Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

The coupling control of biological precursors and environmental factors onβ-carotane enrichment in alkaline lacustrine source rocks:A case study from the Fengcheng formation in the western Junggar Basin,NW China

The organic-rich mudstones and dolostones of the Permian Fengcheng Formation(Fm.)are typically alkaline lacustrine source rocks,which are typified by impressively abundantβ-carotane.Abundant β-carotane has been well acknowledged as an effective indicator of biological sources or depositional environments.However,the specific biological sources of β-carotane and the coupling control of biological sources and environmental factors on the enrichment of β-carotane in the Fengcheng Fm.remains obscure.Based on a comprehensive investigation of the bulk,molecular geochemistry,and organic petrology of sedimentary rocks and the biochemistry of phytoplankton in modern alkaline lakes,we proposed a new understanding of the biological precursors of β-carotane and elucidated the enrichment mechanism of β-carotane in the Fengcheng Fm.The results show that the biological precursors crucially control the enrichment of β-carotane in the Fengcheng Fm.The haloalkaliphilic cyanobacteria are the primary biological sources of β-carotane,which is suggested by a good positive correlation between the 2-methylhopane index,7-+8-methyl heptadecanes/C_(max),C_(29%),and β-carotane/C_(max)in sedimentary rocks and the predominance of cyanobacteria with abundantβ-carotene in modern alkaline lakes.The enrichment of β-carotane requires the reducing condition,and the paleoredox state that affects the enrichment of β-carotane appears to have a threshold.The paleoclimate conditions do not considerably impact the enrichment of β-carotane,but they have some influence on the water's paleosalinity by affecting evaporation and precipitation.While it does not directly affect the enrichment of β-carotane in the Fengcheng Fm.,paleosalinity does have an impact on the cyanobacterial precursor supply and the preservation conditions.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

The occurrence phases and enrichment mechanism of rare earth elements in cobalt-rich crusts from Marcus-Wake Seamounts

To explore the occurrence phases and enrichment mechanism of rare earth elements(REEs)in cobalt-rich crusts,this study analyzes the mineral composition and REE contents of the samples from Marcus-Wake Seamounts by XRD,ICP-OES and ICP-MS.The results show that,(1)the cobalt-rich crusts contain the major crystalline mineral(vernadite),the secondary minerals(quartz,plagioclase and carbonate fluorapatite),and a large amount of amorphous ferric oxyhydroxides(FeOOH).(2)The cobalt-rich crusts contains higher Mn(10.83%to 28.76%)and Fe(6.14%to 18.86%)relative to other elements,and are enriched in REEs,with total REE contents of 1563−3238μg/g and Ce contents of 790−1722μg/g.Rare earth element contents of the old crusts are higher than those of the new crusts.Moreover,the non-phosphatized crusts have positive Ce and negative Y anomalies,and yet the phosphatized crusts have positive Ce and positive Y anomalies,indicating that cobalt-rich crusts is hydrogenetic and REEs mainly come from seawater.(3)Analytical data also show that the occurrence phases of elements in cobalt-rich crusts are closely related to their mineral phases.In the non-phosphatized crusts,REEs are adsorbed by colloidal particles into the crusts(about 67%of REEs in the Fe oxide phase,and about 17%of REEs in the Mn oxide phase).In contrast,in the phosphatized crusts(affected by the phosphatization),REEs may combine with phosphate to form rare earth phosphate minerals,and about 64%of REEs are enriched in the residual phase containing carbonate fluorapatite,but correspondingly the influence of Fe and Mn oxide phases on REEs enrichment is greatly reduced.In addition,the oxidizing environment of seawater,high marine productivity,phosphatization,and slow growth rate can promote the REE enrichment.This study provides a reference for the metallogenesis of cobalt-rich crusts in the Pacific.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Differences in and factors controlling organic matter enrichment in the Ziliujing Formation shale in the Sichuan Basin

Lacustrine shale oil and gas are important fields for unconventional exploration and development in China,and organic-rich shale deposition lays down the critical foundation for hydrocarbon generation.There are two sets of shale,the Dongyuemiao and Da’anzhai Members,in the Ziliujing Formation in the Sichuan Basin.To identify the differential enrichment characteristics of organic matter and clarify its controlling factors,geochemical analyses of organic and inorganic geochemical analyses were performed.The results showed that the total organic carbon content of the Dongyuemiao shale(1.36%)is slightly higher than that of the Da’anzhai shale(0.95%).The enrichment of organic matter in the two shales resulted from the comprehensive controls of paleoproductivity,paleoenvironment,and terrigenous input,but different factors have different effects.In addition,driven by climate,the change in the sulfate concentration in the bottom water further led to the different intensities of bacterial sulfate reduction in early diagenesis.This made a great difference regarding organic matter accumulation in the two members.In general,climate may have played a dominant role in organic matter enrichment in the two sets of shale.

