镍钒水滑石电极用于可放大电催化5-羟甲基糠醛氧化耦合产氢

可再生能源驱动的电催化水裂解是获取绿氢的重要途径,但受到缓慢的阳极析氧反应(OER)限制。使用热力学有利的5-羟甲基糠醛氧化反应(HMFOR)代替OER的电解水制氢耦合氧化策略提供了一种降低能耗的有效策略,同时可以生产高附加值的有机含氧化合物,如2,5-呋喃二甲酸(FDCA)。在该领域,大量工作集中于催化剂工程以获得更好的催化活性和产物选择性。然而,很少有研究关注到5-羟甲基糠醛(HMF)的规模化氧化制备FDCA。为此,我们合成了一种镍钒水滑石(NiV-LDH)催化剂用于高效HMFOR,在1.52 V vs.RHE(可逆氢电极)下,电流密度达到100 mA·cm^(-2) FDCA的法拉第效率高达94.6%。与OER相比,HMFOR将对应的氢气生产率提高了两倍。作为概念验证,我们使用流动反应器展示了连续且可规模化的HMFOR,在10 A条件下,实现了94.8%的高HMF单程转化率和98.5%的高FDCA选择性。

Human Causes of Aeolian Desertification in Northern China

Aeolian desertification has rapidly developed in the past 50 years in Northern China,covered an area of 0.386 million km2 by 2000,affected nearly 170 million population,and caused the direct and indirect economic loss of about $6.75(U.S.dollar) billion per year.Here we present several lines of evidence to demonstrate that human activities guided by policy shifts have been a major force to drive aeolian desertification via changes in land-use patterns and intensity.It is suggested that the desertification can be curbed or even reversed by adopting prevention and control measures with ecologically sound land-use practices in China.

Research progress of viral sepsis:etiology,pathophysiology,diagnosis,and treatment

Sepsis is a common systemic disease characterized by various physiological and pathological disorders.It can result from infection by various pathogens,such as bacteria,viruses,and fungi.The rate of culture-negative sepsis is almost 42%,indicating that most patients may have nonbacterial infections.With the outbreak of coronavirus disease 2019,viral sepsis has attracted growing attention because many critically ill patients develop sepsis.Viral sepsis can be caused by viral infections and combined with,or secondary to,bacterial infections.Understanding the common types of viral sepsis and the main characteristics of its pathogenesis will be helpful for effective diagnosis and treatment,thereby reducing mortality.Early identification of the causative agent of viral sepsis can help reduce the overuse of broad-spectrum antibiotics.In this article,we reviewed the common viruses of sepsis,their potential pathophysiology,targets of diagnosis,and remedies for viral sepsis.

中国森林生态系统碳周转时间的空间格局

森林生态系统持续的碳吸收能力在很大程度上取决于净初级生产力(NPP)的增长趋势及生态系统碳周转的时间,因此,获取生态系统碳周转时间的空间分布格局是有效评估生态系统碳汇潜力的基础.本研究采用数据-模型融合方法,基于区域生态系统碳循环过程模型(TECO-R),结合生态系统观测数据(NPP、生物量及土壤有机碳)、遥感数据(NDVI)及气象、植被与土壤等辅助空间数据,利用遗传算法(Genetic Algorithm)反演了中国森林生态系统各碳库的周转时间及分配系数,并在此基础上估算了平衡状态下森林生态系统碳周转时间的空间分布格局.研究结果表明:数据-模型融合技术能有效地反演中国森林生态系统碳循环过程模型中的关键参数,从而很好地模拟中国森林生态系统的碳循环过程;反演的中国森林生态系统的碳周转时间在空间上存在很大的异质性,其值大多介于24～70年之间;不同森林类型的统计结果表明,落叶针叶林与常绿针叶林的平均周转时间最大(分别为73.8与71.3年),其次是混交林与落叶阔叶林(38.1与37.3年),而常绿阔叶林的值最小(31.7年);从全国尺度看,中国各种森林生态系统总的碳周转时间的均值为57.8年.

