In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to ...In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.展开更多
In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrabilit...In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.展开更多
Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition wi...Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.展开更多
Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. Ho...Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.展开更多
Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the origin...Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
The new technology of direct decomposition of H_(2)S into high value-added H_(2) and S,as an alternative to the Claus process in industry,is an ideal route that can not only deal with toxic and abundant H_(2)S waste g...The new technology of direct decomposition of H_(2)S into high value-added H_(2) and S,as an alternative to the Claus process in industry,is an ideal route that can not only deal with toxic and abundant H_(2)S waste gas but also recover clean energy H_(2),which has significant socio-economic and ecological advantages.However,the highly effective decomposition of H_(2)S at low temperatures is still a great challenge,because of the stringent thermodynamic equilibrium constraints(only 20% even at high temperature of 1010℃).Conventional microwave catalysts exhibit unsatisfactory performance at low temperatures(below 600℃).Herein,Mo_(2)C@CeO_(2) catalysts with a core-shell structure were successfully developed for robust microwave catalytic decomposition of H_(2)S at low temperatures.Two carbon precursors,para-phenylenediamine(Mo_(2)C-p)and meta-phenylenediamine(Mo_(2)C-m),were employed to tailor Mo_(2)C configurations.Remarkably,the H_(2)S conversion of Mo_(2)C-p@CeO_(2) catalyst at a low temperature of 550℃ is as high as 92.1%,which is much higher than the H_(2)S equilibrium conversion under the conventional thermal conditions(2.6% at 550℃).To our knowledge,this represents the most active catalyst for microwave catalytic decomposition of H_(2)S at low temperature of 550℃.Notably,Mo_(2)C-p demonstrated superior intrinsic activity(84%)compared to Mo_(2)C-m(6.4%),with XPS analysis revealing that its enhanced performance stems from a higher concentration of Mo_(2+)active sites.This work presents a substitute approach for the efficient utilization of H_(2)S waste gas and opens up a novel avenue for the rational design of microwave catalysts for microwave catalytic reaction at low-temperature.展开更多
The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of...The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of α-MoC and β-Mo_(2)C catalysts with different Mo/C ratios were synthesized,and their ammonia decomposition performance as well as structural evolution in ADR was investigated.The β-Mo_(2)C catalyst,characterized by a higher Mo/C ratio,demonstrated a remarkable turnover frequency of 1.3 s^(-1),which is over tenfold higher than that ofα-MoC(0.1 s^(-1)).An increase in the Mo/C ratio of molybdenum carbide revealed a direct correlation between the surface Mo/C ratio and the hydrogen yield.The transient response surface reaction indicated that the combination of N*and N*derived from NH_(3) dissociation represents the rate-determining step in the ADR,andβ-Mo2C exhibited exceptional proficiency in facilitating this pivotal step.Concurrently,the accumulation of N*species on the carbide surface could induce the phase transition of molybdenum carbide to nitride,which follows a topological transformation.It is discovered that such phase evolution was affected by the Mo-C surface and reaction temperature simultaneously.When the kinetics of combination of N*was accelerated by rising temperatures and its accumulation on the carbide surface was mitigated,β-Mo_(2)C maintained its carbide phase,preventing nitridation during the ADR at 810℃.Our results contribute to an in-depth understanding of the molybdenum carbides’catalytic properties in ADR and highlight the nature of the carbide-nitride phase transition in the reaction.展开更多
This paper introduces dynamic mode decomposition(DMD)as a novel approach to model the breakage kinetics of particulate systems.DMD provides a data-driven framework to identify a best-fit linear dynamics model from a s...This paper introduces dynamic mode decomposition(DMD)as a novel approach to model the breakage kinetics of particulate systems.DMD provides a data-driven framework to identify a best-fit linear dynamics model from a sequence of system measurement snapshots,bypassing the nontrivial task of determining appropriate mathemat-ical forms for the breakage kernel functions.A key innovation of our method is the instilling of physics-informed constraints into the DMD eigenmodes and eigenvalues,ensuring they adhere to the physical structure of particle breakage processes even under sparse measurement data.The integration of eigen-constraints is computationally aided by a zeroth-order global optimizer for solving the nonlinear,nonconvex optimization problem that elicits system dynamics from data.Our method is evaluated against the state-of-the-art optimized DMD algorithm using both generated data and real-world data of a batch grinding mill,showcasing over an order of magnitude lower prediction errors in data reconstruction and forecasting.展开更多
