Catalytic decomposition of methane,which produces high-purity hydrogen and high-value-added carbon nanomaterials,has shown considerable potential for development and is expected to yield significant economic benefits ...Catalytic decomposition of methane,which produces high-purity hydrogen and high-value-added carbon nanomaterials,has shown considerable potential for development and is expected to yield significant economic benefits in the future.However,designing catalysts that simultaneously exhibit high activity and long-term stability remains a significant challenge.Tuning the catalyst’s structure and electronic properties is an effective strategy for enhancing the reaction performance.In this work,a series of NixZr/ZSM-5 catalysts were prepared using the incipient wetness impregnation method,and the effect of Zr loadings on catalyst properties and performance was systematically investigated.The calcined and reduced catalysts were characterized by low-temperature N_(2)adsorption-desorption,XRD,SEM,H_(2)-TPR and XPS.The results showed that the addition of Zr significantly increased the specific surface area of the catalyst and reduced the metal particle size.Smaller NiO particles were found to enter the pores of the HZSM-5 support,and electronic interactions between NiO and ZrO_(2)markedly enhanced the metal-support interaction.The catalyst exhibited optimal catalytic performance at a Zr loading of 5%,achieving a maximum methane conversion of 68%at 625℃,maintaining activity for 900 min,and delivering a carbon yield of 1927%.Further increasing the Zr loading yielded only limited improvements in catalytic performance.Characterization of the spent catalysts and carbon products via TEM,Raman spectroscopy,and TGA revealed that the introduction of ZrO_(2)reduced metal sintering and promoted a shift in carbon nanofibers growth mode from tip-growth to base-growth.The mechanism of base-growth enabled the catalyst to maintain reaction activity for an extended period.展开更多
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℃ to 250℃.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
In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,eff...In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).展开更多
Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics i...Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics in this region remains unexplored at finer scales.This study investigates how micro site conditions influence litter decomposition of 15 upper Andean species over time.A reciprocal translocation field experiment was conducted over 18 months in 14 permanent plots within four sites in Colombian Andean mountain forests.Each plot contained three litterbeds(microsites),each with the 15 species,harvested at 3,6,12 and 18 months,totaling 2520 litterbags.Different forest variables,including canopy openness,leaf area index,slope and depth of litter,were measured in each litterbed.ANOVAs and linear mixed models were used to assess variation between sites and plots respectively,while multiple linear regression analyses evaluated the effects of forest variables on decay rates over time at the micro site scale.Results showed differences in absolute decay rates between sites but consistent relative decay rates,indicating varying magnitudes of decomposition,yet maintaining the same order based on their litter quality.Decay rates varied between species,with more variation in labile species compared to recalcitrant ones.Despite substantial variation in forest characteristics within sites,their influence on litter decomposition was minimal and declined over time.This suggests that,at finer spatial scales,the forest microenvironment plays a lesser role in litter decomposition,with litter quality emerging as the primary driver.This study is a step towards understanding the fine-scale dynamics of litter decomposition in upper Andean tropical forests,highlighting the intricate interplay between microenvironmental factors and decomposition processes.展开更多
Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and ...Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and diversity on leaf litter decomposition in forests remains understudied.By controlling for macroclimate,soil properties,and litter substrate in a mature common garden,we investigated whether a three-month tea bag incubation of standardized green and rooibos tea substrate is driven by canopy tree species characteristics and diversity.Our study hypothesized two primary pathways:a chemical engineering effect,where trees alter soil properties and decomposer communities through litter input,and a physical engineering effect,where tree canopy structure modulates the local microclimate.The results showed that even under uniform macroclimatic and initial soil conditions,mass loss rates varied widely for green tea(27.4%–73.2%)and rooibos tea(6.1%–34.7%),comparable as found in other research between distinct biomes.While substrate quality was the dominant factor,both engineering