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Abstract While the role of crystal facets is well known in traditional heterogeneous catalysis, this effect has not yet been thoroughly studied in plasmon-assisted catalysis, where attention has primarily focused on plasmon-derived mechanisms. Here, we investigate plasmon-assisted electrocatalytic CO2 reduction using different shapes of plasmonic Au nanoparticles - nanocube (NC), rhombic dodecahedron (RD), and octahedron (OC) - exposing {100}, {110}, and {111} facets, respectively. Upon plasmon excitation, Au OCs doubled CO Faradaic efficiency (FECO) and tripled CO partial current density (jCO) compared to a dark condition, with NCs also improving under illumination. In contrast, Au RDs maintained consistent performance irrespective of light exposure, suggesting minimal influence of light on the reaction. Temperature experiments ruled out heat as the main factor to explain such differences. Atomistic simulations and electromagnetic modeling revealed higher hot carrier abundance and electric field enhancement on Au OCs and NCs than RDs. These effects now dominate the reaction landscape over the crystal facets, thus shifting the reaction sites when comparing dark and plasmon-activated processes. Plasmon-assisted H2 evolution reaction experiments also support these findings. The dominance of low-coordinated sites over facets in plasmonic catalysis suggests key insights for designing efficient photocatalysts for energy conversion and carbon neutralization.

Histone methylation plays crucial roles in regulating chromatin structure and gene transcription in epigenetic modifications. Lysine-specific demethylase 1 (LSD1), the first identified histone demethylase, is universally overexpressed in various diseases. LSD1 dysregulation is closely associated with cancer, viral infections, and neurodegenerative diseases, etc., making it a promising therapeutic target. Several LSD1 inhibitors and two small-molecule degraders (UM171 and BEA-17) have entered the clinical stage. LSD1 can remove methyl groups from histone 3 at lysine 4 or lysine 9 (H3K4 or H3K9), resulting in either transcription repression or activation. While the roles of LSD1 in transcriptional regulation are well-established, studies have revealed that LSD1 can also be dynamically regulated by other factors. For example, the expression or activity of LSD1 can be regulated by many proteins that form transcriptional corepressor complexes with LSD1. Moreover, some post-transcriptional modifications and cellular metabolites can also regulate LSD1 expression or its demethylase activity. Therefore, in this review, we will systematically summarize how proteins involved in the transcriptional corepressor complex, various post-translational modifications, and metabolites act as regulatory factors for LSD1 activity.

Abstract Nitrate (NO3 ‒) pollution poses significant threats to water quality and global nitrogen cycles. Alkaline electrocatalytic NO3 ‒ reduction reaction (NO3RR) emerges as an attractive route for enabling NO3 ‒ removal and sustainable ammonia (NH3) synthesis. However, it suffers from insufficient proton (H+) supply in high pH conditions, restricting NO3 ‒-to-NH3 activity. Herein, we propose a halogen-mediated H+ feeding strategy to enhance the alkaline NO3RR performance. Our platform achieves near-100% NH3 Faradaic efficiency (pH = 14) with a current density of 2 A cm–2 and enables an over 99% NO3 –-to-NH3 conversion efficiency. We also convert NO3 ‒ to high-purity NH4Cl with near-unity efficiency, suggesting a practical approach to valorizing pollutants into valuable ammonia products. Theoretical simulations and in situ experiments reveal that Cl-coordination endows a shifted d-band center of Pd atoms to construct local H+-abundant environments, through arousing dangling O-H water dissociation and fast *H desorption, for *NO intermediate hydrogenation and finally effective NO3 ‒-to-NH3 conversion.