The catalytic performance of supported metal catalysts is highly dependent on the interfacial contact between the metal centers and support materials,which could dynamically adapt to the chemical environment in the re...The catalytic performance of supported metal catalysts is highly dependent on the interfacial contact between the metal centers and support materials,which could dynamically adapt to the chemical environment in the reactions.Herein,the well-known Ru/TiO_(x)interface of the Ru/TiO_(2)catalyst is shown to be transformed into Ru/TiO_(x)N_(y)during the NH_(3)decomposition,which is derived from the nitridation of the support by N^(*)species.Through a series of characterizations and density functional theory(DFT)calculations,it is found that such a nitrogenous interface primarily blocks the cleavage of N-H bonds with a higher energy barrier,leading to the deactivation of Ru/TiO_(2)in NH_(3)decomposition.Nevertheless,the Ru/TiO_(x)interface can be easily restored by oxidation and a subsequent H2 reduction,contributing to the recovery of the catalytic activity toward NH_(3)decomposition.Our study provides a new insight into the deactivation mechanism of Ru/TiO_(2)in NH_(3)decomposition and highlights the significance of the dynamic evolution of the metal-support interfaces in the reactions.展开更多
The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection.However,the impact of residual antibiotics,a common contaminant of man...The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection.However,the impact of residual antibiotics,a common contaminant of manure,on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood.Here,we studied,how oxytetracycline(OTC)and ciprofloxacin(CIP)affect the decomposition,microbial community structure,extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments.Results showed that OTC and CIP greatly inhibited livestock manure decomposition,causing a decreased rate of carbon(28%-87%),nitrogen(15%-44%)and phosphorus(26%-43%)release.The relative abundance of gramnegative(G-)bacteria was reduced by 4.0%-13%while fungi increased by 7.0%-71%during a 28-day incubation period.Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions,particularly among G-bacteria,G+bacteria,and actinomycetes.These changes in microbial community structure and function resulted in decreased activity of urease,β-1,4-N-acetyl-glucosaminidase,alkaline protease,chitinase,and catalase,causing reduced decomposition and nutrient release in cattle and pig ma-nures.These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics,which will help facilitate sustainable agricultural production and soil carbon sequestration.展开更多
Monolithic catalysts with excellent O_(3)catalytic decomposition performance were prepared by in situ loading of Co-doped KMn_(8)O_(16)on the surface of nickel foam.The triple-layer structure with Co-doped KMn_(8)O_(1...Monolithic catalysts with excellent O_(3)catalytic decomposition performance were prepared by in situ loading of Co-doped KMn_(8)O_(16)on the surface of nickel foam.The triple-layer structure with Co-doped KMn_(8)O_(16)/Ni6MnO_(8)/Ni foam was grown spontaneously on the surface of nickel foam by tuning the molar ratio of KMnO_(4)to Co(NO_(3))_(2)·6H_(2)O precursors.Importantly,the formed Ni6MnO_(8)structure between KMn_(8)O_(16)and nickel foam during in situ synthesis process effectively protected nickel foam from further etching,which significantly enhanced the reaction stability of catalyst.The optimum amount of Co doping in KMn_(8)O_(16)was available when the molar ratio of Mn to Co species in the precursor solution was 2:1.And the Mn2Co1 catalyst had abundant oxygen vacancies and excellent hydrophobicity,thus creating outstanding O_(3)decomposition activity.The O_(3)conversion under dry conditions and relative humidity of 65%,90%over a period of 5 hr was 100%,94%and 80%with the space velocity of 28,000 hr^(−1),respectively.The in situ constructed Co-doped KMn_(8)O_(16)/Ni foam catalyst showed the advantages of low price and gradual applicability of the preparation process,which provided an opportunity for the design of monolithic catalyst for O_(3)catalytic decomposition.展开更多