Rates of litter decomposition in terrestrial ecosystems:global patterns and controlling factors

Aims We aim to construct a comprehensive global database of litter decomposition rate(k value)estimated by surface floor litterbags,and investigate the direct and indirect effects of impact factors such as geographic factors(latitude and altitude),climatic factors(mean annual tempePlrature,MAT;mean annual precipitation,MAP)and litter quality factors(the contents of N,P,K,Ca,Mg and C:N ratio,lignin:N ratio)on litter decomposition.Methods We compiled a large data set of litter decomposition rates(k values)from 110 research sites and conducted simple,multiple regression and path analyses to explore the relationship between the k values and impact factors at the global scale.Important findings The k values tended to decrease with latitude(LAT)and lignin content(LIGN)of litter but increased with temperature,precipitation and nutrient concentrations at the large spatial scale.Single factor such as climate,litter quality and geographic variable could not explain litter decomposition rates well.However,the combination of total nutrient(TN)elements and C:N accounted for 70.2%of the variation in the litter decomposition rates.The combination of LAT,MAT,C:N and TN accounted for 87.54%of the variation in the litter decomposition rates.These results indicate that litter quality is the most important direct regulator of litter decomposition at the global scale.This data synthesis revealed significant relationships between litter decomposition rates and the combination of climatic factor(MAT)and litter quality(C:N,TN).The global-scale empirical relationships developed here are useful for a better understanding and modeling of the effects of litter quality and climatic factors on litter decomposition rates.

陆地碳循环的动态非平衡假说

生态系统维持物质与能量的动态平衡是地球系统孕育与维持生物多样性的重要基础。自工业革命以来,人类活动导致陆地生态系统的碳循环转变为动态非平衡,进而使陆地生态系统的结构与功能出现许多难以预测的变化动态。本文阐释了陆地生态系统碳循环的动态非平衡假说。该假说构建于陆地碳循环内部过程的四点基本特征和五类外部驱动因素。基于这些内部特征与外部因素,本文归纳了陆地生态系统碳循环动态非平衡在不同时间与空间尺度的表达现象,并从观测、实验与模型的角度讨论了其检测方法。陆地生态系统碳循环的动态非平衡假说不仅有助于我们理解复杂的陆地碳循环现象,也为预测未来陆地碳汇动态提供了新的理论框架。

Dynamic carbon-nitrogen coupling under global change

Carbon-nitrogen coupling is a fundamental principle in ecosystem ecology.However,how the coupling responds to global change has not yet been examined.Through a comprehensive and systematic literature review,we assessed how the dynamics of carbon processes change with increasing nitrogen input and how nitrogen processes change with increasing carbon input under global change.Our review shows that nitrogen input to the ecosystem mostly stimulates plant primary productivity but inconsistently decreases microbial activities or increases soil carbon sequestration,with nitrogen leaching and nitrogenous gas emission rapidly increasing.Nitrogen fixation increases and nitrogen leaching decreases to improve soil nitrogen availability and support plant growth and ecosystem carbon sequestration under elevated CO_(2)and temperature or along ecosystem succession.We conclude that soil nitrogen cycle processes continually adjust to change in response to either overload under nitrogen addition or deficiency under CO_(2)enrichment and ecosystem succession to couple with carbon cycling.Indeed,processes of both carbon and nitrogen cycles continually adjust under global change,leading to dynamic coupling in carbon and nitrogen cycles.The dynamic coupling framework reconciles previous debates on the“uncoupling”or“decoupling”of ecosystem carbon and nitrogen cycles under global change.Ecosystem models failing to simulate these dynamic adjustments cannot simulate carbonnitrogen coupling nor predict ecosystem carbon sequestration well.

