Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers infl...Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysi...Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.展开更多
This paper deals with the global boundedness of a two-competing-species chemotaxis model with indirect signal production in a three-dimensional bounded domain.The current work extends prior results by ZHENG et al.(202...This paper deals with the global boundedness of a two-competing-species chemotaxis model with indirect signal production in a three-dimensional bounded domain.The current work extends prior results by ZHENG et al.(2022)who established global existence and boundedness of classical solution under the parameter constraintsµ_(1)µ_(2)a_(2)≥χ1(4+µ_(2)^(2)a _(2)^(2)),µ_(1)µ_(2)a_(1)≥χ2(4+µ_(1)^(2)a_(1)^( 2)).Our main contribution demonstrates that these technical conditions can be significantly relaxed toµ1≥5χ_(1),µ2≥5χ_(2),thereby expanding the admissible parameter space for solution boundedness.展开更多
Against the backdrop of profound restructuring in the global industrial and supply chains,data elements have emerged as a critical force driving the transformation of enterprises'new quality productive forces.To a...Against the backdrop of profound restructuring in the global industrial and supply chains,data elements have emerged as a critical force driving the transformation of enterprises'new quality productive forces.To address the ambiguity surrounding its micro-level empowerment mechanism,this paper empirically examines the impact of data elements on enterprises'new quality productive forces and its transmission channels using panel data of Chinese A-share listed firms from 2014 to 2023.The results show that data elements significantly improve the level of enterprises'new quality productive forces.Heterogeneity analysis indicates that this promotional effect is more pronounced in non-state-owned enterprises,large-scale enterprises,and low-leverage enterprises.Mechanism tests confirm that data elements empower new quality productive forces through three paths:enhancing enterprise innovation capability,improving internal operational efficiency,and promoting inter-firm collaboration efficiency.This study provides empirical evidence for understanding the micro-level empowerment logic of data elements and offers theoretical references and practical implications for advancing the deep integration of the digital economy and the real economy.展开更多
Two[FeFe]-hydrogenase compounds with 2-cyanobenzyl groups,{Fe_(2)[(SCH_(2)CH_(3))(SR)](CO)_(6)}(1 or 1′,which are the crystalline states from petroleum ether and dichloromethane solution,respectively)and{Fe_(2)[(SCH_...Two[FeFe]-hydrogenase compounds with 2-cyanobenzyl groups,{Fe_(2)[(SCH_(2)CH_(3))(SR)](CO)_(6)}(1 or 1′,which are the crystalline states from petroleum ether and dichloromethane solution,respectively)and{Fe_(2)[(SCH_(2)CH_(3))(SR)](CO)_(5)(PPh_(3))}(2)(R=2-cyanobenzyl),were synthesized and characterized by infrared spectroscopy,UV-Vis spectroscopy,single-crystal diffraction,powder X-ray diffraction,etc.Their performances as photocatalysts for H_(2)production through water splitting were evaluated.The results showed that 316.8μmol of H_(2)was produced on compound 1 after 3 h of illumination,with a catalytic efficiency of 25.1μmol·mg^(-1)·h^(-1)and a turnover number(TON)of 36.8.The replacement of carbonyl with PPh3 could significantly improve the catalytic performance of the complex,and 705.0μmol of H_(2)was produced on 2 after 3 h of illumination,with a catalytic efficiency of 37.9μmol·mg^(-1)·h^(-1)and a TON of 81.8.CCDC:2515700,1;2515702,1′;2515701,2.展开更多
In response to the global energy crisis and environmental challenges,photocatalytic hydrogen(H_(2))production has emerged as a sustainable alternative toward clean energy conversion.Among diverse photocatalysts invest...In response to the global energy crisis and environmental challenges,photocatalytic hydrogen(H_(2))production has emerged as a sustainable alternative toward clean energy conversion.Among diverse photocatalysts investigated,TiO_(2)-based nanomaterials have attracted significant attention due to their unique physicochemical properties,such as high chemical stability,strong redox capacity and tunable electronic structures,along with high cost-effectiveness.Extensive research on TiO_(2)-based photocatalysts proves their enormous potential in the field of H2 production.This timely and critical review explores the recent advances in TiO_(2)-based photocatalysts,discussing their distinctive advantages and synthesis methods in photocatalytic H2 production.Modification strategies,such as elemental doping(e.g.,precious metals,non-precious metals and non-metals),morphology engineering and composite formation,are summarised to improve photocatalytic efficiency.Advanced in/ex situ characterization techniques employed to probe photocatalytic mechanisms are also highlighted.Finally,major challenges,such as limited visible-light activity and charge recombination,are outlined,with perspectives on emerging TiO_(2)-based nanomaterials and design strategies to overcome current bottlenecks.And the research focus in the future is prospected,such as atomic interface engineering,machine learning auxiliary material design and large-scale preparation technology.This work aims to provide insights into the rational design of TiO_(2)-based photocatalysts for next-generation H2 production systems.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu...In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.展开更多
