Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl m...Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl methylphosphonate(DIMP),a commonly used CWA surrogate,is widely studied to enhance our understanding of CWA behavior.The prevailing thermal decomposition model for DIMP,developed approximately 25 years ago,is based on data collected in nitrogen atmospheres at temperatures ranging from 700 K to 800 K.Despite its limitations,this model continues to serve as a foundation for research across various thermal and reactive environments,including combustion studies.Our recent experiments have extended the scope of decomposition analysis by examining DIMP in both nitrogen and zero air across a lower temperature range of 175??C to 250??C.Infrared spectroscopy results under nitrogen align well with the established model;however,we observed that catalytic effects,stemming from decomposition byproducts and interactions with stainless steel surfaces,alter the reaction kinetics.In zero air environments,we observed a novel infrared absorption band.Spectral fitting suggests this band may represent a combination of propanal and acetone,while GCMS analysis points to vinyl formate and acetone as possible constituents.Although the precise identity of these new products remains unresolved,our findings clearly indicate that the existing decomposition model cannot be reliably extended to lower temperatures or non-nitrogen environments without further revisions.展开更多
The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.A...The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.Accordingly,comprehensive kinetic study by employing thermalgravimetric analysis at various heating rates was presented in this paper.Two main weight loss regions were observed during heating.The initial region corresponded to the dehydration of crystal water,whereas the subsequent region with overlapping peaks involved complex decomposition reactions.The overlapping peaks were separated into two individual reaction peaks and the activation energy of each peak was calculated using isoconversional kinetics methods.The activation energy of peak 1 exhibited a continual increase as the reaction conversion progressed,while that of peak 2 steadily decreased.The optimal kinetic models,identified as belonging to the random nucleation and subsequent growth category,provided valuable insights into the mechanism of the decomposition reactions.Furthermore,the adjustment factor was introduced to reconstruct the kinetic mechanism models,and the reconstructed models described the kinetic mechanism model more accurately for the decomposition reactions.This study enhanced the understanding of the thermochemical behavior and kinetic parameters of the lepidolite sulfation product decomposition reactions,further providing theoretical basis for promoting the selective extraction of lithium.展开更多
The microstructural evolution of Cu−19Ni−6Cr−7Mn alloy during aging treatment was investigated.After aging for 120 min at 500℃,the alloy exhibited excellent mechanical properties,including a tensile strength of 978 M...The microstructural evolution of Cu−19Ni−6Cr−7Mn alloy during aging treatment was investigated.After aging for 120 min at 500℃,the alloy exhibited excellent mechanical properties,including a tensile strength of 978 MPa and an elastic modulus of 145.8 GPa.After aging for 240 min at 500℃,the elastic modulus of the alloy reached 149.5 GPa,which was among the highest values reported for Cu alloys.It was worth mentioning that the tensile strength increased rapidly from 740 to 934 MPa after aging for 5 min at 500℃,which was close to the maximum tensile strength(978 MPa).Analysis of the underlying strengthening mechanisms and phase transformation behavior revealed that the Cu−19Ni−6Cr−7Mn alloy underwent spinodal decomposition and DO_(22) ordering during the first 5 min of aging at 500℃,and L1_(2) ordered phases and bcc-Cr precipitates appeared.Therefore,the enhanced mechanical properties of the Cu−19Ni−6Cr−7Mn alloy can be attributed to the stress field generated by spinodal decomposition and the presence of nanoscale ordered phase and Cr precipitates.展开更多
Protonic ceramic fuel cells(PCFCs)have been recognized as promising power generation devices for future clean energy systems,owing to their relatively low activation energy for proton migration and high energy convers...Protonic ceramic fuel cells(PCFCs)have been recognized as promising power generation devices for future clean energy systems,owing to their relatively low activation energy for proton migration and high energy conversion efficiency.In certain application scenarios,the use of N_(2)O(a potent greenhouse gas),as an alternative oxidant to air,presents a feasible strategy.Herein,we report for the first time the operation of PCFCs employing N_(2)O as the oxidant.A hybrid Pr_(2)Ni_(0.6)Co_(0.4)O_(4-δ)(PNCO-214)catalyst is developed,comprising Ruddlesden-Popper(R-P)structured Pr_(4)Ni_(1.8)Co_(1.2)O_(10-δ)(PNCO-4310)and fluorite structured Pr_(6)O_(11)(PO-611),which synergistically exhibits exceptional catalytic activity toward both N_(2)O decomposition and the oxygen