The strength and plasticity trade-off in metal materials can be effectively addressed by Eutectic High-Entropy Alloys (EHEAs) through the realization of heterogeneous biphase layered structures at both micrometer and nanometer scales. In this work, a series of (CoCrFeNi)88NbxW(12-x) EHEAs were prepared, and the effects of the synergistic of Nb and W elements on the microstructure and mechanical properties of CoCrFeNi High-Entropy Alloys (HEAs) were investigated. With the increase of Nb content and the decrease of W content, the microstructure of the alloy underwent three changes, from Face Center Cubic (FCC) + μ biphase to FCC + μ + Laves triple phase, and finally to FCC + Laves biphase. The strength of the alloy increases, but the compression rate decreases. Among these alloys, (CoCrFeNi)88Nb10W2 alloy exhibits exceptional mechanical properties. The synergistic effect of precipitation strengthening and grain boundary strengthening makes the yield strength, fracture strength, and plastic strain of the alloy are 1568 MPa, 2516 MPa, and 32 % respectively. In addition, in the CoCrFeNi HEAs, the weighted average atomic radius ratio (rm‾/rQ) is used to further precise the phase selection law of Laves phase and μ phase. When rm‾/rQ < 1.112, HEAs forms the μ phase, when 1.112 ≤ rm‾/rQ ≤ 1.14, HEAs forms the Laves + μ biphase, when rm‾/rQ > 1.14, HEAs forms the Laves phase. The results can provide a basis for synergistic strengthening and phase selection of CoCrFeNi HEAs.

Purpose: To inveatigate how effective LMWH was at preventing venous thromboembolism (VTE), major bleeding events, and minor bleeding events after simple knee arthroscopic surgery and anterior cruciate ligament reconstruction (ACLR). Methods: We conducted a comprehensive search of PubMed, EMBASE, Cochrane Library, and the CNKI database for potentially eligible articles. The outcomes were evaluated in terms of odds ratio (OR) and the associated 95% confidence intervals (CIs). Meta-analysis was performed using the Stata software and subgroup analyses were performed based on the surgical setting including ACLR and simple knee arthroscopic surgery. Results: A total of eight studies with 2249 patients and 1794 controls were included in this meta-analysis. In patients undergoing simple knee arthroscopic surgery, LMWH prophylaxis did not bring a significant reduction in the risk of symptomatic deep venous thrombosis (DVT), symptomatic pulmonary embolism (PE), symptomatic VTE, and did not increase the risk of major bleeding events, but did have a higher risk of minor bleeding events (OR = 1.95, 95% CI 1.34–2.84, P = 0.000) and a lower risk of asymptomatic DVT (OR = 0.14, 95% CI 0.04–0.53, P = 0.004) in comparison with non-LMWH prophylaxis. In patients undergoing ACLR, LMWH prophylaxis did not bring a significant reduction in the risk of symptomatic DVT, symptomatic PE, symptomatic VTE, and did not increase the risk of major bleeding events and minor bleeding events, but did have a lower risk of asymptomatic DVT (OR = 0.43, 95% CI 0.23–0.78, P = 0.006). Conclusion: When compared to a control group, this meta-analysis found that LMWH had little potential benefit in preventing major VTE (symptomatic VTE, symptomatic DVT, and symptomatic PE) after simple knee arthroscopy and ACLR. As a result, LMWH should not be considered routinely in patients undergoing knee arthroscopic surgery.

Microstructural evolution and mechanical properties of H13 steel fabricated by selective laser melting (SLM) with subsequent tempering treatment were systematically examined. It was found that the microstructure of the as-SLMed H13 samples consisted of cellular structures, lath martensite and high-volume fraction of retained austenite. After tempering at 600 °C for 1 h, the nanoscale Cr23C6 particles were detected at the boundaries of the partially dissolved cellular structures. The fine grains, the retained cellular structures, and the formation of Cr23C6 carbides significantly improved the mechanical properties of the H13 steel. A superior mechanical properties, including the yield strength (YS) of 1647 ± 29 MPa, ultimate tensile strength (UTS) of 2013 ± 35 MPa and elongation (El) of 4.1 ± 0.3% have been achieved in the SLMed H13 steel after tempering at 600 °C for 1 h. With the increase of tempering temperature to 700 °C, the cellular structures were completely dissolved and the high number density of coarse Cr23C6 carbides were formed, which led to the decrease of UTS at 1083 ± 21 MPa, while the elongation was significantly improved to 12.3 ± 1.2% due to the recovery of dislocation density and the decomposition of martensite in the H13 steel.

Electroreduction of CO2 on single atom catalysts is often hindered by electron delocalization of the metal sites. To improve CO2 activation, here the authors functionalize the carbon support with cyano moieties, thereby attenuating metal-substrate conjugation and improving CO2 to CO conversion.