Current gas well decline analysis under boundary-dominated flow(BDF)is largely based on the Arps'empirical hyperbolic decline model and the analytical type curve tools associated with pseudo-functions.Due to the n...Current gas well decline analysis under boundary-dominated flow(BDF)is largely based on the Arps'empirical hyperbolic decline model and the analytical type curve tools associated with pseudo-functions.Due to the nonlinear flow behavior of natural gas,these analysis methods generally require iterative calculations.In this study,the dimensionless gas rate(qg/qgi)is introduced,and an explicit method to determine the average reservoir pressure and the original gas in place(OGIP)for a volumetric gas reservoir is proposed.We show that the dimensionless gas rate in the BDF is only the function of the gas PVT parameters and reservoir pressure.Step-by-step analysis procedures are presented that enable explicit and straightforward estimation of average reservoir pressure and OGIP by straight-line analysis.Compared with current techniques,this methodology avoids the iterative calculation of pseudo-time and pseudo-pressure functions,lowers the multiplicity of type curve analysis,and is applicable in different production situations(constant/variable gas flow rate,constant/variable bottom-hole pressure)with a broad range of applications and ease of use.Reservoir numerical simulation and field examples are thoroughly discussed to highlight the capabilities of the proposed approach.展开更多
The fracture volume is gradually changed with the depletion of fracture pressure during the production process.However,there are few flowback models available so far that can estimate the fracture volume loss using pr...The fracture volume is gradually changed with the depletion of fracture pressure during the production process.However,there are few flowback models available so far that can estimate the fracture volume loss using pressure transient and rate transient data.The initial flowback involves producing back the fracturing fuid after hydraulic fracturing,while the second flowback involves producing back the preloading fluid injected into the parent wells before fracturing of child wells.The main objective of this research is to compare the initial and second flowback data to capture the changes in fracture volume after production and preload processes.Such a comparison is useful for evaluating well performance and optimizing frac-turing operations.We construct rate-normalized pressure(RNP)versus material balance time(MBT)diagnostic plots using both initial and second flowback data(FB;and FBs,respectively)of six multi-fractured horizontal wells completed in Niobrara and Codell formations in DJ Basin.In general,the slope of RNP plot during the FB,period is higher than that during the FB;period,indicating a potential loss of fracture volume from the FB;to the FB,period.We estimate the changes in effective fracture volume(Ver)by analyzing the changes in the RNP slope and total compressibility between these two flowback periods.Ver during FB,is in general 3%-45%lower than that during FB:.We also compare the drive mechanisms for the two flowback periods by calculating the compaction-drive index(CDI),hydrocarbon-drive index(HDI),and water-drive index(WDI).The dominant drive mechanism during both flowback periods is CDI,but its contribution is reduced by 16%in the FB,period.This drop is generally compensated by a relatively higher HDI during this period.The loss of effective fracture volume might be attributed to the pressure depletion in fractures,which occurs during the production period and can extend 800 days.展开更多
Viral infections play a crucial role in marine biogeochemical cycles,by regulating bacterial mortality and mediating nutrient and carbon fluxes.However,despite of their ecological significance,existing climate change ...Viral infections play a crucial role in marine biogeochemical cycles,by regulating bacterial mortality and mediating nutrient and carbon fluxes.However,despite of their ecological significance,existing climate change models generally fail to incorporate virus-mediated ecological processes due to the current limited understanding of marine viral dynamics under global warming.While numerous studies have explored the effect of warming for viral decay and production,how temperature regulates the total abundance of marine viruses remains unclear.In this study,we conducted year-round measurements of viral production and decay rates in Qingdao's coastal waters,with additional experimental warming treatments.The result showed that under in-situ temperature,the viral decay and production rate displayed distinct seasonal variations.With the exception of summer,elevated temperature stimulated both viral decay rate and production rate,and further improved the net viral production rate.While in summer,the net viral production rate turned negative,implying divergent threshold viral decay and viral production rate on warming.Our study deepens the understanding of the effect of global warming on marine viruses and provides scientific data for climate change models.展开更多