pathways and,to a minor extent,tree diversity modulated mass losses.For green tea,tree chemical and physical characteristics seemed equally important,while the physical environment showed an increased importance for rooibos.Incubation depth played a key role,where forest floor decomposition rates are more susceptible to temporal climate variations,and soil-layer decomposition rates are less susceptible to climate variations and more determined by tree species identity.Our findings suggest that tea bag experiments focusing solely on topsoil burial may underestimate processes in the forest floor and the mineralorganic boundary layer.This study underscores the critical role of litter substrate quality in decomposition while demonstrating that tree community composition and the associated herbaceous layer,through both chemical and physical engineering pathways,strongly modulate decomposition rates.展开更多
Renewable electricity-driven production of value-added sulfur and H_(2)via electrocatalytic H_(2)S decomposition represents a sustainable route to conventional thermocatalysis.Both the electrocatalyst and electrolyte ...Renewable electricity-driven production of value-added sulfur and H_(2)via electrocatalytic H_(2)S decomposition represents a sustainable route to conventional thermocatalysis.Both the electrocatalyst and electrolyte solution strongly impact the H_(2)S decomposition performance.Despite significant progress in developing sophisticated electrocatalysts,a well-designed electrolyte solution in conjunction with industrial catalysts is an attractive strategy to advance the industrialization process of electrocatalytic H_(2)S decomposition,but remains unexplored.Here,for the first time,we design a solid-liquid-gas three-phase indirect electrolysis system based on a kind of CS_(2)-N electrolyte solution and Ni-Mo_(2)C that can efficiently enable H_(2)S decomposition into valuable H_(2)and sulfur.Specifically,the solid-phase Ni-Mo_(2)C as a heterogeneous redox mediator presents excellent electrocatalytic efficiency for the H_(2)S removal efficiency of up to 99%,and the formation of liquid-phase sulfur product(CS_(2)-N electrolyte solution dissolves sulfur,yield up to 95%)with the generation of gas-phase H_(2)product(~1.32 mL min^(-1)),resulting in an interesting three-phase indirect electrolysis system.Remarkably,it enables the scale-up production(~6 g in a batch experiment)of sulfur with continuous operation for 120 h without attenuation.This work may inaugurate a new electrocatalytic H_(2)S decomposition avenue to explore porous metal materials and electrolyte systems in simultaneous production of value-added sulfur and H_(2).展开更多
The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a nove...The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines,leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms.First,the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems,and examines the voltage characteristics of faulted phases in renewable energy scenarios.The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults,whereas permanent faults do not exhibit such variations.Building on this observation,the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition,wherein low-frequency waveform components are selected to characterize fault features.Subsequently,a fault nature identification method is introduced,based on a voltage waveform time-frequency morphology comparison.By employing a windowing technique to quantify waveform differences before and after arc extinction,this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time.Finally,a 220 kV renewable energy grid connection line model is developed using PSCAD for verification.The results demonstrate that the proposed method is highly adaptable across various fault locations,transition resistances,and renewable energy control strategies,and can reliably identify fault nature in renewable energy grid connection scenarios.展开更多
Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this pa...Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.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
Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decompos...Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.展开更多
