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Abstract We investigate the process $$B\rightarrow \bar{D}D_s \pi $$ B → D ¯ D s π via several rescattering processes. It is shown that the triangle singularity (TS) peak around the $$D^*K^*$$ D ∗ K ∗ threshold generated from the $$\chi _{c1}K^* D^*$$ χ c 1 K ∗ D ∗ loop is relatively narrow, which may simulate the resonance-like structure $$T_{c\bar{s}}(2900)$$ T c s ¯ ( 2900 ) recently observed by LHCb in the $$D_s\pi $$ D s π spectrum. However, the TS peak around the $$D_s^*\rho $$ D s ∗ ρ threshold generated from the $$D^{**} D_s^* \rho $$ D ∗ ∗ D s ∗ ρ loop is smoothed by the broad width of $$\rho $$ ρ , which itself can hardly describe the $$T_{c\bar{s}}(2900)$$ T c s ¯ ( 2900 ) structure. A non-resonance TS signal around the DK threshold generated from the $$\chi _{c0}K D $$ χ c 0 K D loop is also predicted.

Electrochemical CO 2 reduction (CO 2 RR) is a promising technology to produce value-added fuels and weaken the greenhouse effect. Plenty of efforts are devoted to exploring high-efficiency electrocatalysts to tackle the issues that show poor intrinsic activity, low selectivity for target products, and short-lived durability. Herein, density functional theory calculations are firstly utilized to demonstrate guidelines for design principles of electrocatalyst, maximum exposure of catalytic active sites for MoS 2 edges, and electron transfer from N-doped carbon (NC) to MoS 2 edges. Based on the guidelines, a hierarchical hollow electrocatalyst comprised of edge-exposed 2H MoS 2 hybridized with NC for CO 2 RR is constructed. In situ atomic-scale observation for catalyst growth is performed by using a specialized Si/SiN x nanochip at a continuous temperature-rise period, which reveals the growth mechanism. Abundant exposed edges of MoS 2 provide a large quantity of active centers, which leads to a low onset potential of ≈40 mV and a remarkable CO production rate of 34.31 mA cm −2 with 92.68% of Faradaic efficiency at an overpotential of 590 mV. The long-term stability shows negligible degradation for more than 24 h. This work provides fascinating insights into the construction of catalysts for efficient CO 2 RR.

Converting carbon dioxide (CO2) into liquid fuels and synthesis gas is a world-wide priority. But there is no experimental information on the initial atomic level events for CO2 electroreduction on the metal catalysts to provide the basis for developing improved catalysts. Here we combine ambient pressure X-ray photoelectron spectroscopy with quantum mechanics to examine the processes as Ag is exposed to CO2 both alone and in the presence of H2O at 298 K. We find that CO2 reacts with surface O on Ag to form a chemisorbed species (O = CO2δ-). Adding H2O and CO2 then leads to up to four water attaching on O = CO2δ- and two water attaching on chemisorbed (b-)CO2. On Ag we find a much more favorable mechanism involving the O = CO2δ- compared to that involving b-CO2 on Cu. Each metal surface modifies the gas-catalyst interactions, providing a basis for tuning CO2 adsorption behavior to facilitate selective product formations.

Abstract Recently the LHCb collaboration reported a new exotic state $$T^+_{cc}$$ T cc + which possesses $$cc\bar{u}\bar{d}$$ c c u ¯ d ¯ flavor structure. Since its mass is very close to the threshold of $$D^0D^{*+}$$ D 0 D ∗ + (or $$D^{*0}D^{+}$$ D ∗ 0 D + ) and its width is very narrow, it is inclined to conjecture that $$T^+_{cc}$$ T cc + is a molecular state of $$D^0D^{*+}$$ D 0 D ∗ + (or $$D^{*0}D^{+}$$ D ∗ 0 D + ). In this paper we study the possible molecular structures of $$D^{(*)}D^{(*)}$$ D ( ∗ ) D ( ∗ ) and $$B^{(*)}B^{(*)}$$ B ( ∗ ) B ( ∗ ) within the Bethe–Salpeter (B–S) framework. We employ one boson exchange model to stand the interaction kernels in the B–S equations. With reasonable input parameters we find the isospin eigenstate $$\frac{1}{\sqrt{2}}(D^0D^{*+}-D^{*0}D^{+})$$ 1 2 ( D 0 D ∗ + - D ∗ 0 D + ) ( $$J^P=1^+$$ J P = 1 + ) constitutes a solution, which supports the ansatz of $$T^+_{cc}$$ T cc + being a molecular state of $$D^0D^{*+}$$ D 0 D ∗ + (or $$D^{*0}D^{+}$$ D ∗ 0 D + ). With the same parameters we also find that the isospin-1 state $$\frac{1}{\sqrt{2}}(D^{*0}D^{*+}+D^{*0}D^{*+})$$ 1 2 ( D ∗ 0 D ∗ + + D ∗ 0 D ∗ + ) ( $$J^P=0^+$$ J P = 0 + ) can exist. Moreover, we also study the systems of $$B^{(*)}B^{(*)}$$ B ( ∗ ) B ( ∗ ) and their counterparts exist as possible molecular states. Consistency of theoretical computations based on such states with the data of the future experiments may consolidate the molecular structure of the exotic state $$T^+_{cc}$$ T cc + .