In order to analyze the influences of storage aging on the safety of typical elemental explosives,the aged cyclotrimethylene trinitramine(RDX)and cyclotetramethylene tetranitramine(HMX)were prepared by isothermal agin...In order to analyze the influences of storage aging on the safety of typical elemental explosives,the aged cyclotrimethylene trinitramine(RDX)and cyclotetramethylene tetranitramine(HMX)were prepared by isothermal aging tests.The reaction thresholds of aged RDX and HMX under any ignition probability were studied by Langlie-Optimal D method.The thermal decomposition characteristics of RDX and HMX after aging were analyzed by DSC and ARC.Experimental results showed that compared with unaged RDX and HMX,on the one hand,the critical impact energy and critical friction of RDX and HMX aged for 14,28,and 56 days are significantly reduced at an explosion probability of 50%,0.01%,and 0.0001%,respectively.With the increase of aging time,the mechanical sensitivity of RDX and HMX increases obviously.On the other hand,the initial decomposition temperature of RDX and HMX after 56 days of aging decreases,the decomposition heat decreases,the activation energy increases,and the reaction difficulty increases.展开更多
Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult f...Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.展开更多
Alkaline earth-metal titanates ATiO_(3)(A=Ca,Sr,and Ba)with a perovskite-type structure were used as supports for Ru-based catalysts to produce CO_(x)-free H_(2)via NH_(3)decomposition.The effects of alkalineearth met...Alkaline earth-metal titanates ATiO_(3)(A=Ca,Sr,and Ba)with a perovskite-type structure were used as supports for Ru-based catalysts to produce CO_(x)-free H_(2)via NH_(3)decomposition.The effects of alkalineearth metals on the physicochemical characteristics and catalytic activities of Ru/ATiO_(3)for NH_(3)decomposition were investigated using various techniques.The order of Ru/ATiO_(3)for NH_(3)conversion is Ru/BaTiO_(3)>Ru/SrTiO_(3)>Ru/CaTiO_(3)>Ru/TiO_(2)at the identical conditions,with the Ru/BaTiO_(3)catalyst demonstrating the highest NH_(3)conversion of 77.8%at 450℃and a gas hourly space velocity of 30,000 mL/gcat/h,which is 8.7,2.1,and 1.3 times of that over Ru/TiO_(2),Ru/CaTiO_(3),and Ru/SrTiO_(3),respectively.The formation of the ATiO_(3)phase can enrich the concentration of basic sites and oxygen vacancies compared with TiO_(2),which can induce the presence of strong metal-support interaction(SMSI)through the formation of Ru-O-Ti bonds.This SMSI effect increased the dispersion and electron density of Ru nano-particles on ATiO_(3)supports,and the electron-rich Ru nano-particles could weaken the chemisorptive strength of N_(2)and H_(2)on the Ru/ATiO_(3)catalysts,thereby promoting the reaction rate for NH_(3)decomposition.展开更多
Cover cropping is a diversifying agricultural practice that can improve soil structure and function by altering the underground litter diversity and soil microbial communities.Here,we tested how a wheat cover crop alt...Cover cropping is a diversifying agricultural practice that can improve soil structure and function by altering the underground litter diversity and soil microbial communities.Here,we tested how a wheat cover crop alters the decomposition of cucumber root litter.A three-year greenhouse litterbag decomposition experiment showed that a wheat cover crop accelerates the decomposition of cucumber root litter.A microcosm litterbag experiment further showed that wheat litter and the soil microbial community could improve cucumber root litter decomposition.Moreover,the wheat cover crop altered the abundances and diversities of soil bacterial and fungal communities,and enriched several putative keystone operational taxonomic units(OTUs),such as Bacillus sp.OTU1837 and Mortierella sp.OTU1236,that were positively related to the mass loss of cucumber root litter.The representative bacterial and fungal strains B186 and M3 were isolated and cultured.In vitro decomposition tests demonstrated that both B186 and M3 had cucumber root litter decomposition activity and a stronger effect was found when they were co-incubated.Overall,a wheat cover crop accelerated cucumber root litter decomposition by altering the soil microbial communities,particularly by stimulating certain putative keystone taxa,which provides a theoretical basis for using cover crops to promote sustainable agricultural development.展开更多
The sustainability of methane catalytic decomposition is significantly enhanced by the production of high-quality value-added carbon products such as carbon nanotubes(CNTs).Understanding the production yields and prop...The sustainability of methane catalytic decomposition is significantly enhanced by the production of high-quality value-added carbon products such as carbon nanotubes(CNTs).Understanding the production yields and properties of CNTs is crucial for improving process feasibility and sustainability.This study employs machine learning technique to develop and analyze predictive models for the carbon yield and mean diameter of CNTs produced through methane catalytic decomposition.Utilizing comprehensive datasets from various experimental studies,the models incorporate variables related to catalyst composition,catalyst preparation,and operational parameters.Both models achieved high predictive accuracy,with R^(2)values exceeding 0.90.Notably,the reduction time during catalyst preparation was found to critically influence carbon yield,evidenced by a permutation importance value of 39.62%.Additionally,the use of Mo as a catalytic metal was observed to significantly reduce the diameter of produced CNTs.These findings highlight the need for future machine learning and simulation studies to include catalyst reduction parameters,thereby enhancing predictive accuracy and deepening process insights.This research provides strategic guidance for optimizing methane catalytic decomposition to produce enhanced CNTs,aligning with sustainability goals.展开更多
Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the phy...Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the physical array aperture,azimuth ambiguity occurs,making range-azimuth imaging on a moving platform challenging.To address this issue,we theoretically analyze azimuth ambiguity generation in sparse motion arrays and propose a dual-aperture adaptive processing(DAAP)method for suppressing azimuth ambiguity.This method combines spatial multiple-input multiple-output(MIMO)arrays with sparse motion arrays to achieve high-resolution range-azimuth imaging.In addition,an adaptive QR decomposition denoising method for sparse array signals based on iterative low-rank matrix approximation(LRMA)and regularized QR is proposed to preprocess sparse motion array signals.Simulations and experiments show that on a two-transmitter-four-receiver array,the signal-to-noise ratio(SNR)of the sparse motion array signal after noise suppression via adaptive QR decomposition can exceed 0 dB,and the azimuth ambiguity signal ratio(AASR)can be reduced to below-20 dB.展开更多
As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional t...As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional theory calculations to explore the structural phase transitions and electronic properties of calcium-sulfur compounds(Ca_(x)S_(1-x),where x=1/4,1/3,1/2,2/3,3/4,4/5)under 0-1200 GPa.The calculated formation enthalpies suggest that Ca_(x)S_(1-x)compounds undergo multiple phase transitions and eventually decompose into elemental Ca and S,challenging the traditional view that pressure stabilizes and densifies compounds.The analysis of formation enthalpy indicates that an increase in pressure leads to a rise in internal energy and the PV term,resulting in thermodynamic instability.Bader charge analysis reveals that this phenomenon is attributed to a decrease in charge transfer under high pressure.The activation of Ca-3d orbitals is significantly enhanced under pressure,leading to competition with Ca-4s orbitals and S-3p orbitals.This may cause the formation enthalpy minimum on the convex hull to shift sequentially from CaS to CaS_(3),then to Ca_(3)S and Ca_(2)S,and finally back to CaS.These findings provide critical insights into the behavior of alkaline-earth metal sulfides under high pressure,with implications for the synthesis and application of novel materials under extreme conditions and for understanding element distribution in planetary interiors.展开更多
Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale opti...Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale optimization problems are solved using computing machines,leading to an enormous computational time being required,which may delay deriving timely solutions.Decomposition methods,which partition a large-scale optimization problem into lower-dimensional subproblems,represent a key approach to addressing time-efficiency issues.There has been significant progress in both applied mathematics and emerging artificial intelligence approaches on this front.This work aims at providing an overview of the decomposition methods from both the mathematics and computer science points of view.We also remark on the state-of-the-art developments and recent applications of the decomposition methods,and discuss the future research and development perspectives.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52470114 and 52022104)the National Key R&D Program of China(No.2022YFC3702802)the Youth Innovation Promotion Association,CAS(No.Y2021020).
文摘In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.
基金sponsored by the National Natural Science Foundations of China under Grant Nos.12301315,12235007,11975131the Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ20A010009。
文摘In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.
基金supported by Ningbo Natural Science Foundation(No.2023J059)Ningbo Commonweal Programme Key Project(No.2023S038)Guangxi Key Research and Development Programme(No.GuikeAB21220063).
文摘Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.
基金supported by the National Natural Science Foundation of China (Nos. 22379121, 62005216)Basic Public Welfare Research Program of Zhejiang (No. LQ22F050013)+1 种基金Zhejiang Province Key Laboratory of Flexible Electronics Open Fund (2023FE005)Shenzhen Foundation Research Program (No. JCYJ20220530112812028)。
文摘Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.
基金supported in part by the Interdisciplinary Project of Dalian University(DLUXK-2023-ZD-001).
