Epitaxial Growth of Two-Dimensional Nonlayered AuCrS2 Materials via Au-Assisted Chemical Vapor Deposition.

Two-dimensional (2D) nonlayered transition metal dichalcogenide (TMD) materials are emergent platforms for various applications from catalysis to quantum devices. However, their limited availability and nonstraightforward synthesis methods hinder our understanding of these materials. Here, we present a novel technique for synthesizing 2D nonlayered AuCrS2 via Au-assisted chemical vapor deposition (CVD). Our detailed structural analysis reveals the layer-by-layer growth of [AuCrS2] units atop an initial CrS2 monolayer, with Au binding to the adjacent monolayer of CrS2, which is in stark contrast with the well-known metal intercalation mechanism in the synthesis of many other 2D nonlayered materials. Theoretical calculations further back the crucial role of Cr in increasing the mobility of Au species and strengthening the adsorption energy of Au on CrS2, thereby aiding the growth throughout the CVD process. Additionally, the resulting free-standing nanoporous AuCrS2 (NP-AuCrS2) exhibits exceptional electrocatalytic properties for the hydrogen evolution reaction.

Colloidal Synthesis of Carbon Dot-ZnSe Nanoplatelet Van der Waals Heterostructures for Boosting Photocatalytic Generation of Methanol-Storable Hydrogen.

Methanol is not only a promising liquid hydrogen carrier but also an important feedstock chemical for chemical synthesis. Catalyst design is vital for enabling the reactions to occur under ambient conditions. This study reports a new class of van der Waals heterojunction photocatalyst, which is synthesized by hot-injection method, whereby carbon dots (CDs) are grown in situ on ZnSe nanoplatelets (NPLs), i.e., metal chalcogenide quantum wells. The resultant organic-inorganic hybrid nanoparticles, CD-NPLs, are able to perform methanol dehydrogenation through CH splitting. The heterostructure has enabled light-induced charge transfer from the CDs into the NPLs occurring on a sub-nanosecond timescale, with charges remaining separated across the CD-NPLs heterostructure for longer than 500 ns. This resulted in significantly heightened H2 production rate of 107 µmole·g-1·h-1 and enhanced photocurrent density up to 34 µA cm-2 at 1 V bias potential. EPR and NMR analyses confirmed the occurrence of α-CH splitting and CC coupling. The novel CD-based organic-inorganic semiconductor heterojunction is poised to enable the discovery of a host of new nano-hybrid photocatalysts with full tunability in the band structure, charge transfer, and divergent surface chemistry for guiding photoredox pathways and accelerating reaction rates.

Scalable-doped Nanoporous 1T″ ReSe2 via a General Surface Co-Alloy Strategy.

Reliable and controllable doping of 2D transition metal dichalcogenides is an efficient approach to tailor their physicochemical properties and expand their functional applications. However, precise control over dopant distribution and scalability of the process remains a challenge. Here, we report a general method to achieve scalable in situ doping of centimeter-sized bicontinuous nanoporous ReSe2 films with transition metal atoms via surface coalloy growth. The distinct strains induced by the bending curvature of nanoporous structures and uniform dopants result in a local 1T' to 1T″ structure phase transition over nanoporous ReSe2 films. The as-prepared nanoporous Ru-ReSe2 with high 1T″ phase exhibits preferable electrochemical activity in hydrogen evolution reaction. The work demonstrates a unique and general approach to synthesize uniformly-doped transition metal dichalcogenides with 3D bicontinuous nanoporous structure, which can be scaled up to batch production for various applications.

Single-Atom Gold Isolated Onto Nanoporous MoSe2 for Boosting Electrochemical Nitrogen Reduction.