In recent years,the research on the methods of text to image based on generative adversarial network (GAN) continues to grow in popularity and have made some progress.The key of text-to-image generation technology is to build a bridge between the text information and the visual information,and promote the model to generate realistic images consistent with the corresponding text description.At present,the mainstream method is to complete the encoding of the descriptions of the input text by pre-training the text encoder,but these methods do not consider the semantic alignment with the corresponding image in the text encoder,and adopt the independent encoding of the input text,ignoring the semantic gap between the language space and the image space.To address the problem,in this paper,a generative adversarial network based on the cross-attention encoder (CAE-GAN) is proposed.The network uses a cross-attention encoder to translate and align text information with visual information,and captures the cross-modal mapping relationship between text and image information,so as to improve the fidelity of the gene-rated images and the matching degree with input text description.The experimental results show that,compared with the DM-GAN model,the inception score (IS) of CAE-GAN model increases by 2.53% and 1.54% on CUB and coco datasets,respectively.The fréchet inception distance score decreases by 15.10% and 5.54%,respectively,indicating that the details and the quality of the images generated by the CAE-GAN model are more perfect.

Subnanometer metal clusters show advantages over conventional metal nanoparticles in numerous catalytic reactions owing to their high percentage of exposed surface sites, abundance of under‐coordinated metal sites and unique electronic structures. However, the applications of subnanometer metal clusters in high‐temperature catalytic reactions (>600 °C) are still hindered, because of their low stability under harsh reaction conditions. In this work, we have developed a zeolite‐confined bimetallic PtIn catalyst with exceptionally high stability against sintering. A combination of experimental and theoretical studies shows that the isolated framework In(III) species serve as the anchoring sites for Pt species, precluding the migration and sintering of Pt species in the oxidative atmosphere at ≥650 °C. The catalyst comprising subnanometer PtIn clusters exhibits long‐term stability of >1000 h during a cyclic reaction‐regeneration test for ethane dehydrogenation reaction. [ABSTRACT FROM AUTHOR]

The particle size distribution (PSD) of eroded sediment can effectively reflect changes in erosion processes. Hedgerows provide significant soil and water conservation benefits and influence erosion processes. However, the impact of hedgerows on the PSD of eroded sediment remains unclear. Therefore, three slope conditions (control check, CK; only hedgerow root, R; and whole hedgerow, P) involving the configuration of two slope gradients (15 and 25°) were established, and simulated rainfall experiments were conducted under three rainfall intensities (60, 90, and 120 mm/h). The PSD, sediment sorting process, and sediment particle agglomeration degree under the different slope conditions were compared to evaluate the influence of hedgerows on the size selectivity of eroded sediment during water erosion. Moreover, the direct and indirect effects of hedgerows on the sorting process and sediment particle agglomeration degree were revealed via correlation analysis and partial least squares path modelling (PLS–PM). Compared with those under CK slope conditions, the average clay, fine silt, and coarse silt contents under R slope conditions decreased by 1.37%, 0.06%, and 1.71%, respectively, whereas the sand content increased by 0.46%. Under P slope conditions, the average clay, fine silt, and coarse silt contents increased by 32.61%, 38.71%, and 36.14%, respectively, whereas the average sand content decreased by 47.30%. Under CK slope conditions, sediment sorting occurred primarily during the early stages of rainfall. R slope conditions caused an increase in the duration of early‐stage sediment sorting but caused a reduction in the degree of sorting, whereas P slope conditions exhibited interceptive sorting of sand throughout the rainfall event. Under R slope conditions, more aggregates were dispersed into individual particles due to raindrop impact, whereas under P slope conditions, more fine particles aggregated initially, but loose aggregates were dispersed into individual particles during the later rainfall stages. The enrichment rates of fine silt and coarse silt were positively correlated with slope conditions, whereas they were negatively correlated with the enrichment rate of sand. However, there was no significant relationship between the slope conditions and the aggregation rates of particles of various sizes. PLS–PM indicated that hedgerows directly and indirectly affect sorting processes and sediment particle agglomeration degree by regulating runoff and sediment erosion, and the direct effects exceed the indirect effects. These findings provide a comprehensive understanding of the influential mechanisms of hedgerows on controlling soil erosion. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate a method of measuring the flexural elastic modulus of ceramics at an intermediate (~millimeter) scale at high temperatures. We used a picosecond laser to precisely cut microbeams from the location of interest in a bulk ceramic. They had a cross-section of approximately 100 μm × 300 μm and a length of ~1 cm. They were then tested in a thermal mechanical analyzer at room temperature, 500 °C, 800 °C, and 1100 °C using the four-point flexural testing method. We compared the elastic moduli of high-purity Al2O3 and AlN measured by our method with the reported values in the literature and found that the difference was less than 5% for both materials. This paper provides a new and accurate method of characterizing the high-temperature elastic modulus of miniature samples extracted from representative/selected areas of bulk materials. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR‐related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in‐depth reviews SAR applications for geosciences, broadly including various aspects in air‐sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability. Plain Language Summary: Synthetic aperture radar (SAR) uses microwaves to remotely see the Earth's surface under all weather conditions, day and night. SAR has been providing high‐resolution images for many decades and they have been applied to many fields in geosciences. Several SAR sensors have been launched in recent years, significantly increasing the SAR data volume and leading to great developments in SAR technology, thereby improving our understanding of geophysical phenomena and processes. This work comprehensively overviews the application of SAR in geosciences, including oceanography, geography, geodesy, climatology, seismology, meteorology, and environmental science. Moreover, this review paper highlights the significance of SAR in various aspects of geosciences, summarizes recent advancements in SAR technology, and demonstrates unique insights and important contributions of SAR in understanding and solving geophysical questions. Future directions and outlooks include integrating SAR with other geophysical data and interdisciplinary applications for complex questions. This review serves as an up‐to‐date guide to the cutting‐edge uses of SAR technology in comprehensive geophysical studies. It is aimed at researchers and practitioners in geosciences, as well as policymakers and stakeholders interested in leveraging SAR for geosciences. Key Points: Synthetic Aperture Radar (SAR) for geosciences is comprehensively reviewed broadly including oceanography, geography, hazards, and climate changeScientific advances contributed by SAR techniques for each topic are overviewed in‐depth with recent developments and frontiers highlightedData, techniques, and scientific insights of SAR are summarized and prospected, highlighting the role of machine learning [ABSTRACT FROM AUTHOR]