reduction reaction,achieving a conversion over 92% and an area specific resistance of 1.301Ω·cm^(2) at 600℃.Quasi-insitu temperature-dependent Fourier transform infrared(FTIR)and electrochemical impedance spectroscopy analyses reveal that abundant oxygen vacancies in PNCO-214 facilitate rapid adsorption and dissociation of N_(2)O into N_(2) and O_(2),while also promoting the surface exchange kinetics of proton/oxygen during oxygen reduction reaction(ORR).When applied in an anode-supported single cell with PNCO-214 cathode operating under N_(2)O,outstanding power density and low resistance are achieved,delivering 0.801 W·cm^(-2) and 0.245Ω·cm^(2) at 600℃.Satisfactory performance is also maintained even when the temperature is reduced to 500℃.Furthermore,the single cell demonstrates relatively good stability with negligible degradation over 130 h at 600℃ and 0.7 V.These findings underscore the potential of PNCO-214 as a highly effective cathode catalyst for enabling the use of N_(2)O as a viable oxidant in PCFCs for specific industrial applications.展开更多
As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limite...As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.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
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
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
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
In this paper,we prove that L(K_(x,y))(λ),theλ-fold line graph of the complete bipartite graph Ka,y,has a C_(6)-decomposition if and only if ry≥6,λxy(c+y-2)=0(mod 12)and(x+y)=0(mod 2),where x,y are nonnegative int...In this paper,we prove that L(K_(x,y))(λ),theλ-fold line graph of the complete bipartite graph Ka,y,has a C_(6)-decomposition if and only if ry≥6,λxy(c+y-2)=0(mod 12)and(x+y)=0(mod 2),where x,y are nonnegative integers and(x,y)≠(2,4)or(2,5).展开更多
To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.展开更多
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
In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed p...In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various re...We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various reaction conditions under microwave irradiation(0-50 W).These conditions included reaction temperatures(300-600℃),oxygen concentrations(0-6%),and varying K^(+)doping levels on the catalysts'activity.The crystalline phase,microstructure,and the catalytic activity of the catalyst were analyzed by XRD,TEM,H_2-TPR,and O_(2)-TPD.The experimental results reveal that La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts consistently form homogeneous perovskite nanoparticles across different doping levels.The NO decomposition efficiency on these catalysts initially increases and then decreases with variations in doping amount,temperature,and microwave power.Additionally,an increase in oxygen concentration positively influences NO conversion rates.The optimal performance is observed with La_(0.7)K_(0.3)CoO_(3)catalyst under conditions of x=0.3,400℃,10 W microwave power,and 4%oxygen concentration,achieving a peak NO conversion rate of La_(0.7)K_(0.3)CoO_(3)catalyst is 93.1%.展开更多
Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaboratio...Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.展开更多
Organic matter(OM)derived from the decomposition of crop residues plays a key role as a sorbent for cadmium(Cd)immobilization.Few studies have explored the straw decomposition processes with the presence of minerals,a...Organic matter(OM)derived from the decomposition of crop residues plays a key role as a sorbent for cadmium(Cd)immobilization.Few studies have explored the straw decomposition processes with the presence of minerals,and the effect of newly generated organomineral complexes on heavy metal adsorption.In this study,we investigated the variations in structure and composition during the rice straw decomposition with or without minerals(goethite and kaolinite),as well as the adsorption behavior and mechanisms by which straw decomposition affects Cd immobilization.The degree of humification of extracted straw organic matter was assessed using excitation-emission matrix(EEM)fluorescence and Ultraviolet-visible spectroscopy(UV-vis),while employing FTIR spectroscopy and XPS to characterize the adsorption mechanisms.The spectra analysis revealed the enrichment of highly aromatic and hydrophobic components,indicating that the degree of straw decomposition and humification were further intensified during incubation.Additionally,the existence of goethite(SG)accelerated the humification of OM.Sorption experiments revealed that the straw humification increased Cd adsorption capacity.Notably,SG exhibited significantly higher adsorption performance compared to the organic matter without minerals(RS)and the existence of kaolinite(SK).Further analysis using FT-IR spectroscopy and XPS verified that the primarymechanisms involved in Cd immobilization were complexion with—OH and—COOH,as well as the formation of Cd-πbinds with aromatic C=C on the surface of solid OMs.These findings will facilitate understanding the interactions of the rice straw decomposing with soil minerals and its remediation effect on Cd-contaminated farmland.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