The electrocatalytic nitrogen reduction reaction (NRR) provides a promising strategy to convert the abundant but inert N2 into NH3 using renewable energy. Herein, single-atom Au isolated onto bicontinous nanoporous MoSe2 (np-MoSe2 ) is designed as an electrocatalyst for achieving highly efficient NRR catalysis, which exhibits a high Faradaic efficiency (FE) of 37.82% and an NH3 production rate of 30.83 µg h-1 mg-1 at -0.3 V versus a reversible hydrogen electrode (RHE) in 0.1 m Na2 SO4 under ambient conditions. Experimental and theoretical investigations reveal that the introduction of single Au atoms onto np-MoSe2 optimizes the adsorption of NRR intermediates while suppressing the competing HER, thus providing an energetic-favorable process for enhancing the catalytic selectivity toward electrochemical N2 reduction into NH3 .

Stable, Strongly Emitting Cesium Lead Bromide Perovskite Nanorods with High Optical Gain Enabled by an Intermediate Monomer Reservoir Synthetic Strategy.

One-dimensional (1D) semiconductor nanorods are important for numerous applications ranging from optics and electronics to biology, yet the direct synthesis of high-quality metal halide perovskite nanorods remains a challenge. Here, we develop an intermediate monomer reservoir synthetic strategy to realize the controllable growth of uniform and low-defect CsPbBr3 perovskite nanorods. Intermediates composed of CsPb2Br5 and Cs3In2Br9 are obtained through the substitution of Pb2+ with In3+ cations in the template of CsPbBr3 nanocubes and act as a precursor reservoir to gradually release monomers, ensuring both the slow growth rate and low defects of nanorods. We have used branched tris(diethylamino)phosphine as a ligand, which not only has unequal binding energies with different crystal faces to promote the orientation growth but also provides strong steric hindrance to shield the nanorods in solution. Because of minor amount of defects and an effective ligand passivation, in addition to significantly enhanced stability, the perovskite nanorods show a high photoluminescence quantum yield of up to 90% and exhibit a net mode gain of 980 cm-1, the latter being a record value among all the perovskite materials. An extremely low amplified spontaneous emission threshold of 7.5 µJ cm-2 is obtained under excitation by a nanosecond laser, which is comparable to that obtained using femtosecond lasers in other recent studies.

Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm.

Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.

Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles.

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

Rejecting Chaotic Disturbances Using a Super-Exponential-Zeroing Neurodynamic Approach for Synchronization of Chaotic Sensor Systems.

Due to the existence of time-varying chaotic disturbances in complex applications, the chaotic synchronization of sensor systems becomes a tough issue in industry electronics fields. To accelerate the synchronization process of chaotic sensor systems, this paper proposes a super-exponential-zeroing neurodynamic (SEZN) approach and its associated controller. Unlike the conventional zeroing neurodynamic (CZN) approach with exponential convergence property, the controller designed by the proposed SEZN approach inherently possesses the advantage of super-exponential convergence property, which makes the synchronization process faster and more accurate. Theoretical analyses on the stability and convergence advantages in terms of both faster convergence speed and lower error bound within the task duration are rigorously presented. Moreover, three synchronization examples substantiate the validity of the SEZN approach and the related controller for synchronization of chaotic sensor systems. Comparisons with other approaches such as the CZN approach, show the convergence superiority of the proposed SEZN approach. Finally, extensive tests further investigate the impact on convergence performance by choosing different values of design parameter and initial state.

Hydrogen-bonding-mediated synthesis of atomically thin TiO2 films with exposed (001) facets and applications in fast lithium insertion/extraction.

Ultrathin two-dimensional (2D) crystalline materials show high specific surface area (SA) of high energy (HE) facets, imparting a significant improvement in their performances. Herein we report a novel route to synthesize TiO2 nanofilms (NFs) with atomic thickness (<2.0 nm) through a solvothermal reaction mediated by the hydrogen-bonding networks constructed by hydroquinone (HQ). The resultant TiO2 NFs have nearly 100 % exposed (001) facets and give an extremely high SA up to 487 m(2) g(-1) . The synergistic effect of HQ and choline chloride plays a vital role in the formation of TiO2 NFs and in the exposure of HE (001) facets. Because of its ultrathin feature and exposed (001) facet, the N2 -annealled TiO2 NFs showed fast kinetics of lithium insertion/extraction, demonstrating foreseeable applications in the energy storage.