In this article, per capita urban carbon emissions were decomposed into manufacturing,transportation, and construction sectors using logarithmic mean Divisia index(LMDI)method. This new decomposition method can provid...In this article, per capita urban carbon emissions were decomposed into manufacturing,transportation, and construction sectors using logarithmic mean Divisia index(LMDI)method. This new decomposition method can provide information about specific drivers of carbon emissions, including urban growth and resident living standards, rather than general demographic and economic factors identified by traditional methods. Using four Chinese megacities(Beijing, Tianjin, Shanghai, and Chongqing) as case studies, we analyzed the factors that influenced per capita carbon emissions from 2010 to 2015. The results showed that per capita carbon emissions increased in Tianjin and Chongqing whereas decreased in Beijing and Shanghai, and that manufacturing was a key driving force. In these four megacities,energy conservation strategies were successfully implemented despite poor energy structure optimization during 2010–2015. Development of manufacturing and improvement of resident living standards in the cities led to an increase in carbon emissions. The unique dual-core urban form of Tianjin might mitigate the increased carbon emissions caused by the transportation sector. Reductions in carbon emissions could be achieved by further optimizing energy structures, limiting the number of private cars, and controlling per capita construction.展开更多
Coarse woody debris （CWD） is an important and particular component of forest ecosystems and is extremely important to forest health. This review describes the decomposition process, decomposition model and influence...Coarse woody debris （CWD） is an important and particular component of forest ecosystems and is extremely important to forest health. This review describes the decomposition process, decomposition model and influence factors. CWD decomposition is a complex and continuous process and characterizes many biological and physical processes, including biological respiration, leaching, and fragmentation. All these processes have closed relationships between each other and work synergistically. During decomposition, there are many controlling factors mainly including site conditions （temperature, humidity, and OJCO2concentration）, woody substrate quality （diameter, species and compound） and organism in CWD. The decomposition rate is generally expresses through a constant k which indicate the percent mass, volume or density loss over time, and can be determined by long-term monitoring, chronosequence approach and the radio between input and the total mass. Now using mathematical models to simulate decomposition patterns and estimate the decomposition rate is widely applied, especially the exponential model. We brought forward that managing and utilizing for the CWD in forest was a primary objective on all forest lands. And it is should be intensified to integrate many related research subjects and to carry a comprehensive, long-term and multi-scale research which mainly focus on seven sections.展开更多
This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing...This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.展开更多
Carbon emission is the current hot issue of global concern. How to assess various contributing factors for carbon emission is of great importance to find out the key factors and promote carbon emission reduction. In t...Carbon emission is the current hot issue of global concern. How to assess various contributing factors for carbon emission is of great importance to find out the key factors and promote carbon emission reduction. In this paper, the author constructs an identical equation for carbon emission, based on the economic aggregate, the economic structure, the efficiency of energy utilization, the structure of energy consumption, and the coefficient of carbon emission; by applying to LMDI decomposition technology, the author analyzes the carbon emission of China from 1995 to 2007 at industrial level and regional level. The results show that the expansion of economic aggregate is the main reason for China' s rapidly increasing carbon emission and the increase of energy utilization efficiency is the key factor that can hold back the increase of carbon emission. In addition, the change of industrial structure or regional structure and the change of traditional energy structure have limited influence on the carbon emission, and their potentials have not yet been exploited. At the end of this paper, the author proposes the efforts that China should make to reduce carbon emission.展开更多
[Objective] By decomposing and studying the relative factors of carbon emissions in Guangdong Province,the policy and suggestion on further keeping the sustainable development were put forward,which provided the refer...[Objective] By decomposing and studying the relative factors of carbon emissions in Guangdong Province,the policy and suggestion on further keeping the sustainable development were put forward,which provided the reference for the carbon emission reduction in other provinces.[Method] Based on the carbon emissions formula which was put forward by Johan,three factors(the energy structure,energy efficiency and economy development) which affected the carbon emissions during 1996-2009 in Guangdong Province were studied by using Divisia decomposition method of logarithmic mean weight(LMD).[Result] The economy development was the main reason that caused the continuous significant increase of carbon emissions in Guangdong Province.The improvement of energy efficiency was the important manner for decreasing the energy consumption and the carbon emissions.The adjustment and optimization of energy consumption structure had the huge potential for reducing the carbon emissions in Guangdong Province.[Conclusion] The carbon emissions in Guangdong Province would continue to increase in the future for a long time.When formulated the development strategy in the future,it needed pay special attention to keep the accord development of economy and environment.展开更多
