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Construction of Z-scheme heterostructure is of great significance for realizing efficient photocatalytic water splitting. However, the conscious modulation of Z-scheme charge transfer is still a great challenge. Herein, interfacial Mo-S bond and internal electric field modulated Z-scheme heterostructure composed by sulfur vacancies-rich ZnIn 2 S 4 and MoSe 2 was rationally fabricated for efficient photocatalytic hydrogen evolution. Systematic investigations reveal that Mo-S bond and internal electric field induce the Z-scheme charge transfer mechanism as confirmed by the surface photovoltage spectra, DMPO spin-trapping electron paramagnetic resonance spectra and density functional theory calculations. Under the intense synergy among the Mo-S bond, internal electric field and S-vacancies, the optimized photocatalyst exhibits high hydrogen evolution rate of 63.21 mmol∙g −1 ·h −1 with an apparent quantum yield of 76.48% at 420 nm monochromatic light, which is about 18.8-fold of the pristine ZIS. This work affords a useful inspiration on consciously modulating Z-scheme charge transfer by atomic-level interface control and internal electric field to signally promote the photocatalytic performance. The construction of Z-scheme heterostructures is of great significance for realizing efficient photocatalytic water splitting. Here, the authors report an interfacial chemical bond and internal electric field modulated Z-Scheme S v -ZnIn 2 S 4 /MoSe 2 photocatalyst for efficient hydrogen evolution.

In catalysis science stability is as crucial as activity and selectivity. Understanding the degradation pathways occurring during operation and developing mitigation strategies will eventually improve catalyst design, thus facilitating the translation of basic science to technological applications. Herein, we reveal the unique and general degradation mechanism of metallic nanocatalysts during electrochemical CO 2 reduction, exemplified by different sized copper nanocubes. We follow their morphological evolution during operation and correlate it with the electrocatalytic performance. In contrast with the most common coalescence and dissolution/precipitation mechanisms, we find a potential-driven nanoclustering to be the predominant degradation pathway. Grand-potential density functional theory calculations confirm the role of the negative potential applied to reduce CO 2 as the main driving force for the clustering. This study offers a novel outlook on future investigations of stability and degradation reaction mechanisms of nanocatalysts in electrochemical CO 2 reduction and, more generally, in electroreduction reactions. While the degradation of materials during usage is crucial in understanding their performance, it is challenging to understand the corrosion processes. Here, authors find copper nanoparticles to undergo an unusual potential-driven nanoclustering degradation pathway during carbon dioxide reduction.

AbstractObjectiveTo study the effect of long term exposure to ambient fine particulate matter of diameter ≤2.5 μm (PM2.5) on the incidence of total, ischemic, and hemorrhagic stroke among Chinese adults.DesignPopulation based prospective cohort study.SettingPrediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project carried out in 15 provinces across China.Participants117 575 Chinese men and women without stroke at baseline in the China-PAR project.Main outcome measuresIncidence of total, ischemic, and hemorrhagic stroke.ResultsThe long term average PM2.5 level from 2000 to 2015 at participants’ residential addresses was 64.9 μg/m3, ranging from 31.2 μg/m3 to 97.0 μg/m3. During 900 214 person years of follow-up, 3540 cases of incident stroke were identified, of which 63.0% (n=2230) were ischemic and 27.5% (n=973) were hemorrhagic. Compared with the first quarter of exposure to PM2.5 (<54.5 μg/m3), participants in the highest quarter (>78.2 μg/m3) had an increased risk of incident stroke (hazard ratio 1.53, 95% confidence interval 1.34 to 1.74), ischemic stroke (1.82, 1.55 to 2.14), and hemorrhagic stroke (1.50, 1.16 to 1.93). For each increase of 10 μg/m3 in PM2.5 concentration, the increased risks of incident stroke, ischemic stroke, and hemorrhagic stroke were 13% (1.13, 1.09 to 1.17), 20% (1.20, 1.15 to 1.25), and 12% (1.12, 1.05 to 1.20), respectively. Almost linear exposure-response relations between long term exposure to PM2.5 and incident stroke, overall and by its subtypes, were observed.ConclusionsThis study provides evidence from China that long term exposure to ambient PM2.5 at relatively high concentrations is positively associated with incident stroke and its major subtypes. These findings are meaningful for both environmental and health policy development related to air pollution and stroke prevention, not only in China, but also in other low and middle income countries.

