Crystallographic defects in noble metal nanocrystals are recognized as highly active catalytic sites, significantly enhancing activities in many important reactions. Despite their importance, synthesizing noble metal ...
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Crystallographic defects in noble metal nanocrystals are recognized as highly active catalytic sites, significantly enhancing activities in many important reactions. Despite their importance, synthesizing noble metal nanocrystals with a high density of defects poses a considerable synthetic challenge. Here, we present a novel lattice mismatch-induced formation mechanism to create high-density defects in noble metal nanocrystals. This approach takes advantage of lattice mismatch to enable non-epitaxial nucleation and growth of a noble metal on a foreign metal substrate, forming abundant noble metal crystallites with random lattice orientations not dictated by the substrate lattice. As these crystallites grow extensively, they merge, forming numerous grain boundaries and yielding defect-rich noble metal nanocrystals. Defect-rich alloy nanocrystals can also be synthesized through a subsequent vacancy-diffusion alloying process. We take defective PdCu alloy nanocages as an example and demonstrate the effectiveness of crystallographic defects in enhancing catalytic performance of noble metal nanocrystals. The nanocages exhibit superior activity in the electrocatalytic formic acid oxidation reaction, which is 1.6 times greater than their defect-free counterparts. Our strategy offers a new avenue for creating defect-rich noble metal nanocrystals as highly efficient catalysts for a wide array of catalytic applications.
Crystallographic defects in noble metal nanocrystals are recognized as highly active catalytic sites, significantly enhancing activities in many important reactions. Despite their importance, synthesizing noble metal ...
Crystallographic defects in noble metal nanocrystals are recognized as highly active catalytic sites, significantly enhancing activities in many important reactions. Despite their importance, synthesizing noble metal nanocrystals with a high density of defects poses a considerable synthetic challenge. Here, we present a novel lattice mismatch-induced formation mechanism to create high-density defects in noble metal nanocrystals. This approach takes advantage of lattice mismatch to enable nonepitaxial nucleation and growth of a noble metal on a foreign metal substrate, forming abundant noble metal crystallites with random lattice orientations not dictated by the substrate lattice. As these crystallites grow extensively, they merge, forming numerous grain boundaries and yielding defect-rich noble metal nanocrystals. Defect-rich alloy nanocrystals can also be synthesized through a subsequent vacancy-diffusion alloying process. We take defective Pd Cu alloy nanocages as an example and demonstrate the effectiveness of crystallographic defects in enhancing catalytic performance of noble metal *** nanocages exhibit superior activity in the electrocatalytic formic acid oxidation reaction, which is 1.6 times greater than their defect-free counterparts. Our strategy offers a new avenue for creating defect-rich noble metal nanocrystals as highly efficient catalysts for a wide array of catalytic applications.
*** Noble metal nanocrystals serve as exceptional catalysts across a wide spectrum of industrial processes[1].Moreover,there exists a substantial demand for noble metal catalysts in emerging clean-energy applications,...
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*** Noble metal nanocrystals serve as exceptional catalysts across a wide spectrum of industrial processes[1].Moreover,there exists a substantial demand for noble metal catalysts in emerging clean-energy applications,such as water-splitting hydrogen production and fuel *** non-noble transition metals or main-group metals into noble metal nanocrystals can reduce the weight of noble metals in the catalysts,thus remarkably lowering the ***,alloy nanocrystals offer intriguing catalytic properties that surpass those of their monometallic counterparts,thanks to the charge transfer between the constituent components and the expansion or shrinkage of the lattice size[2].
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...
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Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate *** the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant ***,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful *** this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization *** this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability *** models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven ***,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific *** addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model *** predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
Furniture is identified as a vital volatile organic compound(VOC)emission source in the indoor *** has become the most common raw and auxiliary fabric material for upholstered furniture,particularly with extensive con...
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Furniture is identified as a vital volatile organic compound(VOC)emission source in the indoor *** has become the most common raw and auxiliary fabric material for upholstered furniture,particularly with extensive consumption in sofas,due to its abundant resources and efficient *** being widely traded across the world,little research has been conducted on the VOCs released by leathermaterials and their health risk assessment in the indoor ***,this study investigated the VOC emissions of leather with different grades and the health risk of the inhalation *** on the ultra-fast gas phase electronic nose(EN)and GC-FID/Qtof,the substantial emissions of aliphatic aldehyde ketones(Aks),particularly hexanal,appear to be the cause of off-flavor in medium and low grade(MG and LG)sofa *** health risk assessment indicated that leather materials barely pose non-carcinogenic and carcinogenic effects to *** the abundance of VOC sources and the accumulation of health risks in the indoor environment,more stringent specifications concerning qualitative and quantitative content should be extended to provide VOC treatment basic for the manufacturing industry and obtain better indoor air quality.
Turbulent agglomeration is viewed as a promising technology for enhancing fine particle removal *** better understand particle transport,agglomeration behaviors,and fluid-particle interactions,we numerically explored ...