Environmental degradation and climate change caused by excessive CO2 emissions have become the most serious challenges facing humanity. The electrocatalytic CO2 reduction reaction (CO2RR) is an ideal way to produce high‐value chemicals and solve environmental pollution at the same time. The electric double layer (EDL) structure at the electrochemical interface can greatly influence the local catalytic environment, but it is not fully understood. Herein, recent advances in EDL structure for CO2RR are focused. The main components of the EDL, including adsorbates on the electrode surface, metal/water interface, cations, anions, and differential capacitance are described. The latest experimental and theoretical understanding of EDL and their impact on CO2RR are presented. Finally, the current challenges encountered and the outlook for future development are discussed. This review describes the influence of EDL structure on CO2 reduction, which is of guiding significance for the design of new and efficient CO2 electrocatalysts.

It is of high importance to understand the origin of single-atom Fe-N4 activity in oxygen reduction reaction. Here, the authors provide a model to understand the catalytic activity of Fe-N4 site from the spatial structure and energy level of the frontier orbitals by density functional theory calculations.

In the present work, a general and simple approach to determine texture patterns using pole figure was developed. For the rolled materials, the rolling plane can be determined by the ratio of OP/R in the given pole figure. Here, OP is the length from the center to an arbitrary pole in the pole figure, and R is the radius of the projection circle in the pole figure. Rolling direction for the rolled materials or the extrusion direction for the extruded materials can be directly calculated using the coordinate values of the poles in the given pole figure. For cubic and hexagonal materials, the calculated results using the developed approach are in agreement with the standard stereographic projection pole figures. The textures in the rolled and annealed Fe–17Cr ferritic stainless steel and extruded Mg–2Mn alloy were examined by electron backscatter diffraction and X-ray diffraction. The proposed approach was applied to determine texture patterns in the above two materials. It was found that {111} type of γ-fiber texture develops for the as-annealed Fe–17Cr ferritic stainless steel and + double fiber texture develops for the extruded Mg–2Mn alloy.

Converting carbon dioxide (CO2) into value-added chemicals by CO2 reduction has been considered as a potential way to solve the current energy crisis and environmental problem. Among the methods of CO2 reduction, the electrochemical method has been widely used due to its mild reaction condition and high reaction efficiency. In the electrochemical reduction system, the CO2 electrocatalyst is the most important part. Although many CO2 electrocatalysts have been developed, efficient catalysts with high activity, selectivity and stability are still lacking. Copper sulfide compound, as a low-toxicity and emerging material, has broad prospects in the field of CO2 reduction due to its unique structural and electrochemical properties. Much progress has been achieved with copper sulfide nanocrystalline and the field is rapidly developing. This paper summarizes the preparation, recent progress in development, and factors affecting the electrocatalytic CO2 reduction performance with copper sulfide compound as a catalyst. Prospects for future development are also outlined, with the aim of using copper sulfide compound as a highly active and stable electrocatalyst for CO2 reduction.

Iron phthalocyanine with a 2D structure and symmetric electron distribution around Fe-N4 active sites is not optimal for O2 adsorption and activation. Here, the authors report an axial Fe–O coordination induced electronic localization strategy to enhance oxygen reduction reaction performance.

Monocular depth estimation is a foundation task of three-dimensional (3D) reconstruction which is used to improve the accuracy of environment perception. Because of the simpler hardware requirement, it is more suitable than other multi-view methods. In this study, a new monocular depth estimation algorithm based on graph convolution network (GCN) is proposed. The pixel-wise depth relationship is introduced into conventional convolution neural network (CNN) to make up the disadvantage of processing non-Euclidian data. And the remaining depth topological graph information on the spatial latent variables are extracted based on a multi-scale reconstruction strategy. The final results on NYU-v2 depth dataset and KITTI depth dataset demonstrate that our algorithm improves the quality of monocular depth estimation, especially there are several little objects coexisting in the scenes.