Paleoenvironmental Evolution and Organic Matter Enrichment Genesis of the Late Turonian Black Shale in the Southern Songliao Basin,NE China

The Upper Cretaceous Qingshankou Formation black shales,deposited in the late Turonian(LTB shales),are the main source rocks of the Songliao Basin.The origins of organic matter enrichment of the shales is a contentious subject fuelling many ongoing debates.This study investigates the genesis of the organic matter-rich shale by using molecular geochemistry.The LTB shales can be divided into three sections.The SectionⅠshales were deposited in saline,stratified and anoxic water conditions,which are related to seawater incursion events.At least three episodic and periodic seawater incursion events were recognized during SectionⅠshale deposition.The SectionⅡshales deposited in brackish to fresh and deep lake-level conditions with high primary productivity,which are related to lake-level transgression.The SectionⅢshales were deposited under fresh and slightly oxidized water conditions,which are related to lake-level regression.Two organic matter enrichment models for the LTB shales are identified,that is,the seawater incursion model and the maximum lake-level transgression sedimentation model,which act on different shale sections,both playing significant roles in the enrichment of organic matter.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Sedimentary paleoenvironment and its control on organic matter enrichment in the Mesoproterozoic hydrocarbon source rocks in the Ordos Basin,southern margin of the North China Craton

The black shale of the Mesoproterozoic Cuizhuang Formation in the Changcheng System in Yongji city,North China Craton,is a potential source rock.Understanding the organic matter enrichment mechanism is crucial for evaluating source rock resources and understanding oil and gas accumulation mechanisms.In this study,we evaluated the sedimentary paleoenvironment and organic matter enrichment mechanisms of shale using thin section observations,mineral composition analysis,organic geochemistry,and elemental geochemistry.We found significant differences in the sedimentary paleoenvironment and organic matter enrichment mechanisms between the lower Cuizhuang Formation and the Beidajian Formation shale.The Cuizhuang Formation was deposited in a late-stage,restricted basin environment during the rift phase,and elemental and geochemical indicators showed that the Cuizhuang Formation was in a suboxic-anoxic water environment,that was influenced by a warm and humid paleoclimate and submarine hydrothermal activities,which promoted the accumulation of organic matter.However,the enrichment of organic matter in the Cuizhuang Formation was mainly controlled by redox conditions.The formation of suboxic-anoxic water environments may be closely related to the warm and humid paleoclimate and submarine hydrothermal activities.Warm conditions promote continental weathering and increase marine productivity,thereby consuming oxygen in the bottom water.Moreover,acidic hydrothermal activity also helps to establish an anoxic environment.Our results reveal the effects controlling various coupled mechanisms dominated by redox conditions,which may explain the development of source rocks in the Cuizhuang Formation.

The abundance,distribution,and enrichment mechanism of harmful trace elements in coals from Guizhou,Southwestern China

Coal seams can enrich a variety of harmful trace elements under specific geological conditions.The spatial distribution of harmful trace elements in coal is extremely uneven,and the distribution characteristics of each element content are different.The harmful elements released in the process of coal mining and utilization will cause serious harm to the environment and the human body.It is of great resource significance to study the geochemistry of coal that affects the enrichment and distribution characteristics of harmful trace elements.Based on the domestic and foreign literature on coal geochemistry in Guizhou published by previous investigators,this study counted 1097 sample data from 23 major coal-producing counties in Guizhou Province,systematically summarized the relevant research results of harmful trace elements in the coal of Guizhou,and revealed the overall distribution and enrichment characteristics of harmful trace elements in the coal of Guizhou.The results show that the average contents of Cd,Pb,Se,Cu,Mo,U,V,As,Hg,and Cr in coal of Guizhou are higher than those in Chinese coal and world coal.A variety of harmful trace elements in the coal of Guizhou have high background values,especially in Liupanshui,Xingyi and Qianbei coalfield.The enrichment of various harmful trace elements in the Late Permian coal in Guizhou is mainly related to the combined action of various geological and geochemical factors.The supply of terrigenous debris and sedimentary environment may be the basic background of the enrichment of harmful elements in western Guizhou,while low-temperature hydrothermal activity and volcanic ash deposition may be the main reasons for the enrichment of harmful elements in southwestern Guizhou.

