Insight into the capacity degradation and structural evolution of single-crystal Ni-rich cathodes

Single-crystal Ni-rich cathodes are a promising candidate for high-energy lithium-ion batteries due to their higher structural and cycling stability than polycrystalline materials.However,the phase evolution and capacity degradation of these single-crystal cathodes during continuous lithation/delithation cycling remains unclear.Understanding the mapping relationship between the macroscopic electrochemical properties and the material physicochemical properties is crucial.Here,we investigate the correlation between the physical-chemical characteristics,phase transition,and capacity decay using capacity differential curve feature identification and in-situ X-ray spectroscopic imaging.We systematically clarify the dominant mechanism of phase evolution in aging cycling.Appropriately high cut-off voltages can mitigate the slow kinetic and electrochemical properties of single-crystal cathodes.We also find that second-order differential capacity discharge characteristic curves can be used to identify the crystal structure disorder of Ni-rich cathodes.These findings constitute a step forward in elucidating the correlation between the electrochemical extrinsic properties and the physicochemical intrinsic properties and provide new perspectives for failure analysis of layered electrode materials.

PV Capacity Evaluation Using ASTM E2848: Techniques for Accuracy and Reliability in Bifacial Systems

A variety of test methodologies are commonly used to assess if a photovoltaic system can perform in line with expectations generated by a computer simulation. One of the commonly used methodologies across the PV industry is an ASTM E2848. ASTM E2848-13, 2023 test method provides measurement and analysis procedures for determining the capacity of a specific photovoltaic system built in a particular place and in operation under natural sunlight. This test method is mainly used for acceptance testing of newly installed photovoltaic systems, reporting of DC or AC system performance, and monitoring of photovoltaic system performance. The purpose of the PV Capacity Test and modeled energy test is to verify that the integrated system formed from all components of the PV Project has a production capacity that achieves the Guaranteed Capacity and the Guaranteed modeled AEP under measured weather conditions that occur when each PV Capacity Test is conducted. In this paper, we will be discussing ASTM E2848 PV Capacity test plan purpose and scope, methodology, Selection of reporting conditions (RC), data requirements, calculation of results, reporting, challenges, acceptance criteria on pass/fail test results, Cure period, and Sole remedy for EPC contractors for bifacial irradiance.

A comparison study on structure-function relationship of polysaccharides obtained from sea buckthorn berries using different methods:antioxidant and bile acid-binding capacity

In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.

Durable K-ion batteries with 100% capacity retention up to 40,000 cycles

Currently,the major challenge in terms of research on K-ion batteries is to ensure that they possess satisfactory cycle stability and specific capacity,especially in terms of the intrinsically sluggish kinetics induced by the large radius of K+ions.Here,we explore high-performance K-ion half/full batteries with high rate capability,high specific capacity,and extremely durable cycle stability based on carbon nanosheets with tailored N dopants,which can alleviate the change of volume,increase electronic conductivity,and enhance the K+ion adsorption.The as-assembled K-ion half-batteries show an excellent rate capability of 468 mA h g^(−1) at 100 mA g^(−1),which is superior to those of most carbon materials reported to date.Moreover,the as-assembled half-cells have an outstanding life span,running 40,000 cycles over 8 months with a specific capacity retention of 100%at a high current density of 2000 mA g^(−1),and the target full cells deliver a high reversible specific capacity of 146 mA h g^(−1) after 2000 cycles over 2 months,with a specific capacity retention of 113%at a high current density of 500 mA g^(−1),both of which are state of the art in the field of K-ion batteries.This study might provide some insights into and potential avenues for exploration of advanced K-ion batteries with durable stability for practical applications.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Sealing capacity evaluation of underground gas storage under intricate geological conditions

Evaluating underground gas storage(UGS)sealing capacity is essential for its safe construction and operational efficiency.This involves evaluating both the static sealing capacity of traps during hydrocarbon accumulation and the dynamic sealing capacity of UGS under intensive gas injection and withdrawal,and alternating loads.This study detailed the methodology developed by Sinopec.The approach merges disciplines like geology,geomechanics,and hydrodynamics,employing both dynamic-static and qualitative-quantitative analyses.Sinopec's evaluation methods,grounded in the in situ stress analysis,include mechanistic studies,laboratory tests,geological surveys,stress analysis,and fluid-solid interactions.Through tests on the static and dynamic sealing capacity of UGS,alongside investigations into sealing mechanisms and the geological and geomechanical properties of cap rocks and faults,A geomechanics-rock damage-seepage mechanics dynamic coupling analysis method has been developed to predict in situ stress variations relative to pore pressure changes during UGS operations and evaluate fault sealing capacity and cap rock integrity,thereby setting the maximum operational pressures.Utilizing this evaluation technique,Sinopec has defined performance metrics and criteria for evaluating the sealing capacity of depleted gas reservoirs,enabling preliminary sealing capacity evaluations at UGS sites.These evaluations have significantly informed the design of UGS construction schemes and the evaluation of fault sealing capacity and cap rock integrity during UGS operations.

Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

Effects of thinning and understory removal on water use efficiency of Pinus massoniana:evidence from photosynthetic capacity and stable carbon isotope analyses

Understanding the relationship between forest management and water use efficiency(WUE)is important for evaluating forest adaptability to climate change.However,the effects of thinning and understory removal on WUE and its key controlling processes are not well understood,which limits our comprehension of the physiological mechanisms of various management practices.In this study,four forest management measures(no thinning:NT;understory removal:UR;light thinning:LT;and heavy thinning:HT)were carried out in Pinus massoniana plantations in a subtropical region of China.Photosynthetic capacity and needle stable carbon isotope composition(δ^(13)C)were measured to assess instantaneous water use efficiency(WUE_(inst))and long-term water use efficiency(WUE_(i)).Multiple regression models and structural equation modelling(SEM)identified the effects of soil properties and physiological performances on WUE_(inst)and WUE_(i).The results show that WUE_(inst)values among the four treatments were insignificant.However,compared with the NT stand(35.8μmol·mol^(-1)),WUE_(i)values significantly increased to 41.7μmol·mol^(-1)in the UR,50.1μmol·mol^(-1)in the LT and 46.6μmol·mol^(-1)in HT treatments,largely explained by photosynthetic capacity and soil water content.Understory removal did not change physiological performance(needle water potential and photosynthetic capacity).Thinning increased the net photosynthetic rate(A_n)but not stomatal conductance(g_s)or predawn needle water potential(ψ_(pd)),implying that the improvement in water use efficiency for thinned stands was largely driven by radiation interception than by soil water availability.In general,thinning may be an appropriate management measure to promote P.massoniana WUE to cope with seasonal droughts under future extreme climates.

Bending Failure Mode and Prediction Method of the Compressive Strain Capacity of A Submarine Pipeline with Dent Defects

A dent is a common type of defects for submarine pipeline.For submarine pipelines,high hydrostatic pressure and internal pressure are the main loads.Once pipelines bend due to complex subsea conditions,the compression strain capacity may be exceeded.Research into the local buckling failure and accurate prediction of the compressive strain capacity are important.A finite element model of a pipeline with a dent is established.Local buckling failure under a bending moment is investigated,and the compressive strain capacity is calculated.The effects of different parameters on pipeline local buckling are analyzed.The results show that the dent depth,external pressure and internal pressure lead to different local buckling failure modes of the pipeline.A higher internal pressure indicates a larger compressive strain capacity,and the opposite is true for external pressure.When the ratio of external pressure to collapse pressure of intact pipeline is greater than 0.1,the deeper the dent,the greater the compressive strain capacity of the pipeline.And as the ratio is less than 0.1,the opposite is true.On the basis of these results,a regression equation for predicting the compressive strain capacity of a dented submarine pipeline is proposed,which can be referred to during the integrity assessment of a submarine pipeline.

Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system capacity.Specifically,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna transmitters.Besides,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is activated.Finally,simulation results are provided to corroborate our proposed studies.

MOF-derived porous graphitic carbon with optimized plateau capacity and rate capability for high performance lithium-ion capacitors

The development of anode materials with high rate capability and long charge-discharge plateau is the key to improve per-formance of lithium-ion capacitors(LICs).Herein,the porous graphitic carbon(PGC-1300)derived from a new triply interpenetrated co-balt metal-organic framework(Co-MOF)was prepared through the facile and robust carbonization at 1300°C and washing by HCl solu-tion.The as-prepared PGC-1300 featured an optimized graphitization degree and porous framework,which not only contributes to high plateau capacity(105.0 mAh·g^(−1)below 0.2 V at 0.05 A·g^(−1)),but also supplies more convenient pathways for ions and increases the rate capability(128.5 mAh·g^(−1)at 3.2 A·g^(−1)).According to the kinetics analyses,it can be found that diffusion regulated surface induced capa-citive process and Li-ions intercalation process are coexisted for lithium-ion storage.Additionally,LIC PGC-1300//AC constructed with pre-lithiated PGC-1300 anode and activated carbon(AC)cathode exhibited an increased energy density of 102.8 Wh·kg^(−1),a power dens-ity of 6017.1 W·kg^(−1),together with the excellent cyclic stability(91.6%retention after 10000 cycles at 1.0 A·g^(−1)).

