq) captures cellular heterogeneity and rare resistant populations, offering valuable insights into treatment responses. However, the distinct distributions of bulk RNA-seq and scRNA-seq data hinder the transfer of drug response knowledge from large-scale cell line datasets. To address this, single-cell Pathway Drug Sensitivity (scPDS) model is developed, a Transformer-based deep learning method that predicts drug sensitivities from scRNA-seq data through pathway activation transformation. By integrating bulk RNA-seq data from extensive cell line datasets, scPDS improves accuracy and computational efficiency in scRNA-seq analysis. It is demonstrated that scPDS outperforms state-of-the-art methods in both time and memory consumption. When applied to breast cancer cells treated with bortezomib, scPDS showed that resistance increases initially but diminishes with prolonged exposure. The method also identifies drug-sensitive populations in bortezomib-resistant cells and predicts the efficacy of combination therapies, including docetaxel, gemcitabine, and irinotecan. Furthermore, scPDS successfully distinguishes between sensitive and resistant patients, predicting significantly different survival outcomes. In summary, scPDS offers a robust tool for predicting cellular responses, providing insights to optimize cancer treatment strategies.

Spatially resolved transcriptomics (SRT) has emerged as a transformative technology for elucidating cellular organization and tissue architecture. However, a significant challenge remains in identifying pathology-relevant spatial functional landscapes within the tissue microenvironment, primarily due to the limited integration of cell-cell communication dynamics. To address this limitation, SpaDCN, a Spatially Dynamic graph Convolutional Network framework is proposed, which aligns cell-cell communications and gene expression within a spatial context to reveal the spatial functional regions with the coherent cellular organization. To effectively transfer the influence of cell-cell communications on expression variation, SpaDCN respectively generates the node layer and edge layer of spatial graph representation from expression data and the ligand-receptor complex contributions and then employs a dynamic graph convolution to switch the propagation of node graph and edge graph. It is demonstrated that SpaDCN outperforms existing methods in identifying spatial domains and denoising expression across various platforms and species. Notably, SpaDCN excels in identifying marker genes with significant prognostic potential in cancer tissues. In conclusion, SpaDCN offers a powerful and precise tool for spatial domain detection in spatial transcriptomics, with broad applicability across various tissue types and research disciplines.

Large-scale fabrication and optimization of high-quality polycrystalline perovskite thin films present significant challenges in scientific research and industry. Shifting from single-spot measurements to imaging techniques facilitates the transition from laboratory-scale to large-scale processing. While single-spot photoluminescence (PL) methods provide high-depth insights into local optoelectronic characteristics, they are insufficient for assessing reliable information on homogeneity and spatial characteristics. Currently, no PL-based imaging method delivers a comparable level of information depth on charge carrier dynamics to single-spot PL methods. In response, this work introduces a non-invasive imaging technique based on double-pulse excitation. Varying the time delay between the pulses gives rise to spatial information on relative photoluminescence quantum yield (rPLQY) in thin films, yielding fundamental optoelectronic characteristics such as external radiative and effective non-radiative recombination rates and charge-carrier lifetime. Compared to traditional PL-imaging and k-imaging demonstrates the superiority of rPLQY-imaging in revealing charge carrier dynamics. This technique proves applicable across various sample configurations, irrespective of the presence of electrodes and charge transport layers. In addition, the method can estimate surface recombination velocity and variations in escape and parasitic absorption probabilities. Overall, the rPLQY-imaging method emerges as a valuable tool for scientific research aimed at characterizing and optimizing large-area perovskite thin films.

A facile in situ method of the liquid/liquid (L/L) polymerization strategy for synthesizing silver-doped hollandite manganese oxide (Ag-HMO) on polypyrrole (PPy) support is reported for the first time. The highly innovative synthetic method involves producing α-MnO₂ attached to PPy oligomers under low-temperature conditions. Subsequently, Ag⁺ ions are in situ intercalated into the 2 × 2 tunnels in α-MnO₂ to generate Ag-HMO-incorporated PPy. Calculations based on density functional theory (DFT) yield negative formation energies, suggesting that Ag-HMO can be formed through the tunnel doping of Ag⁺ in α-MnO₂. Highly crystalline 2D composite mats of Ag-HMO/PPy (PAgMn) with interconnected Ag-HMO nanorod networks with a thickness of ≈1 nm are demonstrated by electron and atomic force microscopy images. Electrochemical detection of formaldehyde on PAgMn-modified screen-printed electrodes opens new prospects for real-time food adulterant sensors. PAgMn is also utilized as electrodes for supercapacitors with a high specific capacitance of 601 mF cm^-2. An all-solid-state asymmetric supercapacitor device assembled with PAgMn and activated carbon as negative and positive electrodes demonstrates outstanding energy storage capability with a remarkable energy density of 6.16 mWh cm^-2 at a power density of 6300 mW cm^-2.