文摘Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the National Natural Science Foundation of China(22178295,21706225)Natural Science Foundation of Hunan Province(2025JJ50085)Hunan Collaborative Innovation Center of New Chemical Technologies for Environmental Benignity and Efficient Resource Utilization.
文摘The new technology of direct decomposition of H_(2)S into high value-added H_(2) and S,as an alternative to the Claus process in industry,is an ideal route that can not only deal with toxic and abundant H_(2)S waste gas but also recover clean energy H_(2),which has significant socio-economic and ecological advantages.However,the highly effective decomposition of H_(2)S at low temperatures is still a great challenge,because of the stringent thermodynamic equilibrium constraints(only 20% even at high temperature of 1010℃).Conventional microwave catalysts exhibit unsatisfactory performance at low temperatures(below 600℃).Herein,Mo_(2)C@CeO_(2) catalysts with a core-shell structure were successfully developed for robust microwave catalytic decomposition of H_(2)S at low temperatures.Two carbon precursors,para-phenylenediamine(Mo_(2)C-p)and meta-phenylenediamine(Mo_(2)C-m),were employed to tailor Mo_(2)C configurations.Remarkably,the H_(2)S conversion of Mo_(2)C-p@CeO_(2) catalyst at a low temperature of 550℃ is as high as 92.1%,which is much higher than the H_(2)S equilibrium conversion under the conventional thermal conditions(2.6% at 550℃).To our knowledge,this represents the most active catalyst for microwave catalytic decomposition of H_(2)S at low temperature of 550℃.Notably,Mo_(2)C-p demonstrated superior intrinsic activity(84%)compared to Mo_(2)C-m(6.4%),with XPS analysis revealing that its enhanced performance stems from a higher concentration of Mo_(2+)active sites.This work presents a substitute approach for the efficient utilization of H_(2)S waste gas and opens up a novel avenue for the rational design of microwave catalysts for microwave catalytic reaction at low-temperature.
文摘The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of α-MoC and β-Mo_(2)C catalysts with different Mo/C ratios were synthesized,and their ammonia decomposition performance as well as structural evolution in ADR was investigated.The β-Mo_(2)C catalyst,characterized by a higher Mo/C ratio,demonstrated a remarkable turnover frequency of 1.3 s^(-1),which is over tenfold higher than that ofα-MoC(0.1 s^(-1)).An increase in the Mo/C ratio of molybdenum carbide revealed a direct correlation between the surface Mo/C ratio and the hydrogen yield.The transient response surface reaction indicated that the combination of N*and N*derived from NH_(3) dissociation represents the rate-determining step in the ADR,andβ-Mo2C exhibited exceptional proficiency in facilitating this pivotal step.Concurrently,the accumulation of N*species on the carbide surface could induce the phase transition of molybdenum carbide to nitride,which follows a topological transformation.It is discovered that such phase evolution was affected by the Mo-C surface and reaction temperature simultaneously.When the kinetics of combination of N*was accelerated by rising temperatures and its accumulation on the carbide surface was mitigated,β-Mo_(2)C maintained its carbide phase,preventing nitridation during the ADR at 810℃.Our results contribute to an in-depth understanding of the molybdenum carbides’catalytic properties in ADR and highlight the nature of the carbide-nitride phase transition in the reaction.
基金supported by the Ramanujan Fellowship from the Science and Engineering Research Board,Government of India(Grant No.RJF/2022/000115).
文摘This paper introduces dynamic mode decomposition(DMD)as a novel approach to model the breakage kinetics of particulate systems.DMD provides a data-driven framework to identify a best-fit linear dynamics model from a sequence of system measurement snapshots,bypassing the nontrivial task of determining appropriate mathemat-ical forms for the breakage kernel functions.A key innovation of our method is the instilling of physics-informed constraints into the DMD eigenmodes and eigenvalues,ensuring they adhere to the physical structure of particle breakage processes even under sparse measurement data.The integration of eigen-constraints is computationally aided by a zeroth-order global optimizer for solving the nonlinear,nonconvex optimization problem that elicits system dynamics from data.Our method is evaluated against the state-of-the-art optimized DMD algorithm using both generated data and real-world data of a batch grinding mill,showcasing over an order of magnitude lower prediction errors in data reconstruction and forecasting.