The physical decomposition method separates atmospheric variables into four parts, correlating each with solar radiation, land-sea distribution, and inter-annual and seasonal internal forcing, strengthening the anomal...The physical decomposition method separates atmospheric variables into four parts, correlating each with solar radiation, land-sea distribution, and inter-annual and seasonal internal forcing, strengthening the anomaly signal and increasing the correlation between variables. This method was applied to the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research （NCEP/NCAR）, to study the effects of Arctic factors （Arctic oscillation （AO） and Arctic polar vortex） on wintertime temperatures in the Northern Hemisphere and China. It was fotmd that AO effects on zonal average temperature disturbance could persist for 1 month. In the AO negative phase in wintertime, the temperatures are lower in the mid-high latitudes than in normal years, but higher in low latitudes. When the polar vortex area is bigger, the zonal average temperature is lower at 50N. Influenced mainly by meridional circulation enhancement, cold air flows from high to low latitudes; thus, the temperatures in Continental Europe and the North American continent exhibit an antiphase seesaw relationship. When the AO is in negative phase and the Arctic polar vortex larger, the temperature is lower in Siberia, but higher in Greenland and the Bering Strait. Influenced by westerly troughs and ridges, the polar air disperses mainly along the tracks of atmospheric activity centers. The AO index can be considered a predictor of wintertime temperature in China. When the AO is in negative phase or the Asian polar vortex is intensified, temperatures in Northeast China and Inner Mongolia are lower, because under the influence of the Siberia High and northeast cold vortex, the cold air flows southwards.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
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
基金Supported by Innovative Research Groups of the National Natural Science Foundation of China(22021004)。
文摘Catalytic decomposition of methane,which produces high-purity hydrogen and high-value-added carbon nanomaterials,has shown considerable potential for development and is expected to yield significant economic benefits in the future.However,designing catalysts that simultaneously exhibit high activity and long-term stability remains a significant challenge.Tuning the catalyst’s structure and electronic properties is an effective strategy for enhancing the reaction performance.In this work,a series of NixZr/ZSM-5 catalysts were prepared using the incipient wetness impregnation method,and the effect of Zr loadings on catalyst properties and performance was systematically investigated.The calcined and reduced catalysts were characterized by low-temperature N_(2)adsorption-desorption,XRD,SEM,H_(2)-TPR and XPS.The results showed that the addition of Zr significantly increased the specific surface area of the catalyst and reduced the metal particle size.Smaller NiO particles were found to enter the pores of the HZSM-5 support,and electronic interactions between NiO and ZrO_(2)markedly enhanced the metal-support interaction.The catalyst exhibited optimal catalytic performance at a Zr loading of 5%,achieving a maximum methane conversion of 68%at 625℃,maintaining activity for 900 min,and delivering a carbon yield of 1927%.Further increasing the Zr loading yielded only limited improvements in catalytic performance.Characterization of the spent catalysts and carbon products via TEM,Raman spectroscopy,and TGA revealed that the introduction of ZrO_(2)reduced metal sintering and promoted a shift in carbon nanofibers growth mode from tip-growth to base-growth.The mechanism of base-growth enabled the catalyst to maintain reaction activity for an extended period.
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℃ to 250℃.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funded by Anhui Province University Key Science and Technology Project(2024AH053415)Anhui Province University Major Science and Technology Project(2024AH040229)+3 种基金Talent Research Initiation Fund Project of Tongling University(2024tlxyrc019)Tongling University School-Level Scientific Research Project(2024tlxyptZD07)TheUniversity Synergy Innovation Programof Anhui Province(GXXT-2023-050)Tongling City Science and Technology Major Special Project(Unveiling and Commanding Model)(200401JB004).
文摘In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).