Wind power ramp events (WPREs) are a common phenomenon in wind power generation. This unavoidable phenomenon poses a great harm to the balance of active power and the stability of frequency in the power supply system, which seriously threatens the safety, stability, and economic operation of the power grid. In order to deal with the impact of ramp events, accurate and rapid detection of ramp events is of great significance for the formulation of response measures. However, some attribute information is ignored in previous studies, and the laws and characteristics of ramp events are difficult to present intuitively. In this paper, we propose a visualization-based ramp event detection model for wind power generation. Firstly, a ramp event detection model is designed considering the multidimensional attributes of ramp events. Then, an uncertainty analysis scheme of ramp events based on the confidence is proposed, enabling users to analyze and judge the detection results of ramp events from different dimensions. In addition, an interactive optimization model is designed, supporting users to update samples interactively, to realize iterative optimization of the detection model. Finally, a set of visual designs and user-friendly interactions are implemented, enabling users to explore WPREs, judge the identification results, and interactively optimize the model. Case studies and expert interviews based on real-world datasets further demonstrate the effectiveness of our system in the WPREs identification, the exploration of the accuracy of identification results, and interactive optimization.

Malolactic fermentation (MLF) is the decarboxylation of L-malic acid to L-lactic acid by lactic acid bacteria (LAB). For the majority of wine production, secondary fermentation is crucial. MLF significantly impacts the quality of most red and some white wine. The outcomes of the spontaneously initiated and finished MLF are frequently unpredictable and can even cause the wine to deteriorate. As a result, individuals typically favour inoculating superior starter cultures when performing MLF. The MLF method for wine has, however, faced new difficulties because of the altered wine fermentation substrate environment brought on by global climate change, the growing demands of winemakers for production efficiency, and the rising demand for high-quality wine. To serve as a reference for the study of wine production and MLF in the current situation, this review primarily updates and summarises the research findings on increasing the effectiveness and dependability of MLF in recent years.

Excessive consumption of fossil fuels gives rise to the increasing emission of carbon dioxide (CO2) in the atmosphere and furthers the ecocrisis. Electrochemical CO2 reduction (ECR) has both functions of dwindling greenhouse gas concentration and converting it into valuable products. Due to the intrinsic chemical inertness of CO2 molecules, the study on efficient and low‐cost catalysts has attracted much attention. Recently isolated atoms, dispersed in stable support, play an important role in decreasing energy barriers of intermediate steps and obtaining target products with high activity and selectivity for ECR. The effective regulation of central atoms or coordination environment is significant to realize the desired performances of ECR with a high efficiency and selectivity. Hence, a comprehensive summary about strategies for improving the performance of ECR on single atom catalysts (SACs) is necessary. Herein, the SACs on various supports are introduced, the methods to design stable SACs are discussed, and the strategies for tuning the performance of ECR on SACs are summarized. The localized environment manipulation is widely used for high‐performance SACs design, including regulating central atoms and coordination environment. Finally, the perspectives are discussed to shed light on the rational design of intriguing SACs for ECR.

H13 stool steel processed by selective laser melting (SLM) suffered from severe brittleness and scatter distribution of mechanical properties. We optimized the mechanical response of as-SLMed H13 by tailoring the optimisation of process parameters and established the correlation between microstructure and mechanical properties in this work. Microstructures were examined using XRD, SEM, EBSD and TEM. The results showed that the microstructures were predominantly featured by cellular structures and columnar grains, which consisted of lath martensite and retained austenite with numerous nanoscale carbides being distributed at and within sub-grain boundaries. The average size of cellular structure was ~500 nm and Cr and Mo element were enriched toward the cell wall of each cellular structure. The as-SLMed H13 offered the yield strength (YS) of 1468 MPa, the ultimate tensile strength (UTS) of 1837 MPa and the fracture strain of 8.48%. The excellent strength-ductility synergy can be attributed to the refined hierarchical microstructures with fine grains, the unique cellular structures and the presence of dislocations. In addition, the enrichment of solute elements along cellular walls and carbides at sub-grain boundaries improve the grain boundary strengthening.

A multiple-quantum-well diode (MQW-diode) can function as a light-emitting diode or a photodiode. In particular, when appropriately biased, this dual-functioning device can simultaneously emit light and process information. Here, an MQW-diode of ring geometry is fabricated on a 1.64-mm-diameter suspended membrane by a combination of silicon separation and III-nitride backside thinning, and the 1.47-mm-diameter inner region enhances the overall light detection. When the MQW-diode operates under the simultaneous emission-detection mode, a three-stage amplifier circuit with a signal gain of 27.8 dB is used to recover the received signals, and the real-time spatial audio acquisition is experimentally demonstrated with the wire-bonded MQW-diode. This paper provides a promising path to produce multifunctional MQW-diodes toward the Internet of Things.

