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Research topic detection in scientific articles using a hybrid BERT integrated telescopic vector tree model with emperor penguin enhanced NSGA II optimization
The northeast materials database for magnetic materials
Effect of intrapulpal cryoanesthesia on intraoperative pain during root canal treatment in mandibular molars: a double blinded randomized control trial
Lowering the Cu-O bond energy in CuO nanocatalysts enhances the efficiency of NH3 oxidation
Abstract Tuning the electronic properties of nanocatalysts via doping with monodispersed hetero-metal atoms is an effective method used to enhance catalytic properties. Doping CuO nanoparticles with monodispersed Co atoms using different reductants affords catalysts (Co B Cu/Al 2 O 3 and Co H Cu/Al 2 O 3 ) with strikingly different electronic structures. Compared to Co H Cu/Al 2 O 3 , the CuO nanoparticles in Co B Cu/Al 2 O 3 have longer and weaker Cu-O bonds, with a lower 1 s → 4 p z antibonding transition and higher 4 p → 1 s bonding transition (as demonstrated from HERFD-XANES and valence-to-core X-ray emission spectroscopy). The weaker Cu-O bonds in Co B Cu/Al 2 O 3 lead to superior redox activity of the CuO nanoparticles, evidenced from operando XAFS and in-situ near ambient pressure-near edge X-ray absorption fine structures studies. Such superior redox properties of CuO in Co B Cu/Al 2 O 3 result in a much reduced activation energy of Co B Cu/Al 2 O 3 compared to Co H Cu/Al 2 O 3 (40.0 vs. 63.5 kJ/mol), thus leading to an enhancement in catalytic performance in the selective catalytic oxidation of NH 3 to N 2 .
Cell type- and species-specific regulation of hepatic lncRNAs by TCDD-activated aryl hydrocarbon receptor
Photochemical rearrangement of isonitriles via energy transfer catalysis
Joint association of triglyceride-glucose index and obesity indicators with stroke risk: a nationwide prospective cohort study
Development of [18F]ACI-19626 as a first-in-class brain PET tracer for imaging TDP-43 pathology
AI-powered digital arbitration framework leveraging smart contracts and electronic evidence authentication
Charge redistribution dynamics in chalcogenide-stabilized cuprous electrocatalysts unleash ampere-scale partial current toward formate production
Abstract Electrochemical CO 2 reduction to formate offers a sustainable route, but achieving high selectivity on transition metal catalysts remains a significant challenge, which is typically favored on p -block metals. Here, we demonstrate that chalcogenide-stabilized cuprous enables near-complete formate selectivity through a charge redistribution mechanism induced by chalcogenides. Using in situ X-ray absorption spectroscopy, high-energy-resolution fluorescence-detected XAS, Raman, and infrared spectroscopy, we reveal that Cu-chalcogen interactions stabilize Cu + , preventing over-reduction to Cu 0 and thereby modulating CO 2 adsorption and intermediate binding. This stabilization enhances the *OCHO pathway, shifting product distribution entirely toward formate. CuS exhibits the highest selectivity, achieving a notable 90% faradaic efficiency at −0.6 V and an ampere-scale formate partial current of 1.36 A, demonstrating industrial feasibility. In contrast, CuO, lacking a charge redistribution effect, promotes a mixture of CO and C2 products, underscoring the critical role of chalcogenides in steering product selectivity. This work provides fundamental insights into charge redistribution in CO 2 RR and introduces a catalyst design strategy leveraging chalcogen-induced electronic modifications for scalable formate production.
Association between going out for work and self-rated health of rural residents: a longitudinal study in Ningxia, China
Real-time self-supervised denoising for high-speed fluorescence neural imaging
Abstract Self-supervised denoising methods significantly enhance the signal-to-noise ratio in fluorescence neural imaging, yet real-time solutions remain scarce in high-speed applications. Here, we present the FrAme-multiplexed SpatioTemporal learning strategy (FAST), a deep-learning framework designed for high-speed fluorescence neural imaging, including in vivo calcium, voltage, and volumetric time-lapse imaging. FAST balances spatial and temporal redundancy across neighboring pixels, preserving structural fidelity while preventing over-smoothing of rapidly evolving fluorescence signals. Utilizing an ultra-light convolutional neural network, FAST enables real-time processing at speeds exceeding 1000 frames per second, substantially surpassing the acquisition rates of most high-speed imaging systems. We also introduce an intuitive graphical user interface that integrates FAST into standard imaging workflows, providing a real-time denoising tool for recorded neural activity and enabling downstream analysis in neuroscience research that requires millisecond-scale temporal precision, particularly in closed-loop studies.