基金supported by the National Key R&D Program of China(2022YFB4300700)National Natural Science Foundation of China(22008230,22208021,21925803)+6 种基金China Postdoctoral Science Foundation funded project(2020M670807)the Doctoral Scientific Research Foundation of Liaoning Province(2022-BS-014)the Innovation Research Fund Project of Dalian Institute of Chemical Physics(DICP I202224)CAS Specific Research Assistant Funding Programthe fund of the State Key Laboratory of Catalysis in Dalian Institute of Chemical Physics(N-22-08)the Youth Innovation Promotion Association CAS(Y2022061)the Young Topnotch Talents of Liaoning Province(XLYC2203108)。
文摘The catalytic performance of supported metal catalysts is highly dependent on the interfacial contact between the metal centers and support materials,which could dynamically adapt to the chemical environment in the reactions.Herein,the well-known Ru/TiO_(x)interface of the Ru/TiO_(2)catalyst is shown to be transformed into Ru/TiO_(x)N_(y)during the NH_(3)decomposition,which is derived from the nitridation of the support by N^(*)species.Through a series of characterizations and density functional theory(DFT)calculations,it is found that such a nitrogenous interface primarily blocks the cleavage of N-H bonds with a higher energy barrier,leading to the deactivation of Ru/TiO_(2)in NH_(3)decomposition.Nevertheless,the Ru/TiO_(x)interface can be easily restored by oxidation and a subsequent H2 reduction,contributing to the recovery of the catalytic activity toward NH_(3)decomposition.Our study provides a new insight into the deactivation mechanism of Ru/TiO_(2)in NH_(3)decomposition and highlights the significance of the dynamic evolution of the metal-support interfaces in the reactions.
基金supported by the National Natural Science Foundation of China(No.U20A2047)the Key Research and Development Project for Tibet Autonomous Region(No.XZ202201ZY0003N)+2 种基金the National Key Research and Development Program of China(No.2022YFD1901402)the Lasa Science and Technology Bureau(No.LSKJ202206)the Foundation of Graduate Research and Innovation in Chongqing(No.CYB22127).
文摘The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection.However,the impact of residual antibiotics,a common contaminant of manure,on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood.Here,we studied,how oxytetracycline(OTC)and ciprofloxacin(CIP)affect the decomposition,microbial community structure,extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments.Results showed that OTC and CIP greatly inhibited livestock manure decomposition,causing a decreased rate of carbon(28%-87%),nitrogen(15%-44%)and phosphorus(26%-43%)release.The relative abundance of gramnegative(G-)bacteria was reduced by 4.0%-13%while fungi increased by 7.0%-71%during a 28-day incubation period.Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions,particularly among G-bacteria,G+bacteria,and actinomycetes.These changes in microbial community structure and function resulted in decreased activity of urease,β-1,4-N-acetyl-glucosaminidase,alkaline protease,chitinase,and catalase,causing reduced decomposition and nutrient release in cattle and pig ma-nures.These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics,which will help facilitate sustainable agricultural production and soil carbon sequestration.
基金supported by the National Natural Science Foundation of China (Nos.21876019 and 22276022)the National Key Research and Development Program of China (No.2019YFC1903903).
文摘Monolithic catalysts with excellent O_(3)catalytic decomposition performance were prepared by in situ loading of Co-doped KMn_(8)O_(16)on the surface of nickel foam.The triple-layer structure with Co-doped KMn_(8)O_(16)/Ni6MnO_(8)/Ni foam was grown spontaneously on the surface of nickel foam by tuning the molar ratio of KMnO_(4)to Co(NO_(3))_(2)·6H_(2)O precursors.Importantly,the formed Ni6MnO_(8)structure between KMn_(8)O_(16)and nickel foam during in situ synthesis process effectively protected nickel foam from further etching,which significantly enhanced the reaction stability of catalyst.The optimum amount of Co doping in KMn_(8)O_(16)was available when the molar ratio of Mn to Co species in the precursor solution was 2:1.And the Mn2Co1 catalyst had abundant oxygen vacancies and excellent hydrophobicity,thus creating outstanding O_(3)decomposition activity.The O_(3)conversion under dry conditions and relative humidity of 65%,90%over a period of 5 hr was 100%,94%and 80%with the space velocity of 28,000 hr^(−1),respectively.The in situ constructed Co-doped KMn_(8)O_(16)/Ni foam catalyst showed the advantages of low price and gradual applicability of the preparation process,which provided an opportunity for the design of monolithic catalyst for O_(3)catalytic decomposition.