[Prevalence and Risk Factors of Major Depression Disorders in Local Teachers Three Months after Lushan Earthquake].

OBJECTIVE: To determine the prevalence and risk factors of major depression disorder (MDD) in local teachers at an early stage post Lushan earthquake. METHODS: A prospective cohort study was undertaken, with 319 local teachers receiving diagnostic MDD assessment using MINI 14-22 d and 85-95 d after the earthquake, respectively. Possible predictive factors were identified using a self-developed questionnaire. Univariate and multivariate logistic regression analyses were performed to determine the predictors of MDD. RESULTS: Fifteen (4. 70%) local teachers were diagnosed with MDD. Those who had an injured direct relative [odds ratio (OR) = 6. 26], with inhabitable house (OR=4. 26), and felt easy to get sad 14-20 d weeks post earthquake (OR= 11. 45) were more likely to suffer from MDD than others. An anxious emotion 14-20 d post earthquake was a protective factor (OR=0. 14) of MDD. CONCLUSION: Post earthquake MDD in local teachers deserves concerns, especially for those who have injured relatives, with an inhabitable house, and who feel easy to get sad 14-20 d post earthquake.

Enhancing Target Tracking: A Novel Grid-Based Beetle Antennae Search Algorithm and Confusion-Aware Detection.

Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm's rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system's robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model's high discriminative ability. The results also indicate that the model meets the real-time requirements.

PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation.

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation.

Mechanism of electroconvulsive therapy in schizophrenia: a bioinformatics analysis study of RNA-seq data.

OBJECTIVE: The molecular mechanism of electroconvulsive therapy (ECT) for schizophrenia remains unclear. The aim of this study was to uncover the underlying biological mechanisms of ECT in the treatment of schizophrenia using a transcriptional dataset. METHODS: The peripheral blood mRNA sequencing data of eight patients (before and after ECT) and eight healthy controls were analyzed by integrated co-expression network analysis and the differentially expressed genes were analyzed by cluster analysis. Gene set overlap analysis was performed using the hypergeometric distribution of phypfunction in R. Associations of these gene sets with psychiatric disorders were explored. Tissue-specific enrichment analysis, gene ontology enrichment analysis, and protein-protein interaction enrichment analysis were used for gene set organization localization and pathway analysis. RESULTS: We found the genes of the green-yellow module were significantly associated with the effect of ECT treatment and the common gene variants of schizophrenia ( P  = 0.0061; family-wise error correction). The genes of the green-yellow module are mainly enriched in brain tissue and mainly involved in the pathways of neurotrophin, mitogen-activated protein kinase and long-term potentiation. CONCLUSION: Genes associated with the efficacy of ECT were predominantly enriched in neurotrophin, mitogen-activated protein kinase and long-term potentiation signaling pathways.

Morphological and molecular analyses reveal two new species of Grifola (Polyporales) from Yunnan, China.

Species of Grifola are famous edible mushrooms and are deeply loved by consumers around the world. Most species of this genus have been described and recorded in Oceania, Europe and South America, with only Grifolafrondosa being recorded in Asia. In this study, two novel species of Grifola from southwestern China (Asia) are introduced. Macro and micromorphological characters are described. Grifolaedulissp. nov. present medium-size basidiomata with gray to gray-brown lobes upper surface, mostly tibiiform or narrowly clavate, rarely narrowly lageniform or ellipsoid chlamydospores, cuticle hyphae terminal segments slightly enlarged. Grifolasinensissp. nov. has white to grayish white lobes upper surface, mostly ellipsoid, rarely narrowly utriform chlamydospores, and broadly ellipsoid to ellipsoid basidiospores (4.6-7.9 × 3.0-5.9 µm). The two new species are supported by phylogenetic analyses of combined nuclear rDNA internal transcribed spacer ITS1-5.8S-ITS2 rDNA (ITS) and ß-tubulin (TUBB). Moreover, the genetic distance between TUBB sequences of those specimen from GenBank was 1.76-1.9%. Thus, the conspecificity relationship of our specimens remains uncertain, and further specimens are required to conclusively confirm its identity.