This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data e...This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.展开更多
Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potent...Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potential to process complex datasets and support decision-making in OC diagnosis.Nevertheless,traditional ML models tend to be biased,overfitting,noisy,and less generalized.Moreover,their black-box nature reduces interpretability and limits their practical clinical applicability.In this study,we introduce an explainable ensemble learning(EL)model,TreeX-Stack,based on a stacking architecture that employs tree-based learners such as Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),and Extreme Gradient Boosting(XGBoost)as base learners,and Logistic Regression(LR)as the meta-learner to enhance ovarian cancer(OC)diagnosis.Local Interpretable ModelAgnostic Explanations(LIME)are used to explain individual predictions,making the model outputs more clinically interpretable and applicable.The model is trained on the dataset that includes demographic information,blood test,general chemistry,and tumor markers.Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7.Relevant features are then selected using the Boruta feature selection method.To obtain robust and unbiased performance estimates during hyperparameter tuning,nested cross-validation(CV)with grid search is employed,and all experiments are repeated five times to ensure statistical reliability.TreeX-Stack demonstrates excellent diagnostic performance,achieving an accuracy of 0.9027,a precision of 0.8673,a recall of 0.9391,and an F1-score of 0.9012.Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4(HE4)as the most significant biomarker for OC.The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis.展开更多
Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and...Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.展开更多
To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative t...To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.展开更多
tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years f...tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.展开更多
0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has...0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
When the operating temperature of a solid oxide electrolysis cell(SOEC)is lower than the outlet temperature of a nuclear reactor,the reactor can be directly coupled with the SOEC as a high-temperature heat source.Howe...When the operating temperature of a solid oxide electrolysis cell(SOEC)is lower than the outlet temperature of a nuclear reactor,the reactor can be directly coupled with the SOEC as a high-temperature heat source.However,the key to the efficiency and return on investment of this hybrid energy system lies in the expected lifetime of the SOEC.This study assessed Ni-YSZ|YSZ|GDC|LSC fuel electrode support cells’long-term stability during electrolysis at 650℃with a current density of−0.5Acm^(−2)over 1818 h.The average voltage degradation rate of 2.63%kh^(−1)unfolded in two phases:an initial rapid decay(90 to 1120 h at 3.58%kh^(−1))and a stable decay(1120 to 1818 h at 2.14%kh^(−1)),emphasizing SOECs’probability coupling with nuclear reactors at 650℃.Post-1818-hour electrolysis revealed nickel particle formation associated with Ni(OH)_(x)diffusion and re-deposition,alongside a strontium-containing layer causing interface cracking.Despite minimal strontium segregation in the EDS,XPS data indicated surface segregation of Sr.This study provides crucial insights into prolonged SOEC operation,highlighting both its potential and challenges.展开更多
基金supported by the Discovery Grants program of the Natural Sciences and Engineering Research Council of Canada(No.RGPIN-2021-03553)the Canadian Research Chair in dendroecology and dendroclimatology(CRC-2021-00368)+3 种基金the Ministère des Ressources Naturelles et des Forèts(MRNF,Contract no.142332177-D)the Natural Sciences and Engineering Research Council of Canada(Alliance Grant No.ALLRP 557148-20,obtained in partnership with the MRNF and Resolute Forest Products)the Fonds de recherche du Qu ebec–Nature et technologies(Partnership Research Program on the Contribution of the Forestry Sector to Climate Change MitigationGrant No.2022-0FC-309064)。
文摘Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
基金funded by the Key Research and Development Program of Shaanxi,China(No.2024GX-YBXM-503)the National Natural Science Foundation of China(No.51974254)。
文摘Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.