Insufficient spatiotemporal coverage of partial pressure of CO2 (p CO2) observations has hindered precise studies of the coastal carbon cycle along the North American Atlantic Coastal Ocean Margin (NAACOM). Earlier p CO2-products have encountered difficulties in accurately capturing the heterogeneity of regional variations and decadal trends of p CO2 in the NAACOM. This study developed a regional reconstructed p CO2-product for the NAACOM (Reconstructed Coastal Acidification Database- p CO2, or ReCAD-NAACOM- p CO2) using a two-step approach combining random forest regression and linear regression. The product provides monthly p CO2 data at 0.25° spatial resolution from 1993 to 2021, enabling investigation of regional spatial differences, seasonal cycles, and decadal changes in p CO2. The observation-based reconstruction was trained using Surface Ocean CO2 Atlas (SOCAT) observations as ground-truth values, with various satellite-derived and reanalysis environmental variables known to control sea surface p CO2 as model inputs. The product shows high accuracy during the model training, validation, and independent test phases, demonstrating robustness and capability to accurately reconstruct p CO2 in regions or periods lacking direct observational data in the NAACOM. Compared with all the observation samples from SOCAT, the p CO2-product yields a determination coefficient of 0.83, a root-mean-square error of 18.64 µatm, and an accumulative uncertainty of 23.83 µatm. The ReCAD-NAACOM- p CO2 product demonstrates its capability to resolve seasonal cycles, regional-scale variations, and decadal linear trends of p CO2 along the NAACOM. This new product provides reliable p CO2 data for more precise studies of coastal carbon dynamics in the NAACOM region. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11500974 (Wu et al., 2024a) and will be updated regularly. [ABSTRACT FROM AUTHOR]

The Central Jilin-Yanbian metallogenic belt, one of the 12 important nickel mineralization belts in China, containing 13 nickel deposits(occurrences), with nickel reserves of more than 280 000 tons, is an important nickel resource base in China. There are intense multistage tectonomagmatic activities in the metallogenic belt, especially a series of ultramafic-mafic rock groups and related copper-nickel sulfide deposits were formed in the Indosinian post-orogenic extensional background. Through study on the two typical nickel deposits in Hongqiling and Changren, the paper sorts out the geological and metallogenic characteristics of nickel deposits, summarizes the spatiotemporal distribution regularity, and extracts the prospecting indicators. Based on the regional geological characteristics and gravity, aeromagnetic anomalies, the prospecting potential of copper-nickel sulfide deposits in Central Jilin-Yanbian metallogenic belt is analyzed, and the prospecting direction of nickel deposits is discussed to provide scientific basis for the further nickel prospecting deployment in the metallogenic belt. [ABSTRACT FROM AUTHOR]