Storage Demand Research Articles

Photocatalytic Dinitrogen Reduction to Ammonia over Biomimetic FeMoSx Nanosheets.

Reduction of atmospheric dinitrogen (N2) to ammonia (NH3) using water and sunlight in the absence of sacrificial reducing reagents at room temperature is very challenging and is considered an eco-friendly approach to meet the rapidly increasing demand for nitrogen storage, fertilizers, and a sustainable society. Currently, ammonia production via the energy-intensive Haber-Bosch process causes ∼350 million tons of carbon dioxide (CO2) emission per year. Interestingly, natural N2 fixation by the nitrogenase enzyme occurs under ambient conditions. Unfortunately, N2 fixation on biomimetic catalysts has rarely been studied. To mimic biological nitrogen fixation, herein, we synthesized the novel iron molybdenum sulfide (FeMoSx) micro-/nanosheets via a simple hydrothermal approach for the first time. Further, we successfully demonstrated the photochemical conversion of N2 to NH3 over a biomimetic FeMoSx photocatalyst. The estimated yield is around 99.79 ± 6.0 μmol/h/g photocatalyst with a quantum efficiency of ∼0.028% at 532 nm visible-light wavelength. Besides, we also systematically studied the influence of key factors to further improve NH3 yields. Overall, this study paves a new pathway to fabricate carbon-free, photochemical N2 fixation materials for future applications.

Combined study of phase transitions in the P2-type NaXNi1/3Mn2/3O2 cathode material: experimental, ab-initio and multiphase-field results

The research of new electrode materials such as sodium intercalation compounds is key to meet the challenges of future demands of sustainable energy storage. For these batteries, the intercalation behavior on the micro-scale is governed by a complex interplay of chemical, electrical and mechanical forces strongly influencing the overall cell performance. The multiphase-field method is a suitable tool to study these multi-physics and bridge the scale from ab-initio methods to the cell level. In this work, we follow a combined approach of experiments, density functional theory (DFT) calculations and multiphase-field simulations to predict thermodynamic and kinetic properties for the P2-type NaXNi1/3Mn2/3O2 sodium-ion cathode material. Experimentally, we obtain the thermodynamic potential and diffusion coefficients at various sodium contents using electrochemical techniques and discuss limitations of the experimentally applied methods. DFT is used to identify stable phases by calculating an energy hull curve. Then, the influence of long-range dispersion interactions and the exchange-correlation functional on the voltage curve is investigated by comparison with experimental results. Finally, multiphase-field simulations are performed based on inputs from experiments and DFT. The fitting of phase-specific chemical free energies from DFT calculations and experimental data is discussed. Our results highlight the thermodynamic consistency of all three approaches close to thermodynamic equilibrium. Furthermore, the phase-field method accurately describes the kinetics of the system including multiple phase transitions, by which we unravel the mechanism of the P2-O2 phase transition in a single crystal under the influence of intercalation reaction, bulk diffusion and elastic deformation. The model is able to predict the kinetic capacity loss depending on charging rate in agreement with C-rate experiments.

Building Flexible, Data-driven Framework for Real-time Analysis

In the contemporary business landscape, the escalating demand for real-time predictive analytics is driven by the imperative for dynamic decision-making. Traditional analytics models often prove inadequate in addressing the need for agility required to respond swiftly to rapidly changing circumstances. Real-time predictive analytics, however, offers a transformative solution, empowering organizations to make informed and timely decisions in fast-paced environments. This capability proves invaluable in industries were staying ahead of emerging trends is critical, fostering a proactive approach to decision-making that can significantly impact competitiveness. The sheer volume and diversity of data require sophisticated solutions for processing and analysis. Real-time predictive analytics becomes an indispensable tool, offering the capability to promptly extract valuable insights from massive datasets. This not only enhances decision-making but also allows organizations to stay ahead by uncovering trends and patterns in real time. Scalability is a fundamental consideration for organizations on a growth trajectory. Real-time predictive analytics frameworks provide a scalable foundation, allowing businesses to seamlessly expand their analytical capabilities. This adaptability ensures that the framework can handle the increasing demands for processing power and storage, aligning with the evolving needs of a growing organization.

Seeing the Unseen: Mg2+, Na+, and K+ Transference Numbers in Post-Li Battery Electrolytes by Electrophoretic Nuclear Magnetic Resonance.