基金Supported by the National Natural Science Foundation of China(12301631)the Natural Science Foundation of Qinghai Province(2023-ZJ-949Q)。
文摘This paper deals with the global boundedness of a two-competing-species chemotaxis model with indirect signal production in a three-dimensional bounded domain.The current work extends prior results by ZHENG et al.(2022)who established global existence and boundedness of classical solution under the parameter constraintsµ_(1)µ_(2)a_(2)≥χ1(4+µ_(2)^(2)a _(2)^(2)),µ_(1)µ_(2)a_(1)≥χ2(4+µ_(1)^(2)a_(1)^( 2)).Our main contribution demonstrates that these technical conditions can be significantly relaxed toµ1≥5χ_(1),µ2≥5χ_(2),thereby expanding the admissible parameter space for solution boundedness.
文摘Against the backdrop of profound restructuring in the global industrial and supply chains,data elements have emerged as a critical force driving the transformation of enterprises'new quality productive forces.To address the ambiguity surrounding its micro-level empowerment mechanism,this paper empirically examines the impact of data elements on enterprises'new quality productive forces and its transmission channels using panel data of Chinese A-share listed firms from 2014 to 2023.The results show that data elements significantly improve the level of enterprises'new quality productive forces.Heterogeneity analysis indicates that this promotional effect is more pronounced in non-state-owned enterprises,large-scale enterprises,and low-leverage enterprises.Mechanism tests confirm that data elements empower new quality productive forces through three paths:enhancing enterprise innovation capability,improving internal operational efficiency,and promoting inter-firm collaboration efficiency.This study provides empirical evidence for understanding the micro-level empowerment logic of data elements and offers theoretical references and practical implications for advancing the deep integration of the digital economy and the real economy.
文摘Two[FeFe]-hydrogenase compounds with 2-cyanobenzyl groups,{Fe_(2)[(SCH_(2)CH_(3))(SR)](CO)_(6)}(1 or 1′,which are the crystalline states from petroleum ether and dichloromethane solution,respectively)and{Fe_(2)[(SCH_(2)CH_(3))(SR)](CO)_(5)(PPh_(3))}(2)(R=2-cyanobenzyl),were synthesized and characterized by infrared spectroscopy,UV-Vis spectroscopy,single-crystal diffraction,powder X-ray diffraction,etc.Their performances as photocatalysts for H_(2)production through water splitting were evaluated.The results showed that 316.8μmol of H_(2)was produced on compound 1 after 3 h of illumination,with a catalytic efficiency of 25.1μmol·mg^(-1)·h^(-1)and a turnover number(TON)of 36.8.The replacement of carbonyl with PPh3 could significantly improve the catalytic performance of the complex,and 705.0μmol of H_(2)was produced on 2 after 3 h of illumination,with a catalytic efficiency of 37.9μmol·mg^(-1)·h^(-1)and a TON of 81.8.CCDC:2515700,1;2515702,1′;2515701,2.
文摘In response to the global energy crisis and environmental challenges,photocatalytic hydrogen(H_(2))production has emerged as a sustainable alternative toward clean energy conversion.Among diverse photocatalysts investigated,TiO_(2)-based nanomaterials have attracted significant attention due to their unique physicochemical properties,such as high chemical stability,strong redox capacity and tunable electronic structures,along with high cost-effectiveness.Extensive research on TiO_(2)-based photocatalysts proves their enormous potential in the field of H2 production.This timely and critical review explores the recent advances in TiO_(2)-based photocatalysts,discussing their distinctive advantages and synthesis methods in photocatalytic H2 production.Modification strategies,such as elemental doping(e.g.,precious metals,non-precious metals and non-metals),morphology engineering and composite formation,are summarised to improve photocatalytic efficiency.Advanced in/ex situ characterization techniques employed to probe photocatalytic mechanisms are also highlighted.Finally,major challenges,such as limited visible-light activity and charge recombination,are outlined,with perspectives on emerging TiO_(2)-based nanomaterials and design strategies to overcome current bottlenecks.And the research focus in the future is prospected,such as atomic interface engineering,machine learning auxiliary material design and large-scale preparation technology.This work aims to provide insights into the rational design of TiO_(2)-based photocatalysts for next-generation H2 production systems.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
文摘In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.