Conditional inversion to estimate parameters from eddy-flux observations

Aims Data assimilation is a useful tool to extract information from large datasets of the net ecosystem exchange(NEE)of CO_(2) obtained by eddy-flux measurements.However,the number of parameters in ecosystem models that can be constrained by eddy-flux data is limited by conventional inverse analysis that estimates parameter values based on one-time inversion.This study aimed to improve data assimilation to increase the number of constrained parameters.Methods In this study,we developed conditional Bayesian inversion to maximize the number of parameters to be constrained by NEE data in several steps.In each step,we conducted a Bayesian inversion to constrain parameters.The maximum likelihood estimates of the constrained parameters were then used as prior to fix parameter values in the next step of inversion.The conditional inversion was repeated until there were no more parameters that could be further constrained.We applied the conditional inversion to hourly NEE data from Harvard Forest with a physiologically based ecosystem model.Important Findings Results showed that the conventional inversion method constrained 6 of 16 parameters in the model while the conditional inversion method constrained 13 parameters after six steps.The cost function that indicates mismatch between the modeled and observed data decreased with each step of conditional Bayesian inversion.The Bayesian information criterion also decreased,suggesting reduced information loss with each step of conditional Bayesian inversion.A wavelet analysis reflected that model performance under conditional Bayesian inversion was better than that under conventional inversion at multiple time scales,except for seasonal and half-yearly scales.In addition,our analysis also demonstrated that parameter convergence in a subsequent step of the conditional inversion depended on correlations with the parameters constrained in a previous step.Overall,the conditional Bayesian inversion substantially increased the number of parameters to be constrained by NEE data and can be a powerful tool to be used in data assimilation in ecology.

Divergent apparent temperature sensitivity of terrestrial ecosystem respiration

Aims Recent studies revealed convergent temperature sensitivity of ecosys-tem respiration(Re)within aquatic ecosystems and between terrestrial and aquatic ecosystems.We do not know yet whether various terres-trial ecosystems have consistent or divergent temperature sensitivity.Here,we synthesized 163 eddy covariance flux sites across the world and examined the global variation of the apparent activation energy(Ea),which characterizes the apparent temperature sensitivity of and its interannual variability(IAV)as well as their controlling factors.Methods We used carbon fluxes and meteorological data across FLUXNET sites to calculate mean annual temperature,tempera-ture range,precipitation,global radiation,potential radiation,gross primary productivity and Re by averaging the daily values over the years in each site.Furthermore,we analyzed the sites with>8 years data to examine the IAV of Ea and calculated the standard deviation of Ea across years at each site to character-ize IAV.Important Findings The results showed a widely global variation of Ea,with significantly lower values in the tropical and subtropical areas than in temperate and boreal areas,and significantly higher values in grasslands and wetlands than that in deciduous broadleaf forests and evergreen for-ests.Globally,spatial variations of Ea were explained by changes in temperature and an index of water availability with differing contribution of each explaining variable among climate zones and biomes.IAV and the corresponding coefficient of variation of Ea decreased with increasing latitude,but increased with radiation and corresponding mean annual temperature.The revealed patterns in the spatial and temporal variations of Ea and its controlling factors indicate divergent temperature sensitivity of Re,which could help to improve our predictive understanding of Re in response to climate change.

Allocation strategies of carbon,nitrogen and phosphorus following a gradient of wildfire severities

Wildfire is crucial in the regulation of nutrient allocation during the succession of boreal forests.However,the allocation strategies of carbon(C),nitrogen(N)and phosphorus(P)between leaves and fine roots in response to wildfire severities remain poorly studied.We aimed to explore the allocation strategies of C,N and P between leaves and fine roots among different fire severities.We selected four wildfire severities(unburned,low,moderate and high severity)after 10 years recovery in the Great Xing’an Mountains,northeast China,and compared C,N and P concentrations in leaves and fine roots of all species among fire severities using stoichiometry theory and allometric growth equations.Compared with unburned treatment,C concentrations in leaves and fine roots increased at low severity,and leaf N concentration was the greatest at high severity,but the lowest fine root N concentration occurred at high severity.Plant nutrient utilization tended to be P-limited at high fire severity according to the mean value of N:P ratio>16.More importantly,C,N and P allocation strategies between fine roots and leaves changed from allometry to isometry with increasing fire severities,which showed more elements allocated to leaves than to fine roots with increasing fire severities.These changes in patterns suggest that the allocation strategies of elements between leaves and fine roots are of imbalance with the wildfire severity.This study deepens our understanding of nutrient dynamics between plant and soil in ecosystem succession.