Earthquakes can cause significant hazards,leading to loss of human life and property damage.In this study,we applied the masking empirical mode decomposition(EMD)to the Gyeongju earthquake(ML 5.8)in South Korea and th...Earthquakes can cause significant hazards,leading to loss of human life and property damage.In this study,we applied the masking empirical mode decomposition(EMD)to the Gyeongju earthquake(ML 5.8)in South Korea and the Abe Pura earthquake(ML 7.0)in Indonesia and then revealed that the 7th intrinsic mode function(IMF7),composed of waveforms with a period band of approximately 150 to 750 minutes.展开更多
基金sponsored by the Department of Defense,Defense Threat Reduction Agency under the Materials Science in Extreme Environments University Research Alliance,HDTRA1-20-2-0001。
文摘Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl methylphosphonate(DIMP),a commonly used CWA surrogate,is widely studied to enhance our understanding of CWA behavior.The prevailing thermal decomposition model for DIMP,developed approximately 25 years ago,is based on data collected in nitrogen atmospheres at temperatures ranging from 700 K to 800 K.Despite its limitations,this model continues to serve as a foundation for research across various thermal and reactive environments,including combustion studies.Our recent experiments have extended the scope of decomposition analysis by examining DIMP in both nitrogen and zero air across a lower temperature range of 175??C to 250??C.Infrared spectroscopy results under nitrogen align well with the established model;however,we observed that catalytic effects,stemming from decomposition byproducts and interactions with stainless steel surfaces,alter the reaction kinetics.In zero air environments,we observed a novel infrared absorption band.Spectral fitting suggests this band may represent a combination of propanal and acetone,while GCMS analysis points to vinyl formate and acetone as possible constituents.Although the precise identity of these new products remains unresolved,our findings clearly indicate that the existing decomposition model cannot be reliably extended to lower temperatures or non-nitrogen environments without further revisions.
基金financially supported by the National Natural Science Foundation of China(Nos.52034002 and U2202254)the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-19-001)。
文摘The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.Accordingly,comprehensive kinetic study by employing thermalgravimetric analysis at various heating rates was presented in this paper.Two main weight loss regions were observed during heating.The initial region corresponded to the dehydration of crystal water,whereas the subsequent region with overlapping peaks involved complex decomposition reactions.The overlapping peaks were separated into two individual reaction peaks and the activation energy of each peak was calculated using isoconversional kinetics methods.The activation energy of peak 1 exhibited a continual increase as the reaction conversion progressed,while that of peak 2 steadily decreased.The optimal kinetic models,identified as belonging to the random nucleation and subsequent growth category,provided valuable insights into the mechanism of the decomposition reactions.Furthermore,the adjustment factor was introduced to reconstruct the kinetic mechanism models,and the reconstructed models described the kinetic mechanism model more accurately for the decomposition reactions.This study enhanced the understanding of the thermochemical behavior and kinetic parameters of the lepidolite sulfation product decomposition reactions,further providing theoretical basis for promoting the selective extraction of lithium.
基金supported by the National Key R&D Program of China (No. 2021YFB3700700)the Henan Province Top Talent Training Program Project, China (No. 244500510020)the High-level Talent Research Start-up Project Funding of Henan Academy of Sciences, China (No. 242017001)。
文摘The microstructural evolution of Cu−19Ni−6Cr−7Mn alloy during aging treatment was investigated.After aging for 120 min at 500℃,the alloy exhibited excellent mechanical properties,including a tensile strength of 978 MPa and an elastic modulus of 145.8 GPa.After aging for 240 min at 500℃,the elastic modulus of the alloy reached 149.5 GPa,which was among the highest values reported for Cu alloys.It was worth mentioning that the tensile strength increased rapidly from 740 to 934 MPa after aging for 5 min at 500℃,which was close to the maximum tensile strength(978 MPa).Analysis of the underlying strengthening mechanisms and phase transformation behavior revealed that the Cu−19Ni−6Cr−7Mn alloy underwent spinodal decomposition and DO_(22) ordering during the first 5 min of aging at 500℃,and L1_(2) ordered phases and bcc-Cr precipitates appeared.Therefore,the enhanced mechanical properties of the Cu−19Ni−6Cr−7Mn alloy can be attributed to the stress field generated by spinodal decomposition and the presence of nanoscale ordered phase and Cr precipitates.