The growing demand for energy storage devices worldwide combined with limited resources for lithium attracts interest in other alkali or alkaline earth metals. In addition to conductivity, the cation transference number T+ is a decisive parameter to rank the electrolyte performance. However, the existing experimental methods for its determination suffer from various intrinsic problems. We demonstrate here a novel approach for T+ determination based on determining the total conductivity with impedance spectroscopy (IS) and the partial conductivity of the anion species, with the latter being obtained from the anion mobility by electrophoretic NMR. First, this eNMR/IS approach is validated by comparing T+ values from different methods in a Li-based solvate ionic liquid electrolyte. Then, it is applied to obtain T+ of cations with nuclei not detectable in NMR transport measurements, employing bis(trifluoromethanesulfonyl)imide (TFSI)-based metal salts. Solvate ionic liquids consisting of triethylene glycol dimethyl ether (G3) and Mg(TFSI)2 or NaTFSI yield values of TNa and TMg on the order of 0.4, similar to TLi. Furthermore, we apply the method to polymer electrolytes, again testing the concept with LiTFSI, and finally investigating NaTFSI, KTFSI, and Mg(TFSI)2 in poly(ethylene oxide). Values of TNa and TK are in the range of 0.14-0.2, similar to those of TLi, while Mg2+ shows a higher transference number (TMg = 0.3). The method is very versatile as it allows quantification of T+ for any type of cation, and moreover, it is applicable to highly concentrated electrolytes without suffering from assumptions about dissociation or from unknown interfacial resistances which impede electrochemical methods.

Blockchain Data Storage Optimisations: A Comprehensive Survey

Blockchain offers immutability, transparency, and security in a decentralised way for many applications, including finance, supply chain, and the Internet of Things (IoT). Due to its popularity and widespread adoption, it has started to process an enormous number of transactions, placing an ever-growing demand for storage. As the technology gains more popularity, the storage requirements of blockchain will increase, necessitating storage optimisation solutions. Proposed solutions for blockchain storage efficiency range from reducing the degree of data replication to redacting or compressing data. Each of these storage optimisation categories involves a complex interplay with the timing of blockchain data processing and mining, yet no existing survey analyses these dimensions. This article surveys the state-of-the-art blockchain storage optimisations and categorises them into replication-based, redaction-based, and content-based optimisations. Replication-based optimisations focus on reducing duplication of blockchain data shared among participants after committing data on the blockchain ledger. Redaction-based optimisations allow users to modify or delete data already committed on the ledger in various ways, while content-based optimisations compress data before or after committing it to the ledger. We analyse and evaluate these solutions in the aspects of security, decentralisation, and scalability. We present the advantages and disadvantages of the existing blockchain storage optimisations and comprehensively compare them. Additionally, we discuss the opportunities and challenges for future work to optimise blockchain storage.

Investigation and improvement of complex characteristics of packed bed thermal energy storage (PBTES) in adiabatic compressed air energy storage (A-CAES) systems

With the application of large-scale renewable energy, power grids have put forward explicit demands for energy storage. Among the various forms of energy storage systems, the adiabatic compressed air energy storage (A-CAES) system has received more attention due to its environmental friendliness and adaptability. As a key link in the regenerative A-CAES, thermal energy storage (TES) has an important impact on the efficiency of the system. Among various forms of TES, PBTES has better economy and adaptability. However, there is insufficient research on the impact of PBTES structure on A-CAES systems. Therefore, a dimensionless governing equation for the PBTES is proposed to study the depth effect of the PBTES structure. Through comparing with the pilot-scale and industrial-scale PBTES experimental data, the maximum error of the model is 2.3%, which proves that the model has good accuracy in the simulation of different scales of PBTES. The result of research shows that: the improved thickness of the structural layer is between 0.1 m and 0.15 m. The increase in the number of cycles of the A-CAES system results in a gradual increase in system efficiency. For the A-CAES system, the round-trip efficiency is improved by 4.1%, reaching 64%, with the improvement of the wall, volume ratio, and height-to-diameter ratio of PBTES.

A comparison of stitching techniques to reconstruct large volume x-ray tomography of batteries

Synchrotron-based X-ray microtomography (μCT) is a valuable technique to study the internal structure of heterogeneous samples with high spatial and temporal resolution. However, synchrotron X-ray imaging, such as many microscopy methods, is solidly limited by its Field of View (FOV): a challenge when approaching large and/or highly detailed volumes at high spatial imaging resolution. In this study, we consider two techniques used to increase the FOV in µCT for studying Li-ion batteries, Local Tomography Stitching (LTS) and Sinogram Oriented Stitching (SOS), and compare in terms of scan time, processing efficiency and computing storage. We complement our study by estimating the impact of different battery geometries on the stitching performance for similar scanning parameters. Evaluation of the two presented techniques reveals that both provide equally satisfyingly stitched volumes. Nevertheless, it is demonstrated that SOS is predominantly more efficient for the considered battery geometries, requires fewer scans in total, and hence has a lower time and storage demand in comparison to LTS. However, technical constraints make the SOS technique more difficult to implement. Further discussed are differences in acquisition and reconstruction for the two techniques, addressing the processing efficiency for both SOS and LTS techniques and we shall provide indicators for selecting the most suitable stitching technique.