基金supported by the Young Elite Scientist Sponsorship Program by Beijing Association for Science and Technology,China(No.BYESS2023262)the Science Foundation of China University of Petroleum(Beijing),China(No.2462022BJRC004).
文摘Current gas well decline analysis under boundary-dominated flow(BDF)is largely based on the Arps'empirical hyperbolic decline model and the analytical type curve tools associated with pseudo-functions.Due to the nonlinear flow behavior of natural gas,these analysis methods generally require iterative calculations.In this study,the dimensionless gas rate(qg/qgi)is introduced,and an explicit method to determine the average reservoir pressure and the original gas in place(OGIP)for a volumetric gas reservoir is proposed.We show that the dimensionless gas rate in the BDF is only the function of the gas PVT parameters and reservoir pressure.Step-by-step analysis procedures are presented that enable explicit and straightforward estimation of average reservoir pressure and OGIP by straight-line analysis.Compared with current techniques,this methodology avoids the iterative calculation of pseudo-time and pseudo-pressure functions,lowers the multiplicity of type curve analysis,and is applicable in different production situations(constant/variable gas flow rate,constant/variable bottom-hole pressure)with a broad range of applications and ease of use.Reservoir numerical simulation and field examples are thoroughly discussed to highlight the capabilities of the proposed approach.
文摘The fracture volume is gradually changed with the depletion of fracture pressure during the production process.However,there are few flowback models available so far that can estimate the fracture volume loss using pressure transient and rate transient data.The initial flowback involves producing back the fracturing fuid after hydraulic fracturing,while the second flowback involves producing back the preloading fluid injected into the parent wells before fracturing of child wells.The main objective of this research is to compare the initial and second flowback data to capture the changes in fracture volume after production and preload processes.Such a comparison is useful for evaluating well performance and optimizing frac-turing operations.We construct rate-normalized pressure(RNP)versus material balance time(MBT)diagnostic plots using both initial and second flowback data(FB;and FBs,respectively)of six multi-fractured horizontal wells completed in Niobrara and Codell formations in DJ Basin.In general,the slope of RNP plot during the FB,period is higher than that during the FB;period,indicating a potential loss of fracture volume from the FB;to the FB,period.We estimate the changes in effective fracture volume(Ver)by analyzing the changes in the RNP slope and total compressibility between these two flowback periods.Ver during FB,is in general 3%-45%lower than that during FB:.We also compare the drive mechanisms for the two flowback periods by calculating the compaction-drive index(CDI),hydrocarbon-drive index(HDI),and water-drive index(WDI).The dominant drive mechanism during both flowback periods is CDI,but its contribution is reduced by 16%in the FB,period.This drop is generally compensated by a relatively higher HDI during this period.The loss of effective fracture volume might be attributed to the pressure depletion in fractures,which occurs during the production period and can extend 800 days.
基金supported by the National Natural Science Foundation of China(No.42276108)the Young Scientists Fund of Shandong Provincial Natural Science Foundation(No.ZR2022QD052)。
文摘Viral infections play a crucial role in marine biogeochemical cycles,by regulating bacterial mortality and mediating nutrient and carbon fluxes.However,despite of their ecological significance,existing climate change models generally fail to incorporate virus-mediated ecological processes due to the current limited understanding of marine viral dynamics under global warming.While numerous studies have explored the effect of warming for viral decay and production,how temperature regulates the total abundance of marine viruses remains unclear.In this study,we conducted year-round measurements of viral production and decay rates in Qingdao's coastal waters,with additional experimental warming treatments.The result showed that under in-situ temperature,the viral decay and production rate displayed distinct seasonal variations.With the exception of summer,elevated temperature stimulated both viral decay rate and production rate,and further improved the net viral production rate.While in summer,the net viral production rate turned negative,implying divergent threshold viral decay and viral production rate on warming.Our study deepens the understanding of the effect of global warming on marine viruses and provides scientific data for climate change models.