基金financially supported by the National Key R&D Program of China(No.2024YFF0506300)National Natural Science Foundation of China(No.52336009)+5 种基金Key Research and Development Program of Shaanxi(No.2024CY2-GJHX-66)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515010429)Natural Science Basic Research Plan in Shaanxi Province of China(No.2024JC-YBQN-0475)Xidian University Specially Funded Project for Interdisciplinary Exploration(No.TZJH2024063)the Fundamental Research Funds for the Central Universities(No.QTZX23061)the Innovation Center of Nuclear Power Technology(No.HDLCXZX-2022-ZH-013).
文摘Protonic ceramic fuel cells(PCFCs)have been recognized as promising power generation devices for future clean energy systems,owing to their relatively low activation energy for proton migration and high energy conversion efficiency.In certain application scenarios,the use of N_(2)O(a potent greenhouse gas),as an alternative oxidant to air,presents a feasible strategy.Herein,we report for the first time the operation of PCFCs employing N_(2)O as the oxidant.A hybrid Pr_(2)Ni_(0.6)Co_(0.4)O_(4-δ)(PNCO-214)catalyst is developed,comprising Ruddlesden-Popper(R-P)structured Pr_(4)Ni_(1.8)Co_(1.2)O_(10-δ)(PNCO-4310)and fluorite structured Pr_(6)O_(11)(PO-611),which synergistically exhibits exceptional catalytic activity toward both N_(2)O decomposition and the oxygen reduction reaction,achieving a conversion over 92% and an area specific resistance of 1.301Ω·cm^(2) at 600℃.Quasi-insitu temperature-dependent Fourier transform infrared(FTIR)and electrochemical impedance spectroscopy analyses reveal that abundant oxygen vacancies in PNCO-214 facilitate rapid adsorption and dissociation of N_(2)O into N_(2) and O_(2),while also promoting the surface exchange kinetics of proton/oxygen during oxygen reduction reaction(ORR).When applied in an anode-supported single cell with PNCO-214 cathode operating under N_(2)O,outstanding power density and low resistance are achieved,delivering 0.801 W·cm^(-2) and 0.245Ω·cm^(2) at 600℃.Satisfactory performance is also maintained even when the temperature is reduced to 500℃.Furthermore,the single cell demonstrates relatively good stability with negligible degradation over 130 h at 600℃ and 0.7 V.These findings underscore the potential of PNCO-214 as a highly effective cathode catalyst for enabling the use of N_(2)O as a viable oxidant in PCFCs for specific industrial applications.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037,and 42105133)the National Key Research and Development Program of China(No.2022YFC3703502)+1 种基金the Plan for Anhui Major Provincial Science&Technology Project(No.202203a07020003)Hefei Ecological Environment Bureau Project(No.2020BFFFD01804).
文摘As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion.However,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants.To address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022.The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data.Temporally,there were pronounced seasonal variations in air pollutants.Spatially,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial emissions.The study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer.In spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two rings.This study offers data support for formulating the next phase of air pollution control measures in urban areas.
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 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 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 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.
文摘In this paper,we prove that L(K_(x,y))(λ),theλ-fold line graph of the complete bipartite graph Ka,y,has a C_(6)-decomposition if and only if ry≥6,λxy(c+y-2)=0(mod 12)and(x+y)=0(mod 2),where x,y are nonnegative integers and(x,y)≠(2,4)or(2,5).
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
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 the National Office for Philosophy and Social Sciences(grant reference 22&ZD067).
文摘In the current situation of decelerating economic expansion,examining the digital economy(DE)as a novel economic model is beneficial for the local economy’s sustainable and high-quality development(HQD).We analyzed panel data from the Yellow River(YR)region from 2013 to 2021 and discovered notable spatial variances in the composite index and coupling coordination of the two systems.Specifically,the downstream region exhibited the highest coupling coordination,while the upstream region had the lowest.We identified that favorable factors such as economic development,innovation,industrial upgrading,and government intervention can bolster the coupling.Our findings provide a valuable framework for promoting DE and HQD in the YR region.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
文摘We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various reaction conditions under microwave irradiation(0-50 W).These conditions included reaction temperatures(300-600℃),oxygen concentrations(0-6%),and varying K^(+)doping levels on the catalysts'activity.The crystalline phase,microstructure,and the catalytic activity of the catalyst were analyzed by XRD,TEM,H_2-TPR,and O_(2)-TPD.The experimental results reveal that La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts consistently form homogeneous perovskite nanoparticles across different doping levels.The NO decomposition efficiency on these catalysts initially increases and then decreases with variations in doping amount,temperature,and microwave power.Additionally,an increase in oxygen concentration positively influences NO conversion rates.The optimal performance is observed with La_(0.7)K_(0.3)CoO_(3)catalyst under conditions of x=0.3,400℃,10 W microwave power,and 4%oxygen concentration,achieving a peak NO conversion rate of La_(0.7)K_(0.3)CoO_(3)catalyst is 93.1%.