基金supported by the National Key Laboratory of Energetic Materials, China (Grant No. 2023-LB-036-09).
文摘In order to analyze the influences of storage aging on the safety of typical elemental explosives,the aged cyclotrimethylene trinitramine(RDX)and cyclotetramethylene tetranitramine(HMX)were prepared by isothermal aging tests.The reaction thresholds of aged RDX and HMX under any ignition probability were studied by Langlie-Optimal D method.The thermal decomposition characteristics of RDX and HMX after aging were analyzed by DSC and ARC.Experimental results showed that compared with unaged RDX and HMX,on the one hand,the critical impact energy and critical friction of RDX and HMX aged for 14,28,and 56 days are significantly reduced at an explosion probability of 50%,0.01%,and 0.0001%,respectively.With the increase of aging time,the mechanical sensitivity of RDX and HMX increases obviously.On the other hand,the initial decomposition temperature of RDX and HMX after 56 days of aging decreases,the decomposition heat decreases,the activation energy increases,and the reaction difficulty increases.
基金the financial support by the National Natural Science Foundation of China(Grant No.U24B2029)the Key Projects of the National Natural Science Foundation of China(Grant No.52334001)+1 种基金the Strategic Cooperation Technology Projects of CNPC and CUPB(Grand No.ZLZX2020-02)the China University of Petroleum,Beijing(Grand No.ZX20230042)。
文摘Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.
基金financially supported by the National Natural Science Foundation of China(21968028)the Xinjiang Tianchi Talent Project(CZ002732)。
文摘Alkaline earth-metal titanates ATiO_(3)(A=Ca,Sr,and Ba)with a perovskite-type structure were used as supports for Ru-based catalysts to produce CO_(x)-free H_(2)via NH_(3)decomposition.The effects of alkalineearth metals on the physicochemical characteristics and catalytic activities of Ru/ATiO_(3)for NH_(3)decomposition were investigated using various techniques.The order of Ru/ATiO_(3)for NH_(3)conversion is Ru/BaTiO_(3)>Ru/SrTiO_(3)>Ru/CaTiO_(3)>Ru/TiO_(2)at the identical conditions,with the Ru/BaTiO_(3)catalyst demonstrating the highest NH_(3)conversion of 77.8%at 450℃and a gas hourly space velocity of 30,000 mL/gcat/h,which is 8.7,2.1,and 1.3 times of that over Ru/TiO_(2),Ru/CaTiO_(3),and Ru/SrTiO_(3),respectively.The formation of the ATiO_(3)phase can enrich the concentration of basic sites and oxygen vacancies compared with TiO_(2),which can induce the presence of strong metal-support interaction(SMSI)through the formation of Ru-O-Ti bonds.This SMSI effect increased the dispersion and electron density of Ru nano-particles on ATiO_(3)supports,and the electron-rich Ru nano-particles could weaken the chemisorptive strength of N_(2)and H_(2)on the Ru/ATiO_(3)catalysts,thereby promoting the reaction rate for NH_(3)decomposition.
基金supported by the National Natural Science Foundation of China(32072655 and 32272792)。
文摘Cover cropping is a diversifying agricultural practice that can improve soil structure and function by altering the underground litter diversity and soil microbial communities.Here,we tested how a wheat cover crop alters the decomposition of cucumber root litter.A three-year greenhouse litterbag decomposition experiment showed that a wheat cover crop accelerates the decomposition of cucumber root litter.A microcosm litterbag experiment further showed that wheat litter and the soil microbial community could improve cucumber root litter decomposition.Moreover,the wheat cover crop altered the abundances and diversities of soil bacterial and fungal communities,and enriched several putative keystone operational taxonomic units(OTUs),such as Bacillus sp.OTU1837 and Mortierella sp.OTU1236,that were positively related to the mass loss of cucumber root litter.The representative bacterial and fungal strains B186 and M3 were isolated and cultured.In vitro decomposition tests demonstrated that both B186 and M3 had cucumber root litter decomposition activity and a stronger effect was found when they were co-incubated.Overall,a wheat cover crop accelerated cucumber root litter decomposition by altering the soil microbial communities,particularly by stimulating certain putative keystone taxa,which provides a theoretical basis for using cover crops to promote sustainable agricultural development.