基金supported by the Universidad del Rosario(Small grant ID:IV-FPD003)。
文摘Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics in this region remains unexplored at finer scales.This study investigates how micro site conditions influence litter decomposition of 15 upper Andean species over time.A reciprocal translocation field experiment was conducted over 18 months in 14 permanent plots within four sites in Colombian Andean mountain forests.Each plot contained three litterbeds(microsites),each with the 15 species,harvested at 3,6,12 and 18 months,totaling 2520 litterbags.Different forest variables,including canopy openness,leaf area index,slope and depth of litter,were measured in each litterbed.ANOVAs and linear mixed models were used to assess variation between sites and plots respectively,while multiple linear regression analyses evaluated the effects of forest variables on decay rates over time at the micro site scale.Results showed differences in absolute decay rates between sites but consistent relative decay rates,indicating varying magnitudes of decomposition,yet maintaining the same order based on their litter quality.Decay rates varied between species,with more variation in labile species compared to recalcitrant ones.Despite substantial variation in forest characteristics within sites,their influence on litter decomposition was minimal and declined over time.This suggests that,at finer spatial scales,the forest microenvironment plays a lesser role in litter decomposition,with litter quality emerging as the primary driver.This study is a step towards understanding the fine-scale dynamics of litter decomposition in upper Andean tropical forests,highlighting the intricate interplay between microenvironmental factors and decomposition processes.
基金funded by the Global PhD Scholarship between KU Leuven and UCLouvain。
文摘Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and diversity on leaf litter decomposition in forests remains understudied.By controlling for macroclimate,soil properties,and litter substrate in a mature common garden,we investigated whether a three-month tea bag incubation of standardized green and rooibos tea substrate is driven by canopy tree species characteristics and diversity.Our study hypothesized two primary pathways:a chemical engineering effect,where trees alter soil properties and decomposer communities through litter input,and a physical engineering effect,where tree canopy structure modulates the local microclimate.The results showed that even under uniform macroclimatic and initial soil conditions,mass loss rates varied widely for green tea(27.4%–73.2%)and rooibos tea(6.1%–34.7%),comparable as found in other research between distinct biomes.While substrate quality was the dominant factor,both engineering pathways and,to a minor extent,tree diversity modulated mass losses.For green tea,tree chemical and physical characteristics seemed equally important,while the physical environment showed an increased importance for rooibos.Incubation depth played a key role,where forest floor decomposition rates are more susceptible to temporal climate variations,and soil-layer decomposition rates are less susceptible to climate variations and more determined by tree species identity.Our findings suggest that tea bag experiments focusing solely on topsoil burial may underestimate processes in the forest floor and the mineralorganic boundary layer.This study underscores the critical role of litter substrate quality in decomposition while demonstrating that tree community composition and the associated herbaceous layer,through both chemical and physical engineering pathways,strongly modulate decomposition rates.
基金supported by the National Natural Science Foundation of China(No.22278439 and 21776313).
文摘Renewable electricity-driven production of value-added sulfur and H_(2)via electrocatalytic H_(2)S decomposition represents a sustainable route to conventional thermocatalysis.Both the electrocatalyst and electrolyte solution strongly impact the H_(2)S decomposition performance.Despite significant progress in developing sophisticated electrocatalysts,a well-designed electrolyte solution in conjunction with industrial catalysts is an attractive strategy to advance the industrialization process of electrocatalytic H_(2)S decomposition,but remains unexplored.Here,for the first time,we design a solid-liquid-gas three-phase indirect electrolysis system based on a kind of CS_(2)-N electrolyte solution and Ni-Mo_(2)C that can efficiently enable H_(2)S decomposition into valuable H_(2)and sulfur.Specifically,the solid-phase Ni-Mo_(2)C as a heterogeneous redox mediator presents excellent electrocatalytic efficiency for the H_(2)S removal efficiency of up to 99%,and the formation of liquid-phase sulfur product(CS_(2)-N electrolyte solution dissolves sulfur,yield up to 95%)with the generation of gas-phase H_(2)product(~1.32 mL min^(-1)),resulting in an interesting three-phase indirect electrolysis system.Remarkably,it enables the scale-up production(~6 g in a batch experiment)of sulfur with continuous operation for 120 h without attenuation.This work may inaugurate a new electrocatalytic H_(2)S decomposition avenue to explore porous metal materials and electrolyte systems in simultaneous production of value-added sulfur and H_(2).