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Turbulent agglomeration is viewed as a promising technology for enhancing fine particle removal *** better understand particle transport,agglomeration behaviors,and fluid-particle interactions,we numerically explored these phenomena under cylindrical vortex wake influence using a coupled large eddy simulation and discrete element method(LES-DEM)*** validity of the LES approach was verified by comparison with available direct numerical simulation(DNS)*** adopted the Johnson-Kendall-Roberts(JKR)contact model for particle-particle *** particle dispersion and agglomeration characteristics of particles with different diameters(d_(p)=2-20μm)in the laminar and transition of shear layer(TrSL)flow regimes were *** particles were concentrated at the vortex centers,while larger particles accumulated around the *** agglomeration efficiency exhibited an M-shaped profile spanwise(y-direction).With increasing Reynolds number,the agglomeration efficiency and turbulence intensity *** particle agglomeration efficiency peaks at a certain Reynolds ***,at higher Reynolds numbers,reducing the residence time of particles in the flow field decreases the agglomeration efficiency.
A 3D simulation using Computational Particle Fluid Dynamics(CPFD)methods was used to calculate coal combustion in a 75 t/h industrial-scale circulating fluidized bed(CFB)*** characteristics,gas-solid flow characterist...
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A 3D simulation using Computational Particle Fluid Dynamics(CPFD)methods was used to calculate coal combustion in a 75 t/h industrial-scale circulating fluidized bed(CFB)*** characteristics,gas-solid flow characteristics,and gaseous pollutant emissions of CFB boilers from combustion ignition to stable operation were systematically evaluated in this *** show that the temperature distribution is relatively uniform throughout the *** the combustion process unfolds within the boiler,the gas composition curve strikingly portrays the inverse correlation between CO_(2)and O_(2)*** the combustion reaction progresses,it becomes evident that the concentration of CO_(2)progressively increases,while the concentration of O_(2)concurrently *** inverse relationship underscores the fundamental combustion reaction,where carbon-based fuels react with oxygen to produce carbon dioxide and release ***,a comprehensive analysis has revealed that,from ignition to stable combustion,both nitric oxide(NO)and sulfur dioxide(SO_(2))emissions exhibit a declining *** reduction in pollutant generation is attributed to the improvement in combustion *** complete combustion leads to lower levels of unburned hydrocarbons,which are prone to NO ***,the sulfur content in the fuel is more efficiently oxidized to sulfur trioxide(SO_(3))or bound in sulfates,reducing SO_(2)*** steady state in the simulation,the SO_(2)mass flow rate varies significantly with the furnace height,gradually increasing from 0.07 kg·s^(-1)at 4 m at the bottom of the furnace to a peak of 0.078 kg·s^(-1)at 8 m in the center,and then decreasing to 0.06 kg·s^(-1)at 20m at the top of the furnace.
All-solid-state batteries(ASSBs) assembled with sulfide solid electrolytes(SSEs) and nickel(Ni)-rich oxide cathode materials are expected to achieve high energy density and safety,representing potential candidates for...
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All-solid-state batteries(ASSBs) assembled with sulfide solid electrolytes(SSEs) and nickel(Ni)-rich oxide cathode materials are expected to achieve high energy density and safety,representing potential candidates for the next-generation energy storage ***,interfacial issues between SSEs and Nirich oxide cathode materials,attributed to space charge layer,interfacial side reactions,and mechanical contact failure,significantly restrict the performances of *** interface degradation is closely related to the components of the composite cathode and the process of electrode *** on the influencing factors of interface compatibility between SSEs and Ni-rich oxide cathode,this article systematically discusses how cathode active materials(CAMs),electrolytes,conductive additives,binders,and electrode fabrication impact the interface *** addition,the strategies for the compatibility modification are ***,the challenges and prospects of intensive research on the degradation and modification of the SSE/Ni-rich cathode material interface are *** review is intended to inspire the development of high-energy-density and high-safety all-solid-state batteries.
In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather *** data-driven transformer models such as Pangu-Weather,FengWu,a...
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In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather *** data-driven transformer models such as Pangu-Weather,FengWu,and FuXi have emerged as promising alternatives for numerical weather prediction in weather ***,these models have been characterized by their substantial computational resource consumption during training and limited incorporation of explicit physical guidance in their modeling *** contrast,TianXing applies a linear complexity mechanism that ensures proportional scalability with input data size while significantly diminishing GPU resource demands,with only a marginal compromise in ***,TianXing proposes an explicit attention decay mechanism in the linear attention derived from physical insights to enhance its forecasting *** mechanism can reweight attention based on Earth's spherical distances and learned sparse multivariate coupling relationships,promptingTianXing to prioritize dynamically relevant neighboring ***,to enhance its performance in mediumrange forecasting,TianXing employs a stacked autoregressive forecast *** of the model's architecture is conducted using ERA5 reanalysis data at a 5.625°latitude-longitude resolution,while a high-resolution dataset at 0.25°is utilized for training the actual forecasting ***,the TianXing exhibits excellent performance,particularly in the Z500(geopotential height)and T850(temperature)fields,surpassing previous data-driven models and operational fullresolution models such as NCEP GFS and ECMWF IFS,as evidenced by latitude-weighted RMSE and ACC ***,the TianXing has demonstrated remarkable capabilities in predicting extreme weather events,such as typhoons.
Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** dee...
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Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.
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