Conditions for the enrichment of karst hydrothermal resources in Bohai Bay Basin

Drilling for karst hydrothermal resources in eastern China has posed challenges,including disparities between the temperature and yield of geothermal water.It is evident that relying solely on geothermal anomalies or indications of karst reservoirs is inadequate for the exploration of karst hydrothermal resources.This study seeks to elucidate the cause of geothermal sweet spots by analyzing the interplay between geothermal anomalies and karst reservoirs and the underlying geological conditions for karst hydrothermal enrichment.Key findings include:(1)the Bohai Bay Basin has been geologically favorable for the development of karst hydrothermal resources since the Mesozoic era;(2)the karst hydrothermal enrichment varies significantly between the basin’s margin and its interior.On the basin margin,the enrichment is largely driven by groundwater activity and faults,particularly where faults facilitate the upwelling of geothermal water.In contrast,within the basin’s interior,karst hydrothermal resources are predominantly influenced by buried hills and are especially enriched in areas facilitating the discharge of deep geothermal waters.

Environmental enrichment in combination with Bifidobacterium breve HNXY26M4 intervention amplifies neuroprotective benefits in a mouse model of Alzheimer's disease by modulating glutamine metabolism of the gut microbiome

The gut microbiota-brain axis has emerged as a novel target for Alzheimer's disease(AD),a neurodegenerative disease characterised by behavioural and cognitive impairment.However,most previous microbiome-based intervention studies have focused on single factors and yielded only modest cognitive improvements.Here,we proposed a multidomain intervention strategy that combined Bifidobacterium breve treatment with environmental enrichment(EE)training.In this study,we found that compared with EE or B.breve treatment alone,B.breve intervention combined with EE amplified its neuroprotective effects on AD mice,as reflected by improved cognition,inhibited neuroinflammation and enhanced synaptic function.Moreover,using microbiome and metabolome profiling,we found that the combination of B.breve and EE treatment restored AD-related gut microbiota dysbiosis and reversed microbial metabolite changes.Finally,by integrating behavioural and neurological data with metabolomic profiles,we revealed that the underlying mechanism may involve the modulation of microbiota-derived glutamine metabolism via gut-brain interactions.Collectively,combined B.breve intervention with EE treatment can alleviate AD-related cognitive impairment and improve brain function by regulating glutamine metabolism of the gut microbiome.Our findings provide a promising multidomain intervention strategy,with a combination of dietary microbiome-based and lifestyle-targeted interventions,to promote brain function and delay the progression of AD.

Large-Scale Carbon Dioxide Storage in Salt Caverns:Evaluation of Operation,Safety,and Potential in China

Underground salt cavern CO_(2) storage(SCCS)offers the dual benefits of enabling extensive CO_(2) storage and facilitating the utilization of CO_(2) resources while contributing the regulation of the carbon market.Its economic and operational advantages over traditional carbon capture,utilization,and storage(CCUS)projects make SCCS a more cost-effective and flexible option.Despite the widespread use of salt caverns for storing various substances,differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness,carbon injection,brine extraction control,long-term carbon storage stability,and site selection criteria.These distinctions stem from the unique phase change characteristics of CO_(2) and the application scenarios of SCCS.Therefore,targeted and forward-looking scientific research on SCCS is imperative.This paper introduces the implementation principles and application scenarios of SCCS,emphasizing its connections with carbon emissions,carbon utilization,and renewable energy peak shaving.It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods,and addresses associated scientific challenges.In this paper,we establish a pressure equation for carbon injection and brine extraction,that considers the phase change characteristics of CO_(2),and we analyze the pressure during carbon injection.By comparing the viscosities of CO_(2) and other gases,SCCS’s excellent sealing performance is demonstrated.Building on this,we develop a long-term stability evaluation model and associated indices,which analyze the impact of the injection speed and minimum operating pressure on stability.Field countermeasures to ensure stability are proposed.Site selection criteria for SCCS are established,preliminary salt mine sites suitable for SCCS are identified in China,and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons,utilizing only 20%-30%volume of abandoned salt caverns.This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters,such as the operating pressure,burial depth,and storage scale,and it offers essential guidance for implementing SCCS projects in China.