基金supported by State Grid Sichuan Electric Power Company science and technology project“Research on Key Technologies for Reclosing of High-Ratio New Energy Grid Connection Lines.”(Program No:52199723002Q).
文摘The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines,leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms.First,the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems,and examines the voltage characteristics of faulted phases in renewable energy scenarios.The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults,whereas permanent faults do not exhibit such variations.Building on this observation,the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition,wherein low-frequency waveform components are selected to characterize fault features.Subsequently,a fault nature identification method is introduced,based on a voltage waveform time-frequency morphology comparison.By employing a windowing technique to quantify waveform differences before and after arc extinction,this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time.Finally,a 220 kV renewable energy grid connection line model is developed using PSCAD for verification.The results demonstrate that the proposed method is highly adaptable across various fault locations,transition resistances,and renewable energy control strategies,and can reliably identify fault nature in renewable energy grid connection scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.42274035)the Major Program(JD)of Hubei Province(Grant No.2023BAA026)the Hunan Provincial Land Surveying and Mapping Project(HNGTCH-2023-05)。
文摘Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.
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.
文摘Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.
基金supported by the National Key Research & Development Program of China (No.2017YFF0207302)the National Natural Science Foundation of China (Nos.71573242 and 71273252)
文摘In this article, per capita urban carbon emissions were decomposed into manufacturing,transportation, and construction sectors using logarithmic mean Divisia index(LMDI)method. This new decomposition method can provide information about specific drivers of carbon emissions, including urban growth and resident living standards, rather than general demographic and economic factors identified by traditional methods. Using four Chinese megacities(Beijing, Tianjin, Shanghai, and Chongqing) as case studies, we analyzed the factors that influenced per capita carbon emissions from 2010 to 2015. The results showed that per capita carbon emissions increased in Tianjin and Chongqing whereas decreased in Beijing and Shanghai, and that manufacturing was a key driving force. In these four megacities,energy conservation strategies were successfully implemented despite poor energy structure optimization during 2010–2015. Development of manufacturing and improvement of resident living standards in the cities led to an increase in carbon emissions. The unique dual-core urban form of Tianjin might mitigate the increased carbon emissions caused by the transportation sector. Reductions in carbon emissions could be achieved by further optimizing energy structures, limiting the number of private cars, and controlling per capita construction.
基金This research was supported by National Natural Science Foundation of China (No.30470302&70373044)Overseas Scholar Funding from Chinese Academy of Science (C13HU&C13HK).
文摘Coarse woody debris （CWD） is an important and particular component of forest ecosystems and is extremely important to forest health. This review describes the decomposition process, decomposition model and influence factors. CWD decomposition is a complex and continuous process and characterizes many biological and physical processes, including biological respiration, leaching, and fragmentation. All these processes have closed relationships between each other and work synergistically. During decomposition, there are many controlling factors mainly including site conditions （temperature, humidity, and OJCO2concentration）, woody substrate quality （diameter, species and compound） and organism in CWD. The decomposition rate is generally expresses through a constant k which indicate the percent mass, volume or density loss over time, and can be determined by long-term monitoring, chronosequence approach and the radio between input and the total mass. Now using mathematical models to simulate decomposition patterns and estimate the decomposition rate is widely applied, especially the exponential model. We brought forward that managing and utilizing for the CWD in forest was a primary objective on all forest lands. And it is should be intensified to integrate many related research subjects and to carry a comprehensive, long-term and multi-scale research which mainly focus on seven sections.
基金supported by the National Natural Science Foundation of China(6137116961601167)+2 种基金the Jiangsu Natural Science Foundation(BK20161489)the open research fund of State Key Laboratory of Millimeter Waves,Southeast University(K201826)the Fundamental Research Funds for the Central Universities(NE2017103)
文摘This paper links parallel factor（PARAFAC） analysis to the problem of nominal direction-of-arrival（DOA） estimation for coherently distributed（CD） sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model.Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD（ESPRIT-CD） and propagator method CD（PM-CD）methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario,where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.