Long-term ecological restoration increased plant diversity and soil total phosphorus content of the alpine flowing sand land in northwest Sichuan, China.

The ecological restoration techniques that combine grazing, sand barriers with willows, fertilization, artificial planting, and continuous management are increasingly adopted in the management of flowing sandy land in high-altitude and cold regions. However, few studies have focused on the long-term ecological restoration effects of such technologies. This study systematically compared the vegetation and soil characteristics under different ecological restoration durations (0 (CK), 3 (F1), 14 (F2), 26 (F3), and 46 (F4) years) in the alpine sandy land of northwest Sichuan. The results showed that, with the increase of ecological restoration durations, (1) the aboveground and underground biomass of plants, and species number significantly increased, while the shannon-wiener index, margalef index, and simpson index dramatically decreased; (2) in the early stage of ecological restoration (0-3 yr), Cyperaceae accounted for the main groups, while in the late stage of ecological restoration (14-46 yr), Leguminosae and Forb groups predominated; (3) ecological restoration durations significantly influenced the total phosphorus (TP) content at a soil depth of 0-60 cm, but soil organic carbon and C/P ratio were only significantly impacted at 40-60 cm; (4) the plant and soil characteristics of F1, F2, and F3 treatments were more similar, and CK and F4 treatments were clearly distinguished on PC1 of principal component analysis; (5) there was no significant correlation between Leguminosae groups and environmental factors. Instead, a correlation between total nitrogen (TN) and Forb groups, Gramineae groups, and Cyperaceae groups was revealed. TN was very significantly positively correlated with species diversity and TP. Long-term ecological restoration improved plants biomass, plant species diversity, functional plant groups, and increased soil TP content in the alpine sandy land of northwest Sichuan.

WS2 Moiré Superlattices Supporting Au Nanoclusters and Isolated Ru to Boost Hydrogen Production.

Maximizing the catalytic activity of single-atom and nanocluster catalysts through the modulation of the interaction between these components and the corresponding supports is crucial but challenging. Herein, guided by theoretical calculations, a nanoporous bilayer WS2 Moiré superlattices (MSLs) supported Au nanoclusters (NCs) adjacent to Ru single atoms (SAs) (Ru1/Aun-2LWS2) is developed for alkaline hydrogen evolution reaction (HER) for the first time. Theoretical analysis suggests that the induced robust electronic metal-support interaction effect in Ru1/Aun-2LWS2 is prone to promote the charge redistribution among Ru SAs, Au NCs, and WS2 MSLs support, which is beneficial to reduce the energy barrier for water adsorption and thus promoting the subsequent H2 formation. As feedback, the well-designed Ru1/Aun-2LWS2 electrocatalyst exhibits outstanding HER performance with high activity (η10 = 19 mV), low Tafel slope (35 mV dec-1), and excellent long-term stability. Further, in situ, experimental studies reveal that the reconstruction of Ru SAs/NCs with S vacancies in Ru1/Aun-2LWS2 structure acts as the main catalytically active center, while high-valence Au NCs are responsible for activating and stabilizing Ru sites to prevent the dissolution and deactivation of active sites. This work offers guidelines for the rational design of high-performance atomic-scale electrocatalysts.

A Novel Convolutional Neural Network Model Based on Beetle Antennae Search Optimization Algorithm for Computerized Tomography Diagnosis.

Convolutional neural networks (CNNs) are widely used in the field of medical imaging diagnosis but have the disadvantages of slow training speed and low diagnostic accuracy due to the initialization of parameters before training. In this article, a CNN optimization method based on the beetle antennae search (BAS) optimization algorithm is proposed. The method optimizes the initial parameters of the CNN through the BAS optimization algorithm. Based on this optimization approach, a novel CNN model with a pretrained BAS optimization algorithm was developed and applied to the analysis and diagnosis of medical imaging data for intracranial hemorrhage. Experimental results on 330 test images show that the proposed method has a better diagnostic performance than the traditional CNN. The proposed method achieves a diagnostic accuracy of 93.9394% and 100% recall, and the diagnosis of 66 human head computerized tomography image data only takes 0.1596 s. Moreover, the proposed method has more advantages than the three other optimization algorithms.

