Explore the vast range of titles available.

Photocatalytic hydrogen production has been considered a promising approach to obtain green hydrogen energy. Crystalline porous materials have arisen as key photocatalysts for efficient hydrogen production. Here, we report a strategy to in situ photodeposit platinum clusters as cocatalyst on a covalent organic framework, which makes it an efficient photocatalyst for light-driven hydrogen evolution. Periodically dispersed adsorption sites of platinum species are constructed by introducing adjacent hydroxyl group and imine-N in the region of the covalent organic framework structural unit where photogenerated electrons converge, leading to the in situ reduction of the adsorbed platinum species into metal clusters by photogenerated electrons. The widespread platinum clusters on the covalent organic framework expose large active surface and greatly facilitate the electron transfer, finally contributing to a high photocatalytic hydrogen evolution rate of 42432 μmol g −1  h −1 at 1 wt% platinum loading. This work provides a direction for structural design on covalent organic frameworks to precisely manipulate cocatalyst morphologies and positions at the atomic level for developing efficient photocatalysts. Porous covalent organic frameworks have arisen as tunable photocatalysts for H 2 production. Here, authors report frameworks engineered with co-catalyst binding sites to improve photocatalytic H 2 evolution performances.

Corneal blindness is a significant reason for visual impairment globally. Researchers have been investigating several methods for corneal regeneration in order to cure these patients. Biomaterials are favored due to their biocompatibility and capacity to promote cell adhesion. A variety of natural and synthetic biomaterials, along with decellularized cornea, have been employed in corneal wound healing. Commonly utilized natural biomaterials encompass proteins such as collagen, gelatin, and silk fibroin (SF), as well as polysaccharides including alginate, chitosan (CS), hyaluronic acid (HA), and cellulose. Synthetic biomaterials primarily consist of polyvinyl alcohol (PVA), poly(ε‐caprolactone) (PCL), and poly (lactic‐co‐glycolic acid) (PLGA). Bio‐based materials and their composites are primarily utilized as hydrogels, films, scaffolds, patches, nanocapsules, and other formats for the treatment of blinding ocular conditions, including corneal wounds, corneal ulcers, corneal endothelium, and stromal defects. This review attempts to summarize in vitro, preclinical, and clinical trial studies relevant to corneal regeneration using biomaterials within the last five years, and expect that these experiences and outcomes will inspire and provide practical strategies for the future development of biomaterials for corneal regeneration. Furthermore, potential improvements and difficulties for these biomaterials are discussed. A variety of natural and synthetic biomaterials, along with decellularized cornea, have been employed in corneal wound healing. The current progress of biomaterials is summarized for corneal regeneration, and expect that these experiences and outcomes will inspire and provide practical strategies for the future development of corneal regeneration. Furthermore, potential improvements and difficulties for these biomaterials are discussed.

This paper examines the use of blockchain technology in power battery echelon recycling. The technology helps to improve battery capacity identification and market transaction trust. The study focuses on power battery manufacturers and recycling participants. Two recycling modes are constructed using the Stackelberg game method, and the optimal decision-making of the participating subjects in the two modes of power battery echelon recycling under the embedding of blockchain technology is compared. The influence of each parameter on the optimal decision-making is analyzed. The research findings indicate that the degree of blockchain technology integration rises as the preference coefficient for traceability information increases. When recycling competition is intense and the sensitivity of recycling prices is low, the optimal recycling model for the number of spent power batteries (SPBs) to be recycled is the model in which echelon utilizers do not participate in recycling if the level of cost optimization coefficient embedded in blockchain technology is high, otherwise, it is the model in which echelon utilizers participate in recycling. The profit of power battery manufacturers and echelon utilizers decreases with the increase of the intensity of power battery recycling competition, the cost optimization coefficient of echelon utilizers and the cost optimization coefficient of manufacturers.

The digital transformation of high-end equipment manufacturing enterprises serves as a critical driver for upgrading the manufacturing value chain and achieving high-quality development. This paper constructs an evaluation index system for assessing the digital transformation level of high-end equipment manufacturing enterprises based on the Input-Process-Output (I-P-O) theoretical model. It employs the VHSD-EM model to evaluate the digital transformation levels of 124 such enterprises from 2016 to 2021. Additionally, the barrier model is utilized to analyze the primary obstacles affecting their digital transformation. The findings indicate that (1) overall, the digital transformation levels of high-end equipment manufacturing enterprises exhibited an upward trend from 2016 to 2021, though the growth rate was slow, with relatively few enterprises achieving outstanding transformation levels. Notable differences in scores and changes were observed across five key fields. (2) An indicator perspective reveals that the primary obstacles from 2016 to 2021 are concentrated within the top five, with most showing a slight upward trend. Conversely, from a criteria perspective, the challenges primarily involve enterprise awareness of digital transformation and the process itself, demonstrating a slight downward trend.