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Effect of speed humps on instantaneous traffic emissions in a microscopic model with limited deceleration capacity

As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the classic instantaneous traffic emission model and the limited deceleration capacity microscopic traffic flow model with slow-to-start rules, this paper has investigated the impact of speed humps on traffic flow and the instantaneous emissions of vehicle pollutants in a single lane situation. The numerical simulation results have shown that speed humps have significant effects on traffic flow and traffic emissions. In a free-flow region, the increase of speed humps leads to the continuous rise of CO_(2), NO_(X) and PM emissions. Within some density ranges, one finds that these pollutant emissions can evolve into some higher values under some random seeds. Under other random seeds, they can evolve into some lower values. In a wide moving jam region, the emission values of these pollutants sometimes appear as continuous or intermittent phenomenon. Compared to the refined Na Sch model, the present model has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher volatile organic components(VOC) emissions. Compared to the limited deceleration capacity model without slow-to-start rules, the present model also has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher VOC emissions in a wide moving jam region. These results can also be confirmed or explained by the statistical values of vehicle velocity and acceleration.

Investigations of methane adsorption characteristics on marine-continental transitional shales and gas storage capacity models considering pore evolution

Methane adsorption is a critical assessment of the gas storage capacity(GSC)of shales with geological conditions.Although the related research of marine shales has been well-illustrated,the methane adsorption of marine-continental transitional(MCT)shales is still ambiguous.In this study,a method of combining experimental data with analytical models was used to investigate the methane adsorption characteristics and GSC of MCT shales collected from the Qinshui Basin,China.The Ono-Kondo model was used to fit the adsorption data to obtain the adsorption parameters.Subsequently,the geological model of GSC based on pore evolution was constructed using a representative shale sample with a total organic carbon(TOC)content of 1.71%,and the effects of reservoir pressure coefficient and water saturation on GSC were explored.In experimental results,compared to the composition of the MCT shale,the pore structure dominates the methane adsorption,and meanwhile,the maturity mainly governs the pore structure.Besides,maturity in the middle-eastern region of the Qinshui Basin shows a strong positive correlation with burial depth.The two parameters,micropore pore volume and non-micropore surface area,induce a good fit for the adsorption capacity data of the shale.In simulation results,the depth,pressure coefficient,and water saturation of the shale all affect the GSC.It demonstrates a promising shale gas potential of the MCT shale in a deeper block,especially with low water saturation.Specifically,the economic feasibility of shale gas could be a major consideration for the shale with a depth of<800 m and/or water saturation>60%in the Yushe-Wuxiang area.This study provides a valuable reference for the reservoir evaluation and favorable block search of MCT shale gas.

Bearing capacity of circular footings on multi-layered sand-waste tire shreds reinforced with geogrids

The presence of waste tires poses an environmental challenge as they occupy a significant amount of land and are expensive to dispose in landfills.However,reusing waste tires can address this issue when waste tires are used in geotechnical applications.To determine the viability of this approach,laboratoryscale tests were conducted to investigate load-bearing capacity of circular footings on sand-tire shred(STS)mixtures with shredded waste tire contents of 5%e15%by weight and three different widths of shreds.The investigation focused on analyzing the thickness of layers composed of STS mixtures,the soil cap,and the impact of geogrids on bearing capacity.The results indicate that a specific mixture of sand and tire shreds provides the highest footing-bearing capacity.In addition,the optimal shred content and size were found to be 10%by weight and 2 cm×10 cm,respectively.Furthermore,for a given tire shred width,a particular length provides the largest bearing capacity.The results agree well with that of previous research conducted by the first author and his colleagues in direct shear and California bearing ratio(CBR)tests.The primary finding of this research is that the use of two-layered STS mixtures reinforced by geogrids significantly enhances the bearing capacity.

Effective Capacity of URLLC over Parallel Fading Channels with Imperfect Channel State Information

This paper investigates the effective capacity of a point-to-point ultra-reliable low latency communication(URLLC)transmission over multiple parallel sub-channels at finite blocklength(FBL)with imperfect channel state information(CSI).Based on reasonable assumptions and approximations,we derive the effective capacity as a function of the pilot length,decoding error probability,transmit power and the sub-channel number.Then we reveal significant impact of the above parameters on the effective capacity.A closed-form lower bound of the effective capacity is derived and an alternating optimization based algorithm is proposed to find the optimal pilot length and decoding error probability.Simulation results validate our theoretical analysis and show that the closedform lower bound is very tight.In addition,through the simulations of the optimized effective capacity,insights for pilot length and decoding error probability optimization are provided to evaluate the optimal parameters in realistic systems.