Uncertainty analysis of forest carbon sink forecast with varying measurement errors:a data assimilation approach

Aims Accurate forecast of ecosystem states is critical for improving natural resourcemanagement and climate change mitigation.Assimilating observed data into models is an effective way to reduce uncertainties in ecological forecasting.However,influences ofmeasurement errors on parameter estimation and forecasted state changes have not been carefully examined.This study analyzed the parameter identifiability of a process-based ecosystem carbon cycle model,the sensitivity of parameter estimates and model forecasts to the magnitudes of measurement errors and the information contributions of the assimilated data to model forecasts with a data assimilation approach.Methods We applied a Markov Chain Monte Carlo method to assimilate eight biometric data sets into the Terrestrial ECOsystemmodel.The data were the observations of foliage biomass,wood biomass,fine root biomass,microbial biomass,litter fall,litter,soil carbon and soil respiration,collected at the Duke Forest free-air CO_(2)enrichment facilities from 1996 to 2005.Three levels ofmeasurement errorswere assigned to these data sets by halving and doubling their original standard deviations.Important Findings Results showed that only less than half of the 30 parameters could be constrained,though the observations were extensive and themodelwas relatively simple.Highermeasurement errors led to higher uncertainties in parameters estimates and forecasted carbon(C)pool sizes.The longterm predictions of the slow turnover pools were affected less by the measurement errors than those of fast turnover pools.Assimilated data contributed less information for the pools with long residence times in long-term forecasts.These results indicate the residence times of C pools played a key role in regulating propagation of errors from measurements to model forecasts in a data assimilation system.Improving the estimation of parameters of slowturnover C pools is the key to better forecast long-term ecosystem C dynamics.

nvasive Spartina and reduced sediments: Shanghai’s dangerous silver bullet

Aims This synthesis paper is developed to provide a summary of ecological,socioeconomic challenges facing the estuarine wetlands within the Yangtze River delta.Methods We combined literature review of the estuarine wetlands and ground measurements of sedimentation,vegetation,and carbon fluxes to illustrate the foreseeable crises in managing these wetlands that play a critical role in Shanghai’s urban development.Where the Yangtze River meets the Pacific Ocean,4.153108 mg/year of suspended sediments are deposited along mainland and island shorelines of the 40000 km2 delta-resulting in an average growth rate of land outwards 64 m/year since 1951.However,completion of the Three Gorges Dam in 2003,and earlier dam projects,reduced the rates of sedimentation and growth of the islands.To meet the increasing demands for lands and agriculture,policymakers have attempted to enlarge the islands by diking coastal areas and introducing Spartina alterniflora-a grass native to tidal salt marshes of the southeastern USA but exotic to China.Spartina is one of the 16 greatest invasive species listed by the State Environmental Protection Administration of China.Successful plantations and rapid spread of this species have increased the production and fertility of the coast,but at the cost of native ecosystems.We outline the social,economic,and ecological controversies related to this land management strategy in the context of global warming.Important findings Combinations of these changes,including sea level rise,and alterations to storm patterns and long-shore currents,with the continued spread of Spartina,human population growth,and river flow and sediment reduction will make current management untenable.

Inverse analysis of coupled carbon-nitrogen cycles against multiple datasets at ambient and elevated CO_(2)

Aims Carbon(C)sequestration in terrestrial ecosystems is strongly regulated by nitrogen(N)processes.However,key parameters that determine the degree of N regulation on terrestrial C sequestration have not been well quantified.Methods Here,we used a Bayesian probabilistic inversion approach to estimate 14 target parameters related to ecosystem C and N interactions from 19 datasets obtained from Duke Forests under ambient and elevated carbon dioxide(CO_(2)).Important FindingsOur results indicated that 8 of the 14 target parameters,such as C:N ratios in most ecosystem compartments,plant N uptake and external N input,were well constrained by available datasets whereas the others,such as N allocation coefficients,N loss and the initial value of mineral N pool were poorly constrained.Our analysis showed that elevated CO_(2)led to the increases in C:N ratios in foliage,fine roots and litter.Moreover,elevated CO_(2)stimulated plant N uptake and increased ecosystem N capital in Duke Forests by 25.2 and 8.5%,respectively.In addition,elevated CO_(2)resulted in the decrease of C exit rates(i.e.increases in C residence times)in foliage,woody biomass,structural litter and passive soil organic matter,but the increase of C exit rate in fine roots.Our results demonstrated that CO_(2)enrichment substantially altered key parameters in determining terrestrial C and N interactions,which have profound implications for model improvement and predictions of future C sequestration in terrestrial ecosystems in response to global change.