基金supported by the Agency for Science,Technology and Research(A*STAR),Singapore,under the project Methane Pyrolysis for Hydrogen and Carbon Nanotube Production via Novel Catalytic Membrane Reactor System(No.U2102d2011)。
文摘The sustainability of methane catalytic decomposition is significantly enhanced by the production of high-quality value-added carbon products such as carbon nanotubes(CNTs).Understanding the production yields and properties of CNTs is crucial for improving process feasibility and sustainability.This study employs machine learning technique to develop and analyze predictive models for the carbon yield and mean diameter of CNTs produced through methane catalytic decomposition.Utilizing comprehensive datasets from various experimental studies,the models incorporate variables related to catalyst composition,catalyst preparation,and operational parameters.Both models achieved high predictive accuracy,with R^(2)values exceeding 0.90.Notably,the reduction time during catalyst preparation was found to critically influence carbon yield,evidenced by a permutation importance value of 39.62%.Additionally,the use of Mo as a catalytic metal was observed to significantly reduce the diameter of produced CNTs.These findings highlight the need for future machine learning and simulation studies to include catalyst reduction parameters,thereby enhancing predictive accuracy and deepening process insights.This research provides strategic guidance for optimizing methane catalytic decomposition to produce enhanced CNTs,aligning with sustainability goals.
基金supported by the National Natural Science Foundation of China under Grant 62301051.
文摘Range-azimuth imaging of ground targets via frequency-modulated continuous wave(FMCW)radar is crucial for effective target detection.However,when the pitch of the moving array constructed during motion exceeds the physical array aperture,azimuth ambiguity occurs,making range-azimuth imaging on a moving platform challenging.To address this issue,we theoretically analyze azimuth ambiguity generation in sparse motion arrays and propose a dual-aperture adaptive processing(DAAP)method for suppressing azimuth ambiguity.This method combines spatial multiple-input multiple-output(MIMO)arrays with sparse motion arrays to achieve high-resolution range-azimuth imaging.In addition,an adaptive QR decomposition denoising method for sparse array signals based on iterative low-rank matrix approximation(LRMA)and regularized QR is proposed to preprocess sparse motion array signals.Simulations and experiments show that on a two-transmitter-four-receiver array,the signal-to-noise ratio(SNR)of the sparse motion array signal after noise suppression via adaptive QR decomposition can exceed 0 dB,and the azimuth ambiguity signal ratio(AASR)can be reduced to below-20 dB.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11974154 and 12304278)the Taishan Scholars Special Funding for Construction Projects(Grant No.tstp20230622)+1 种基金the Natural Science Foundation of Shandong Province(Grant Nos.ZR2022MA004,ZR2023QA127,and ZR2024QA121)the Special Foundation of Yantai for Leading Talents above Provincial Level.
文摘As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional theory calculations to explore the structural phase transitions and electronic properties of calcium-sulfur compounds(Ca_(x)S_(1-x),where x=1/4,1/3,1/2,2/3,3/4,4/5)under 0-1200 GPa.The calculated formation enthalpies suggest that Ca_(x)S_(1-x)compounds undergo multiple phase transitions and eventually decompose into elemental Ca and S,challenging the traditional view that pressure stabilizes and densifies compounds.The analysis of formation enthalpy indicates that an increase in pressure leads to a rise in internal energy and the PV term,resulting in thermodynamic instability.Bader charge analysis reveals that this phenomenon is attributed to a decrease in charge transfer under high pressure.The activation of Ca-3d orbitals is significantly enhanced under pressure,leading to competition with Ca-4s orbitals and S-3p orbitals.This may cause the formation enthalpy minimum on the convex hull to shift sequentially from CaS to CaS_(3),then to Ca_(3)S and Ca_(2)S,and finally back to CaS.These findings provide critical insights into the behavior of alkaline-earth metal sulfides under high pressure,with implications for the synthesis and application of novel materials under extreme conditions and for understanding element distribution in planetary interiors.
基金The Australian Research Council(DP200101197,DP230101107).
文摘Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale optimization problems are solved using computing machines,leading to an enormous computational time being required,which may delay deriving timely solutions.Decomposition methods,which partition a large-scale optimization problem into lower-dimensional subproblems,represent a key approach to addressing time-efficiency issues.There has been significant progress in both applied mathematics and emerging artificial intelligence approaches on this front.This work aims at providing an overview of the decomposition methods from both the mathematics and computer science points of view.We also remark on the state-of-the-art developments and recent applications of the decomposition methods,and discuss the future research and development perspectives.