Carrying Capacity and Coupling Coordination of Water and Land Resources Systems in Arid and Semi-arid Areas: A Case Study of Yulin City, China

Quantitatively assessing the carrying capacity of water and land resources systems in arid and semi-arid areas is crucial for achieving the 2030 Sustainable Development Goals.In this work,taking Yulin City in China as a case study and employing the Criteria Importance Through Intercriteria Correlation(CRITIC)method,a modified model of coupling degree was developed to evaluate the car-rying capacity of water and land resources systems endowment and utilization,as well as their coupling coordination degree from 2013 to 2020.Our findings indicate that the water and land resources of Yulin are diminishing due to declines in agriculture,higher industrial water use,and wetland shrinkage.However,reallocating domestic water for ecological sustainability and reducing sloping farmland can mitigate this trend of decline.Temporally,as the coupling coordination between water and land resources system endowment in Yulin continuously improved,the coupling coordination between water and land resources system utilization first decreased and then in-creased with 2016 as the turning point.Spatially,the carrying capacity of water and land resources systems,the coupling coordination degree between water and land resources system endowment,and the coupling coordination degree between water and land resources system utilization in Yulin exhibited the same pattern of being higher in the six northern counties than in the six southern counties.Improving the water resources endowment is vital for the highly efficient use of water and land resources.

Atomic Ni directional-substitution on ZnIn_(2)S_(4) nanosheet to achieve the equilibrium of elevated redox capacity and efficient carrier-kinetics performance in photocatalysis

It is a challenge to coordinate carrier-kinetics performance and the redox capacity of photogenerated charges synchronously at the atomic level for boosting photocatalytic activity.Herein,the atomic Ni was introduced into the lattice of hexagonal ZnIn_(2)S_(4) nanosheets(Ni/ZnIn_(2)S_(4))via directionalsubstituting Zn atom with the facile hydrothermal method.The electronic structure calculations indicate that the introduction of Ni atom effectively extracts more electrons and acts as active site for subsequent reduction reaction.Besides the optimized light absorption range,the elevation of Efand ECBendows Ni/ZnIn_(2)S_(4) photocatalyst with the increased electron concentration and the enhanced reduction ability for surface reaction.Moreover,ultrafast transient absorption spectroscopy,as well as a series of electrochemical tests,demonstrates that Ni/ZnIn_(2)S_(4) possesses 2.15 times longer lifetime of the excited charge carriers and an order of magnitude increase for carrier mobility and separation efficiency compared with pristine ZnIn_(2)S_(4).These efficient kinetics performances of charge carriers and enhanced redox capacity synergistically boost photocatalytic activity,in which a 3-times higher conversion efficiency of nitrobenzene reduction was achieved upon Ni/ZnIn_(2)S_(4).Our study not only provides in-depth insights into the effect of atomic directional-substitution on the kinetic behavior of photogenerated charges,but also opens an avenue to the synchronous optimization of redox capacity and carrier-kinetics performance for efficient solar energy conversion.

Three-dimensional limit variation analysis on the ultimate pullout capacity of the anchor cables based on the Hoek-Brown failure criterion

Only simplified two-dimensional model and a single failure mode are adopted to calculate the ultimate pullout capacity(UPC)of anchor cables in most previous research.This study focuses on a more comprehensive combination failure mode that consists of bond failure of an anchorage body and failure of an anchored rock mass.The three-dimensional ultimate pullout capacity of the anchor cables is calculated based on the Hoek-Brown failure criterion and variation analysis method.The numerical solution for the curvilinear function in fracture plane is obtained based on the finite difference theory,which more accurately reflects the failure state of the anchor cable,as opposed to that being assumed in advance.The results reveal that relying solely on a single failure mode for UPC calculations has limitations,as changes in parameter values not only directly impact the UPC value but also can alter the failure model and thus the calculation method.

Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

Plain concrete is strong in compression but brittle in tension,having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures,even when steel reinforcing is present.In order to address these challenges,short polymer fibers are randomly dispersed in a cement-based matrix to forma highly ductile engineered cementitious composite(ECC).Thismaterial exhibits high ductility under tensile forces,with its tensile strain being several hundred times greater than conventional concrete.Since concrete is inherently weak in tension,the tensile strain capacity(TSC)has become one of the most extensively researched properties.As a result,developing a model to predict the TSC of the ECC and to optimize the mixture proportions becomes challenging.Meanwhile,the effort required for laboratory trial batches to determine the TSC is reduced.To achieve the research objectives,five distinct models,artificial neural network(ANN),nonlinear model(NLR),linear relationship model(LR),multi-logistic model(MLR),and M5P-tree model(M5P),are investigated and employed to predict the TSCof ECCmixtures containing fly ash.Data from115 mixtures are gathered and analyzed to develop a new model.The input variables include mixture proportions,fiber length and diameter,and the time required for curing the various mixtures.The model’s effectiveness is evaluated and verified based on statistical parameters such as R2,mean absolute error(MAE),scatter index(SI),root mean squared error(RMSE),and objective function(OBJ)value.Consequently,the ANN model outperforms the others in predicting the TSC of the ECC,with RMSE,MAE,OBJ,SI,and R2 values of 0.42%,0.3%,0.33%,0.135%,and 0.98,respectively.