Anisotropic Cu@Cu-BTC core-shell nanostructure for memory device

The activity and stability of Cu nanostructures strongly depend on their sizes,morphology and structures.Here we report the preparation of two-dimensional(2 D)Cu@Cu-BTC core-shell nanosheets(NSs).The thickness of the Cu NSs could be tuned to sub-10 nm through a mild etching process,in which the Cu-BTC in situ grow along with the oxidation on the surface of the Cu NSs.This unique strategy can also be extended to synthesize one-dimensional(1 D)Cu@Cu-BTC nanowires(NWs).Furthermore,the obtained Cu@Cu-BTC NSs could be applied as an effective material to the memory device with the write-onceread-many times(WORM)behavior and the high ION/I(OFF)ratio(>2.7×103).

A traceability analysis system for model evaluation on land carbon dynamics: design and applications

Background:An increasing number of ecological processes have been incorporated into Earth system models.However,model evaluations usually lag behind the fast development of models,leading to a pervasive simulation uncertainty in key ecological processes,especially the terrestrial carbon(C)cycle.Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models.Thus,a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.Methods:A new cloud-based model evaluation platform,i.e.,the online traceability analysis system for model evaluation(TraceME v1.0),was established.The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project(CMIP6).Results:The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models.For example,the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models.Among all models,IPSL-CM6A-LR simulated the lowest land C storage,which mainly resulted from its shortest baseline C residence time.Over the historical period of 1850–2014,gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.Conclusion:TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.

Evapotranspiration partitioning using an optimality-based ecohydrological model in a semiarid shrubland

Partitioning of evapotranspiration(ET)into biological component transpiration(T)and non-biological component evaporation(E)is crucial in understanding the impact of environmental change on ecosystems and water resources.However,direct measurement of transpiration is still challenging.In this paper,an optimality-based ecohydrological model named Vegetation Optimality Model(VOM)is applied for ET partitioning.The results show that VOM model can reasonably simulate ET and ET components in a semiarid shrubland.Overall,the ratio of transpiration to evapotranspiration is 49%for the whole period.Evaporation and plant transpiration mainly occur in monsoon following the precipitation events.Evaporation responds immediately to precipitation events,while transpiration shows a lagged response of several days to those events.Different years demonstrate different patterns of T/ET ratio dynamic in monsoon.Some of the years show a low T/ET ratio at the beginning of monsoon and slowly increased T/ET ratio.Other years show a high level of T/ET ratio for the whole monsoon.We find out that spring precipitation,especially the size of the precipitation,has a significant influence on the T/ET ratio in monsoon.

Climate warming restructures seasonal dynamics of grassland soil microbial communities

Soil microbial community's responses to climate warming alter the global carbon cycle.In temperate ecosystems,soil microbial communities function along seasonal cycles.However,little is known about how the responses of soil microbial communities to warming vary when the season changes.In this study,we investigated the seasonal dynamics of soil bacterial community under experimental warming in a temperate tall‐grass prairie ecosystem.Our results showed that warming significantly(p=0.001)shifted community structure,such that the differences of microbial communities between warming and control plots increased nonlinearly(R^(2)=0.578,p=0.021)from spring to winter.Also,warming significantly(p<0.050)increased microbial network complexity and robustness,especially during the colder seasons,despite large variations in network size and complexity in different seasons.In addition,the relative importance of stochastic processes in shaping the microbial community decreased by warming in fall and winter but not in spring and summer.Our study indicates that climate warming restructures the seasonal dynamics of soil microbial community in a temperate ecosystem.Such seasonality of microbial responses to warming may enlarge over time and could have significant impacts on the terrestrial carbon cycle.