文摘Carbon emission is the current hot issue of global concern. How to assess various contributing factors for carbon emission is of great importance to find out the key factors and promote carbon emission reduction. In this paper, the author constructs an identical equation for carbon emission, based on the economic aggregate, the economic structure, the efficiency of energy utilization, the structure of energy consumption, and the coefficient of carbon emission; by applying to LMDI decomposition technology, the author analyzes the carbon emission of China from 1995 to 2007 at industrial level and regional level. The results show that the expansion of economic aggregate is the main reason for China' s rapidly increasing carbon emission and the increase of energy utilization efficiency is the key factor that can hold back the increase of carbon emission. In addition, the change of industrial structure or regional structure and the change of traditional energy structure have limited influence on the carbon emission, and their potentials have not yet been exploited. At the end of this paper, the author proposes the efforts that China should make to reduce carbon emission.
文摘[Objective] By decomposing and studying the relative factors of carbon emissions in Guangdong Province,the policy and suggestion on further keeping the sustainable development were put forward,which provided the reference for the carbon emission reduction in other provinces.[Method] Based on the carbon emissions formula which was put forward by Johan,three factors(the energy structure,energy efficiency and economy development) which affected the carbon emissions during 1996-2009 in Guangdong Province were studied by using Divisia decomposition method of logarithmic mean weight(LMD).[Result] The economy development was the main reason that caused the continuous significant increase of carbon emissions in Guangdong Province.The improvement of energy efficiency was the important manner for decreasing the energy consumption and the carbon emissions.The adjustment and optimization of energy consumption structure had the huge potential for reducing the carbon emissions in Guangdong Province.[Conclusion] The carbon emissions in Guangdong Province would continue to increase in the future for a long time.When formulated the development strategy in the future,it needed pay special attention to keep the accord development of economy and environment.
基金supported by the Young Scientists Fund of the Natural Science Foundation of China (Grant nos.41106165, 41106159)the Chinese Polar Environment Comprehensive Investigation & Evaluation Programmes(Grant no.CHINARE2014-04-04)+1 种基金the Project of Comprehensive Evaluation of Polar Regions on Global and Regional Climate Changes (Grant no.201105019)the National Science and Technology Support Plan of China (Grant no.2012BAC19B08)
文摘The physical decomposition method separates atmospheric variables into four parts, correlating each with solar radiation, land-sea distribution, and inter-annual and seasonal internal forcing, strengthening the anomaly signal and increasing the correlation between variables. This method was applied to the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research （NCEP/NCAR）, to study the effects of Arctic factors （Arctic oscillation （AO） and Arctic polar vortex） on wintertime temperatures in the Northern Hemisphere and China. It was fotmd that AO effects on zonal average temperature disturbance could persist for 1 month. In the AO negative phase in wintertime, the temperatures are lower in the mid-high latitudes than in normal years, but higher in low latitudes. When the polar vortex area is bigger, the zonal average temperature is lower at 50N. Influenced mainly by meridional circulation enhancement, cold air flows from high to low latitudes; thus, the temperatures in Continental Europe and the North American continent exhibit an antiphase seesaw relationship. When the AO is in negative phase and the Arctic polar vortex larger, the temperature is lower in Siberia, but higher in Greenland and the Bering Strait. Influenced by westerly troughs and ridges, the polar air disperses mainly along the tracks of atmospheric activity centers. The AO index can be considered a predictor of wintertime temperature in China. When the AO is in negative phase or the Asian polar vortex is intensified, temperatures in Northeast China and Inner Mongolia are lower, because under the influence of the Siberia High and northeast cold vortex, the cold air flows southwards.
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 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.