基金supported by the National Natural Science Foundation of China[grant numbers 12171158,12371474 and 12571510]Fundamental Research Funds for the Central Universities[grant number 2025ECNU-WLJC006].
文摘This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)under the grant number IMSIU-DDRSP2601.
文摘Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potential to process complex datasets and support decision-making in OC diagnosis.Nevertheless,traditional ML models tend to be biased,overfitting,noisy,and less generalized.Moreover,their black-box nature reduces interpretability and limits their practical clinical applicability.In this study,we introduce an explainable ensemble learning(EL)model,TreeX-Stack,based on a stacking architecture that employs tree-based learners such as Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),and Extreme Gradient Boosting(XGBoost)as base learners,and Logistic Regression(LR)as the meta-learner to enhance ovarian cancer(OC)diagnosis.Local Interpretable ModelAgnostic Explanations(LIME)are used to explain individual predictions,making the model outputs more clinically interpretable and applicable.The model is trained on the dataset that includes demographic information,blood test,general chemistry,and tumor markers.Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7.Relevant features are then selected using the Boruta feature selection method.To obtain robust and unbiased performance estimates during hyperparameter tuning,nested cross-validation(CV)with grid search is employed,and all experiments are repeated five times to ensure statistical reliability.TreeX-Stack demonstrates excellent diagnostic performance,achieving an accuracy of 0.9027,a precision of 0.8673,a recall of 0.9391,and an F1-score of 0.9012.Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4(HE4)as the most significant biomarker for OC.The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis.
基金supported by the International Partnership program of the Chinese Academy of Sciences(170GJHZ2023074GC)National Natural Science Foundation of China(42425706 and 42488201)+1 种基金National Key Research and Development Program of China(2024YFF0807902)Beijing Natural Science Foundation(8242041),and China Postdoctoral Science Foundation(2025M770353).
文摘Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.
文摘To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.
基金supported by the National Natural Science Foundation of China(91959106)the Foundation of the Shanghai Municipal Education Commission(24RGZNC02)+4 种基金Shanghai Key Laboratory of Intelligent Information Processing,Fudan University(IIPL-2025-RD3-02)Key University Science Research Project of Anhui Province(2023AH030108)Climbing Peak Training Program for Innovative Technology team of Yijishan Hospital,Wannan Medical College(PF201904)Peak Training Program for Scientific Research of Yijishan Hospital,Wannan Medical College(GF2019G15)the talent project of the First Affiliated Hospital of Wannan Medical College(Yijishan Hospital of Wannan Medical College)(YR202422).
文摘tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.
基金supported by National Key R&D Program of China(No.2021YFF0501301)the National Natural Science Foundation of China(No.42172231)。
文摘0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA0400000)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2021253)+1 种基金the Major Science and Technology Projects of China National Offshore Oil Corporation Limited during the 14th Five Year Plan(No.KJGG-2022-12-CCUS-030500)the Photon Science Center for Carbon Neutrality of Chinese Academy of Science.
文摘When the operating temperature of a solid oxide electrolysis cell(SOEC)is lower than the outlet temperature of a nuclear reactor,the reactor can be directly coupled with the SOEC as a high-temperature heat source.However,the key to the efficiency and return on investment of this hybrid energy system lies in the expected lifetime of the SOEC.This study assessed Ni-YSZ|YSZ|GDC|LSC fuel electrode support cells’long-term stability during electrolysis at 650℃with a current density of−0.5Acm^(−2)over 1818 h.The average voltage degradation rate of 2.63%kh^(−1)unfolded in two phases:an initial rapid decay(90 to 1120 h at 3.58%kh^(−1))and a stable decay(1120 to 1818 h at 2.14%kh^(−1)),emphasizing SOECs’probability coupling with nuclear reactors at 650℃.Post-1818-hour electrolysis revealed nickel particle formation associated with Ni(OH)_(x)diffusion and re-deposition,alongside a strontium-containing layer causing interface cracking.Despite minimal strontium segregation in the EDS,XPS data indicated surface segregation of Sr.This study provides crucial insights into prolonged SOEC operation,highlighting both its potential and challenges.