New mitochondrial genomes of Pseudogobio guilinensis, P. giganteus, and P. anderssoni have the length of 16,605, 16,606, and 16,609 bp with A + T bias. Inferred phylogeny shows that P. guilinensis occupies basal position. P. esocinus and P. anderssoni+P. longirostris are sister groups, and they together are a sister taxa of P. giganteus.

Abstract The photoreduction of CO2 to hydrocarbon products has attracted much attention because it provides an avenue to directly synthesize value‐added carbon‐based fuels and feedstocks using solar energy. Among various photocatalysts, graphitic carbon nitride (g‐C3N4) has emerged as an attractive metal‐free visible‐light photocatalyst due to its advantages of earth‐abundance, nontoxicity, and stability. Unfortunately, its photocatalytic efficiency is seriously limited by charge carriers′ ready recombination and their low reaction dynamics. Modifying the local electronic structure of g‐C3N4 is predicted to be an efficient way to improve the charge transfer and reaction efficiency. Here, boron (B) is doped into the large cavity between adjacent tri‐s‐triazine units via coordination with two‐coordinated N atoms. Theoretical calculations prove that the new electron excitation from N (2px, 2py) to B (2px, 2py) with the same orbital direction in B‐doped g‐C3N4 is much easier than N (2px, 2py) to C 2pz in pure g‐C3N4, and improves the charge transfer and localization, and thus the reaction dynamics. Moreover, B atoms doping changes the adsorption of CO (intermediate), and can act as active sites for CH4 production. As a result, the optimal sample of 1%B/g‐C3N4 exhibits better selectivity for CH4 with ≈32 times higher yield than that of pure g‐C3N4.

A binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm based on generative adversarial networks (GAN) is proposed to enhance the robustness of binocular vision systems. The proposed model consists of two parts: generative network and discriminator network. To improve the quality of a virtual view, a generative network structure based on 3D convolutional neural networks (3D-CNN) and attentive mechanisms is introduced to extract the time-series features from image sequences. To avoid gradient vanish during training, the dense block structure is utilized to improve the discriminator network. Meanwhile, three kinds of image features, including image edge, depth map and optical flow are extracted to constrain the supervised training of model. The final results on KITTI and Cityscapes datasets demonstrate that our algorithm outperforms conventional methods, and the missing vision signal can be replaced by a generated virtual view.

Recurrent neural networks (RNN) are efficient in modeling sequences for generation and classification, but their training is obstructed by the vanishing and exploding gradient issues. In this paper, we reformulate the RNN unit to learn the residual functions with reference to the hidden state instead of conventional gated mechanisms such as long short-term memory (LSTM) and the gated recurrent unit (GRU). The residual structure has two main highlights: firstly, it solves the gradient vanishing and exploding issues for large time-distributed scales; secondly, the residual structure promotes the optimizations for backward updates. In the experiments, we apply language modeling, emotion classification and polyphonic modeling to evaluate our layer compared with LSTM and GRU layers. The results show that our layer gives state-of-the-art performance, outperforms LSTM and GRU layers in terms of speed, and supports an accuracy competitive with that of the other methods.

Tetrafluoromethane (CF4), the simplest perfluorocarbons, is a permanently potent greenhouse gas due to its powerful infrared radiation adsorption capacity. The highly symmetric and robust C-F bond structure makes its activation a great challenge. Herein, we presented an innovated approach that efficiently activates C-F bond utilizing protonated sulfate (-HSO4) modified Al2O3@ZrO2 (S-Al2O3@ZrO2) catalyst, resulting in highly efficient CF4 decomposition. By combining in situ infrared spectroscopy tests and density function theory simulations, we demonstrate that the introduced -HSO4 proton donor has a stronger interaction on the C-F bond than the hydroxyl (-OH) proton donor, which can effectively stretch the C-F bond for its activation. Consequently, the obtained S-Al2O3@ZrO2 catalyst achieved a stable 100% CF4 decomposition at a record low temperature of 580 °C with a turnover frequency value of ~8.3 times higher than the Al2O3@ZrO2 catalyst without -HSO4 modification, outperforming the previously reported results. This work paves a new way for achieving efficient C-F bond activation to decompose CF4 at a low temperature. [ABSTRACT FROM AUTHOR]