Background Mango is one of the world’s most important tropical fruits. It belongs to the family Anacardiaceae, which includes several other economically important species, notably cashew, sumac and pistachio from other genera. Many species in this family produce family-specific urushiols and related phenols, which can induce contact dermatitis. Results We generate a chromosome-scale genome assembly of mango, providing a reference genome for the Anacardiaceae family. Our results indicate the occurrence of a recent whole-genome duplication (WGD) event in mango. Duplicated genes preferentially retained include photosynthetic, photorespiration, and lipid metabolic genes that may have provided adaptive advantages to sharp historical decreases in atmospheric carbon dioxide and global temperatures. A notable example of an extended gene family is the chalcone synthase (CHS) family of genes, and particular genes in this family show universally higher expression in peels than in flesh, likely for the biosynthesis of urushiols and related phenols. Genome resequencing reveals two distinct groups of mango varieties, with commercial varieties clustered with India germplasms and demonstrating allelic admixture, and indigenous varieties from Southeast Asia in the second group. Landraces indigenous in China formed distinct clades, and some showed admixture in genomes. Conclusions Analysis of chromosome-scale mango genome sequences reveals photosynthesis and lipid metabolism are preferentially retained after a recent WGD event, and expansion of CHS genes is likely associated with urushiol biosynthesis in mango. Genome resequencing clarifies two groups of mango varieties, discovers allelic admixture in commercial varieties, and shows distinct genetic background of landraces.

Carbon quantum dots (CQDs) as a rising class of carbon family have gained widespread attention in view of their multiple properties such as great photoluminescence (PL) properties, facile synthesis route, needing economical and cheap raw material, high physiochemical stability, and simple functionalization. This makes CQDs highly versatile and with potential for different applications. To date, CQDs-enabled photocatalysts are regarded as one of the most efficient technologies to degrade pollutants in water; however, poor activity under visible light and the recombination of photogenerated electron and hole pairs hinder getting an ideal performance that may be applied on a large scale. Conventional techniques have been modified via a new advanced method. In this review, we highlighted the strategies to improve the activity of conventional semiconductor photocatalysis via coupling with CQDs, and strategies to improve the photocatalytic activity such as functionalization, doping, and Z-scheme heterojunctions were discussed in detail. This review also covered the CQDs heterojunction application in pollutant degradation and discussed several examples with high-performance photocatalytic activity.

Engineering broadband light absorbers is crucial to many applications, including energy-harvesting devices and optical interconnects. The performances of an ideal absorber are that of a black body, a dark material that absorbs radiation at all angles and polarizations. Despite advances in micrometre-thick films, the absorbers available to date are still far from an ideal black body. Here, we describe a disordered nanostructured material that shows an almost ideal black-body absorption of 98–99% between 400 and 1,400 nm that is insensitive to the angle and polarization of the incident light. The material comprises nanoparticles composed of a nanorod with a nanosphere of 30 nm diameter attached. When diluted into liquids, a small concentration of nanoparticles absorbs on average 26% more than carbon nanotubes, the darkest material available to date. By pumping a dye optical amplifier with nanosecond pulses of ∼100 mW power, we harness the structural darkness of the material and create a new type of light source, which generates monochromatic emission (∼5 nm wide) without the need for any resonance. This is achieved through the dynamics of light condensation in which all absorbed electromagnetic energy spontaneously generates single-colour energy pulses. Nanoparticles can absorb most of the incoming light irrespective of incidence angle and polarization and condense it into a monochromatic emission in the presence of a dye.