Hydrocarbon Generation Potential and Organic Matter Enrichment Mechanism of the Cambrian Marine Shale in the Tadong Low Uplift,Tarim Basin

Cambrian shales in China and elsewhere contain abundant oil and gas resources.However,due to its deep burial and limited outcrop,there has been relatively little research conducted on it.The Cambrian shale of the Tadong low uplift in the Tarim Basin of western China,specifically the Xidashan-Xishanbulake Formation(Fm.)and overlying Moheershan Fm.provide a case study through the use of organic petrology,mineralogy,organic and elemental geochemistry,with the aim of analyzing and exploring the hydrocarbon generation potential(PG)and organic matter(OM)enrichment mechanisms within these shale formations.The results indicate that:(1)the Cambrian shale of the Tadong low uplift exhibits relatively dispersed OM that consists of vitrinite-like macerals and solid bitumen.These formations have a higher content of quartz and are primarily composed of silica-based lithology;(2)shale samples from the Xidashan-Xishanbulake and Moheershan formations demonstrate high total organic carbon(TOC)and low pyrolytic hydrocarbon content(S_(2))content.The OM is predominantly typeⅠand typeⅡkerogens,indicating a high level of maturation in the wet gas period.These shales have undergone extensive hydrocarbon generation,showing characteristics of relatively poor PG;(3)the sedimentary environments of the Xidashan-Xishanbulake and Moheershan formations in the Tadong low uplift are similar.They were deposited in warm and humid climatic conditions,in oxygen-deficient environments,with stable terrigenous inputs,high paleoproductivity,high paleosalinity,weak water-holding capacity,and no significant hydrothermal activity;and(4)the relationship between TOC and the paleoproductivity parameter(P/Ti)is most significant in the Lower Cambrian Xidashan-Xishanbulake Fm.,whereas correlation with other indicators is not evident.This suggests a productivity-driven OM enrichment model,where input of landderived material was relatively small during the Middle Cambrian,and the ancient water exhibited lower salinity.A comprehensive pattern was formed under the combined control of paleoproductivity and preservation conditions.This study provides valuable guidance for oil and gas exploration in the Tarim Basin.

Factorized Smith Method for A Class of High-Ranked Large-Scale T-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked T-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.

FCF-LDH/BiVO_(4)with synergistic effect of physical enrichment and chemical adsorption for efficient reduction of nitrate

Photoelectrochemical NO_(3)^(-)reduction(PEC NITRR)not only provides a promising solution for promoting the global nitrogen cycle,but also converts NO_(3)^(-)to the important chemicals(NH_(3)).However,it is still a great challenge to prepare catalysts with excellent NO_(3)^(-)adsorption/activation capacity to achieve high NITRR.Herein,we designed a novel Fe^(2+)~Cu^(2+)Fe^(3+)LDH/BiVO_(4)(FCF-LDH/BVO)catalyst with synergistic effect of chemical adsorption and physical enrichment.Fe^(2+)in FCF-LDH/BVO provides the rich Lewis acid sites for the adsorption of NO_(3)^(-),and the appropriate layer spacing of FCF-LDH further promotes the physical enrichment of NO_(3)^(-)in its interior,thus realizing the effective contact between NO_(3)^(-)and active sites(Fe^(2+)).FCF-LDH/BVO showed excellent NH_(3)production performance(FE_(NH_(3))=66.1%,r_(NH_(3))=13.8μg h^(-1)cm^(-2))and selectivity(FE_(NO_(2)^(-))=2.5%,r_(NO_(2)^(-))=4.9μg h^(-1)cm^(-2))in 0.5 mol L^(-1)Na_(2)SO_(4)electrolyte.In addition,FCF-LDH/BVO maintains the desirable PEC stability for six cycle experiments,showing great potential for practical application.The^(14)NO_(3)^(-)and^(15)NO_(3)^(-)isotope test provides strong evidence for further verification of the origin of N in the generated NH_(3).This LDH catalyst has a great potential in PEC removal of NO_(3)^(-)from groundwater.