Country-level land carbon sink and its causing components by the middle of the twenty-first century

Background:Countries have long been making efforts by reducing greenhouse-gas emissions to mitigate climate change.In the agreements of the United Nations Framework Convention on Climate Change,involved countries have committed to reduction targets.However,carbon(C)sink and its involving processes by natural ecosystems remain difficult to quantify.Methods:Using a transient traceability framework,we estimated country-level land C sink and its causing components by 2050 simulated by 12 Earth System Models involved in the Coupled Model Intercomparison Project Phase 5(CMIP5)under RCP8.5.Results:The top 20 countries with highest C sink have the potential to sequester 62 Pg C in total,among which,Russia,Canada,USA,China,and Brazil sequester the most.This C sink consists of four components:productiondriven change,turnover-driven change,change in instantaneous C storage potential,and interaction between production-driven change and turnover-driven change.The four components account for 49.5%,28.1%,14.5%,and 7.9%of the land C sink,respectively.Conclusion:The model-based estimates highlight that land C sink potentially offsets a substantial proportion of greenhouse-gas emissions,especially for countries where net primary production(NPP)likely increases substantially and inherent residence time elongates.

The response of ecosystem carbon and nitrogen pools to experimental warming in grasslands: a meta-analysis

Carbon(C)and nitrogen(N)coupling processes in terrestrial ecosystems have the potential to modify the sensitivity of the global C cycle to climate change.But the degree to which C–N interactions contribute to the sequestration of terrestrial ecosystem C(C_(seq)),both now and in the future,remains uncertain.In this study,we used a meta-analysis to quantitatively synthesize C and N responses from feld experiments on grasslands subjected to simulated warming and assessed the relative importance of three properties(changes in ecosystem N amount,redistribution of N among soil,litter and vegetation,and modifcations in the C:N ratio)associated with grassland C_(seq) in response to warming.Warming increased soil,litter and vegetation C:N ratios and approximately 2%of N shifted from the soil to vegetation and litter.Warming-induced grassland C_(seq) was the result of the net balance between increases in vegetation and litter C(111.2 g·m^(−2))and decreases in soil C(30.0 g·m^(−2)).Warming-induced accumulation of C stocks in grassland ecosystems indicated that the three processes examined were the main contributors to C_(seq),with the changes in C:N ratios in soil,litter and vegetation as the major contributors,followed by N redistribution,whilst a decrease in total N had a negative effect on C_(seq).These results indicate that elevated temperatures have a signifcant infuence on grassland C and N stocks and their coupling processes,suggesting that ecological models need to include C–N interactions for more accurate predictions of future terrestrial C storage.

Experimental warming shifts coupling of carbon and nitrogen cycles in an alpine meadow

Aims Terrestrial ecosystem carbon(C)uptake is remarkably regulated by nitrogen(N)availability in the soil.However,the coupling of C and N cycles,as reflected by C:N ratios in different components,has not been well explored in response to climate change.Methods Here,we applied a data assimilation approach to assimilate 14 datasets collected from a warming experiment in an alpine meadow in China into a grassland ecosystem model.We attempted to evaluate how experimental warming affects C and N coupling as indicated by constrained parameters under ambient and warming treatments separately.Important Findings The results showed that warming increased soil N availability with decreased C:N ratio in soil labile C pool,leading to an increase in N uptake by plants.Nonetheless,C input to leaf increased more than N,leading to an increase and a decrease in the C:N ratio in leaf and root,respectively.Litter C:N ratio was decreased due to the increased N immobilization under high soil N availability or warming-accelerated decomposition of litter mass.Warming also increased C:N ratio of slow soil organic matter pool,suggesting a greater soil C sequestration potential.As most models usually use a fixed C:N ratio across different environments,the divergent shifts of C:N ratios under climate warming detected in this study could provide a useful benchmark for model parameterization and benefit models to predict C-N coupled responses to future climate change.