Naringenin is a natural flavanone with potent pharmacological properties. It has demonstrated therapeutic potential in treating various diseases and organ injuries, including radiation-induced lung injury (RILI). Ferroptosis is a newly type of cell death, and naringenin has been shown to attenuates ferroptosis. To evaluate the inhibitory effect and molecular mechanism of naringenin on ferroptosis during RILI process. Firstly, BEAS-2B and HUVECs cells were pre-incubated with naringenin for 1 h prior to 8 Gy of X-ray irradiation to evaluate oxidative stress, inflammation, and the mRNA levels of ferroptosis-related genes. Next, target genes of naringenin, RILI, and ferroptosis were identified using the TCMSP, SwissTargetPrediction, and GeneCards databases. The target network was constructed with Cytoscape and STRING. Finally, the core target genes were identified through experiments by qRT-PCR, western blot and immunofluorescence staining. Naringenin effectively reduced radiation-induced increasement of oxidative stress, inflammation, and ferroptosis markers in both cell lines. Network pharmacology identified 14 target genes, with prostaglandin endoperoxide synthase (PTGS2) and Valosin-containing protein (VCP) mRNA levels being prominent, which were crucial for ferroptosis regulation. Molecular docking revealed strong binding interactions between naringenin and the two target proteins. Subsequently, experimental validation confirmed that naringenin reduced the elevated levels of PTGS2 and VCP induced by radiation. Naringenin alleviates radiation-induced lung damage by inhibiting ferroptosis, with PTGS2 and VCP emerging as potential therapeutic targets.

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

In recent years, reservoir flood control and dam safety have faced severe challenges due to changing environmental conditions and intense human activities. There has been a significant increase in the proportion of dam breaks caused by floods exceeding reservoir design levels. Dam breaks have periodically occurred due to flood overtopping, threatening people’s lives and properties. This highlights the importance of describing the challenges encountered in reservoir flood risk prevention and control under extreme climatic conditions and proposing strategies to safeguard reservoirs against floods and to protect downstream communities. This study conducts a statistical analysis of dam breaks resulting from floods exceeding reservoir design levels, revealing new risk indicators in these settings. The study examines recent representative engineering cases involving flood risks and reviews research findings pertaining to reservoir flood risks under extreme climatic conditions. By comparing flood prevention standards at typical reservoirs and investigating the problems and challenges associated with current standards, the study presents the challenges and strategies associated with managing flood risks in reservoirs under extreme climatic conditions. The findings show that the driving forces and their effects shaping flood risk characteristics in specific regions are influenced by atmospheric circulation and vegetative changes in underlying surfaces or land use. There is a clear increasing probability of dam breaks or accidents caused by floods exceeding design levels. Most dam breaks or accidents occur in small and medium-sized reservoirs, due to low flood control standards and poor management. Therefore, this paper recommends measures for improving the flood prevention capacity of these specific types of reservoirs. This paper proposes key measures to cope with floods exceeding reservoir design levels, to supplement the existing standard system. This includes implementing an improved flood standard based on dam risk level and the rapid reduction in the reservoir water level. To prevent breaks associated with overtopping, earth–rock dams should be designed to consider extreme rainfall events. More clarity is needed in the execution principles of flood prevention standards, and the effectiveness of flood calculations should be studied, adjusted, and validated. The research results provide better descriptions of flood risks in reservoirs under extreme climatic conditions, and the proposed strategies have both theoretical and practical implications for building resilience against flood risks and protecting people’s lives and properties.

Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especially in highly dynamic industrial environments. However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer–Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. The Pyraformer model effectively captures multi-scale temporal features, while the BLS-based incremental learning mechanism adapts to evolving data without full retraining. The proposed framework enhances both spatial and temporal representation, enabling robust fault detection in high-dimensional and continuously changing industrial environments. Experimental validation on the Tennessee Eastman (TE) process, Continuous Stirred-Tank Reactor (CSTR) system, and injection molding process demonstrated superior detection performance. In the TE scenario, our framework achieved a 100% Fault Detection Rate with a 4.35% False Alarm Rate, surpassing DCCA variants. Similarly, in the CSTR case, the approach reached a perfect 100% Fault Detection Rate (FDR) and 3.48% False Alarm Rate (FAR), while in the injection molding process, it delivered a 97.02% FDR with 0% FAR. The findings underline the framework’s effectiveness in handling complex and dynamic data streams, thereby providing a powerful approach for real-time monitoring and proactive maintenance.