Physicochemical properties and interactions of perfluoroalkyl substances (PFAS) - Challenges and opportunities in sensing and remediation.

Per- and polyfluoroalkyl substances (PFAS) is a class of persistent organic pollutants that presents health and environmental risks. PFAS are ubiquitously present in the environment, but current remediation technologies are ineffective in degrading them into innocuous chemicals, especially high energy degradation processes often generate toxic short chain intermediates. Therefore, the best remediation strategy is to first detect the source of pollution, followed by capturing and mineralising or recycling of the compounds. The main objective of this article is to summarise the unique physicochemical properties and to critically review the intermolecular and intramolecular physicochemical interactions of PFAS, and how these interactions can become obstacles; and at the same time, how they can be applied to the PFAS sensing, capturing, and recycling process. The physicochemical interactions of PFAS chemicals are being reviewed in this paper includes, (1) fluorophilic interactions, (2) hydrophobic interactions, (3) electrostatic interactions and cation bridging, (4) ionic exchange and (5) hydrogen bond. Moreover, all the different influential factors to these interactions have also been reported. Finally, properties of these interactions are compared against one another, and the recommendations for future designs of affinity materials for PFAS have been given.

Efficient electrosynthesis of formamide from carbon monoxide and nitrite on a Ru-dispersed Cu nanocluster catalyst.

Conversion into high-value-added organic nitrogen compounds through electrochemical C-N coupling reactions under ambient conditions is regarded as a sustainable development strategy to achieve carbon neutrality and high-value utilization of harmful substances. Herein, we report an electrochemical process for selective synthesis of high-valued formamide from carbon monoxide and nitrite with a Ru1Cu single-atom alloy under ambient conditions, which achieves a high formamide selectivity with Faradaic efficiency of 45.65 ± 0.76% at -0.5 V vs. RHE. In situ X-ray absorption spectroscopy, coupled with in situ Raman spectroscopy and density functional theory calculations results reveal that the adjacent Ru-Cu dual active sites can spontaneously couple *CO and *NH2 intermediates to realize a critical C-N coupling reaction, enabling high-performance electrosynthesis of formamide. This work offers insight into the high-value formamide electrocatalysis through coupling CO and NO2- under ambient conditions, paving the way for the synthesis of more-sustainable and high-value chemical products.

Rapid Melt-Quenching Enables General Synthesis of High-Loading Single-Atom Catalysts with Bicontinuous Nanoporous Structure.

Single-atom catalysts have attracted extensive attention due to their unique atomic structures and extraordinary activities in catalyzing chemical reactions. However, the lack of general and efficient approaches for producing high-density single atoms on suitably tailored supporting matrixes hinders their industrial applications. Here, a rapid melt-quenching strategy with high throughput to synthesize single atoms with high metal-atom loadings of up to 9.7 wt% or 2.6 at% on nanoporous metal compounds is reported, representing several-fold improvements compared to benchmarks in the literature. Mechanism characterizations reveal that the high-temperature melting provides the essential liquid environment and activation energy to achieve the atomization of metals, while the following rapid-quenching pins the isolated metal atoms and stabilizes the coordination environment. In comparison with carbon-supported single-atom catalysts, various collaboration combinations of single atoms and nanoporous metal compounds can be synthesized using the strategy, thus achieving efficient hydrazine oxidation-assisted H2 production. This synthesis protocol is highly compatible with automatic operation, which provides a feasible and general route to design and manufacture specific single-atom catalysts with tunable atomic metal components and supporting matrixes, thus promoting the deployment of single-atom catalysts for various energy technology applications.