Solar‐driven interfacial evaporation (SDIE) has attracted great attention by offering a zero‐carbon‐emission solution for clean water production. The manipulation of the surface structure of the evaporator markedly promotes the enhancement of light capture and the improvement of evaporation performance. Herein, inspired by seedless lotus pod, a flexible pristine polypyrrole (PPy) membrane with macro/micro‐bubble and nanotube asymmetric structure is fabricated through template‐assisted interfacial polymerization. The macro‐ and micro‐hierarchical structure of the open bubbles enable multiple reflections inner and among the bubble cavities for enhanced light trapping and omnidirectional photothermal conversion. In addition, the multilevel structure (macro/micro/nano) of the asymmetric PPy (PPy‐A) membrane induces water evaporation in the form of clusters, leading to a reduction of water evaporation enthalpy. The PPy‐A membranes achieve a full‐spectrum light absorption of 96.3% and high evaporation rate of 2.03 kg m−2 h−1 under 1 sun. Long‐term stable desalination is also verified with PPy‐A membranes by applying one‐way water channel. This study demonstrates the feasibility of pristine PPy membranes in SDIE applications, providing guidelines for modulation of the evaporator topologies toward high‐efficient solar evaporation. A seedless lotus‐pod‐inspired asymmetric pristine PPy (PPy‐A) membrane is fabricated via template‐assisted interfacial polymerization for solar‐driven interfacial evaporation. The hierarchical macro/micro open bubbles enhance light absorption of PPy‐A membrane, facilitating omnidirectional photothermal conversion. The unique multilevel structure of PPy‐A membrane induces water evaporation in cluster form, leading to low evaporation enthalpy, achieving highly efficient solar evaporation.

Changes in lifestyle induce an increase in patients with hyperuricemia (HUA), leading to gout, gouty arthritis, renal damage, and cardiovascular injury. There is a strong inflammatory response in the process of HUA, while dysregulation of immune cells, including monocytes, macrophages, and T cells, plays a crucial role in the inflammatory response. Recent studies have indicated that urate has a direct impact on immune cell populations, changes in cytokine expression, modifications in chemotaxis and differentiation, and the provocation of immune cells by intrinsic cells to cause the aforementioned conditions. Here we conducted a detailed review of the relationship among uric acid, immune response, and inflammatory status in hyperuricemia and its complications, providing new therapeutic targets and strategies.

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA- seq ) and bulk RNA- seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS , TP53 , and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA- seq and bulk RNA- seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

Background Graphene quantum dots (GQDs) provide a bright prospect in the biomedical application because they contain low-toxic compounds and promise imaging of deep tissues and tiny vascular structures. However, the biosafety of this novel QDs has not been thoroughly evaluated, especially in the central nervous system (CNS). The microarray analysis provides a hint that nitrogen-doped GQDs (N-GQDs) exposure could cause ferroptosis in microglia, which is a novel form of cell death dependent on iron overload and lipid peroxidation. Results The cytosolic iron overload, glutathione (GSH) depletion, excessive reactive oxygen species (ROS) production and lipid peroxidation (LPO) were observed in microglial BV2 cells treated with N-GQDs, which indicated that N-GQDs could damage the iron metabolism and redox balance in microglia. The pre-treatments of a specific ferroptosis inhibitor Ferrostatin-1 (Fer-1) and an iron chelater Deferoxamine mesylate (DFO) not only inhibited cell death, but also alleviated iron overload, LPO and alternations in ferroptosis biomarkers in microglia, which were caused by N-GQDs. When assessing the potential mechanisms of N-GQDs causing ferroptosis in microglia, we found that the iron content, ROS generation and LPO level in mitochondria of BV2 cells all enhanced after N-GQDs exposure. When the antioxidant ability of mitochondria was increased by the pre-treatment of a mitochondria targeted ROS scavenger MitoTEMPO, the ferroptotic biological changes were effectively reversed in BV2 cells treated with N-GQDs, which indicated that the N-GQDs-induced ferroptosis in microglia could be attributed to the mitochondrial oxidative stress. Additionally, amino functionalized GQDs (A-GQDs) elicited milder redox imbalance in mitochondria and resulted in less ferroptotic effects than N-GQDs in microglia, which suggested a slight protection of amino group functionalization in GQDs causing ferroptosis. Conclusion N-GQDs exposure caused ferroptosis in microglia via inducing mitochondrial oxidative stress, and the ferroptotic effects induced by A-GQDs were milder than N-GQDs when the exposure method is same. This study will not only provide new insights in the GQDs-induced cell damage performed in multiple types of cell death, but also in the influence of chemical modification on the toxicity of GQDs.

To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time−frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time−frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time–frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre−training and supervised inverse fine−tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN−based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.

Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence. The diffusion model component, built on the UNet architecture, effectively captures multiscale features with rich contextual and edge information from images. The lightweight classification module, which leverages spatial-channel attention, focuses more efficiently on spatial-channel regions with significant feature information. We evaluated our approach using three publicly available datasets: Postdam, GID, and Five Billion Pixels. In the test of three datasets, our method achieved the best results. On the GID dataset, the overall accuracy was 96.99%, the mean IoU was 92.17%, and the mean F1 score was 95.83%. In the training phase, our model achieved good performance after only 30 training cycles. Compared with other models, our method reduces the number of parameters, improves the training speed, and has obvious performance advantages.