基金supported by the Beijing Natural Science Foundation(Certificate Number:L234025).
文摘Spatial-temporal traffic prediction technology is crucial for network planning,resource allocation optimizing,and user experience improving.With the development of virtual network operators,multi-operator collaborations,and edge computing,spatial-temporal traffic data has taken on a distributed nature.Consequently,noncentralized spatial-temporal traffic prediction solutions have emerged as a recent research focus.Currently,the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station.This method reduces additional burden on communication systems.However,this method has a drawback:it cannot handle irregular traffic data.Due to unstable wireless network environments,device failures,insufficient storage resources,etc.,data missing inevitably occurs during the process of collecting traffic data.This results in the irregular nature of distributed traffic data.Yet,commonly used traffic prediction models such as Recurrent Neural Networks(RNN)and Long Short-Term Memory(LSTM)typically assume that the data is complete and regular.To address the challenge of handling irregular traffic data,this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic.To solve the aforementioned problems,this paper introduces split learning to design a structured distributed learning framework.The framework comprises a Global-level Spatial structure mining Model(GSM)and several Nodelevel Generative Models(NGMs).NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller.Firstly,the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables.Secondly,GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data.Finally,NGM generates future traffic based on latent temporal and spatial feature variables.The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data.Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction,which compensates for missing information in local irregular traffic data.The proposed framework effectively addresses the distributed prediction issues of irregular traffic data.By testing on real world datasets,the proposed framework improves traffic prediction accuracy by 35%compared to other commonly used distributed traffic prediction methods.
基金supported by the National Key Research and Development Program of China(No.2022YFD1700102)the National Natural Science Foundation of China(No.U20A20108)the Hunan Provincial Natural Science Foundation of China(No.2021JJ30357).
文摘Organic matter(OM)derived from the decomposition of crop residues plays a key role as a sorbent for cadmium(Cd)immobilization.Few studies have explored the straw decomposition processes with the presence of minerals,and the effect of newly generated organomineral complexes on heavy metal adsorption.In this study,we investigated the variations in structure and composition during the rice straw decomposition with or without minerals(goethite and kaolinite),as well as the adsorption behavior and mechanisms by which straw decomposition affects Cd immobilization.The degree of humification of extracted straw organic matter was assessed using excitation-emission matrix(EEM)fluorescence and Ultraviolet-visible spectroscopy(UV-vis),while employing FTIR spectroscopy and XPS to characterize the adsorption mechanisms.The spectra analysis revealed the enrichment of highly aromatic and hydrophobic components,indicating that the degree of straw decomposition and humification were further intensified during incubation.Additionally,the existence of goethite(SG)accelerated the humification of OM.Sorption experiments revealed that the straw humification increased Cd adsorption capacity.Notably,SG exhibited significantly higher adsorption performance compared to the organic matter without minerals(RS)and the existence of kaolinite(SK).Further analysis using FT-IR spectroscopy and XPS verified that the primarymechanisms involved in Cd immobilization were complexion with—OH and—COOH,as well as the formation of Cd-πbinds with aromatic C=C on the surface of solid OMs.These findings will facilitate understanding the interactions of the rice straw decomposing with soil minerals and its remediation effect on Cd-contaminated farmland.
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supported as part of the"2024 Groundwater Basic Survey Project,"funded by the Ministry of Environment(Project No.20240123-001)。
文摘Earthquakes can cause significant hazards,leading to loss of human life and property damage.In this study,we applied the masking empirical mode decomposition(EMD)to the Gyeongju earthquake(ML 5.8)in South Korea and the Abe Pura earthquake(ML 7.0)in Indonesia and then revealed that the 7th intrinsic mode function(IMF7),composed of waveforms with a period band of approximately 150 to 750 minutes.