Integration and performance analysis of optimal large-scale hybrid PV and pump hydro storage system based upon floating PV for practical application

The widespread use of green energy sources creates a significant demand for energy storage. Hybrid floating photovoltaic (FPV) and pumped hydro storage (PHS) represent one of the most dependable and cost-effective solutions, which uses the PV system on the water body combined with a pair of lakes with different heights. This study focuses on the load side as well as the PHS capacity factor and aims to lower the cost of energy (COE) by raising the PHS capacity factor. Since building a PHS requires a significant upfront investment, doing so will also lower the COE and enable the acquisition of PHS for large-scale power production, which will guarantee power access in large cities. To simulate the FPV-PHS system, the multi-objective genetic algorithm (MOGA) is employed. Sufficient power management is a prerequisite for achieving system reliability in the best possible hybrid energy system design and implementation. Considering the 60-year system lifespan, the net present cost (NPC) analysis shows that, out of all the communities evaluated, the FPV-PHS system has the lowest NPC and COE. For the optimal configuration (FPV (Block A) 105 MW, PHS 80 MW, FPV (Block B) 357 MW, and the current hydropower plant), the expected NPC and energy costs for implementing the hybrid energy system (HRS) at the chosen location are $44,737,613 and $40/MWh, respectively. The existing FPV system spans 4.65 km2 and lowers the evaporation fraction by 17,279,400 m3. The hybrid FPV-PHS system reduces annual CO2 emissions by 581,830 tons. Our research reveals that a hybrid floating PV and pump storage hydropower system offers more steady clean electricity, implying a great significance for power grid infrastructure.

Transport properties of ethylene glycol functionalized membranes exposed to nonaqueous electrolytes

Non-aqueous redox flow batteries (RFBs) are a promising technology to meet growing demand for grid-scale energy storage. Membrane separators, designed specifically for use with organic solvents, are necessary to advance non-aqueous RFBs. Herein, we report the development of a series of poly(phenylene oxide) (PPO) membranes functionalized with poly(ethylene glycol) (PEG) side chains to investigate the influence of PEG side chain length and degree of PEGylation on membrane transport properties. Increasing the degree of PEGylation generally led to increases in electrolyte uptake, hydroxy TEMPO permeability, and ionic conductivity likely caused by an increase in overall PEG content, as opposed to specific interactions caused by changing the degree of PEGylation. For membranes with similar PEG content, increasing the length of the PEG side chain resulted in decreases in electrolyte uptake, permeability, and conductivity possibly due to differences in the solvation behavior of the PEG chains with different lengths.

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%.

Confined grotthuss proton-conduction along polyoxometalate chains inside carbon nanotubes for high-rate charge storage

Redox-active materials with excellent rate capability and cycling performance are in highly demand for electrochemical energy storage and conversion applications. Here, we unveil the confined proton-conduction behavior of one-dimensional polyoxometalate chains inside single-walled carbon nanotubes (SWNTs). The polyoxometalate molecules including phosphomolybdic acid {HPMo} and phosphotungstic acid {HPW} are encapsulated within SWNTs via host–guest recognition, driven by the electron transfer from nanotubes to polyoxometalates. Impressively, the redox hybrids of polyoxometalate@SWNTs deliver abnormal rate performance in acidic solution with capacity retentions over 73 % at high sweep rate of 1000 mV s−1, relative to the capacity at 10 mV s−1. This value is comparable to that of the pure SWNTs, typically known as double-layer capacitive materials. Electrochemical analyses reveal the Grotthuss proton-conduction mechanism of {HPMo} in SWNTs with a low activation energy of 0.11 eV. Therefore, the {HPMo}@SWNT hybrid demonstrates quasi diffusion-free characteristic with dominant capacitive contribution over 60 % at different sweep rates. Molecular dynamic simulations and density functional theory calculations evidence the strong burst-like transport of protons in the {HPMo}@SWNT hybrid. This process is dynamically favored in the continuous and long-range hydrogen-bond network along polyoxometalate chains with bound/free water molecules inside SWNTs. The superiority of nanoconfined Grotthuss mechanism may engender an exciting avenue for developing high-performance energy storage materials.

Investigation of the part load behavior for the R-Graz cycle with pure oxygen and hydrogen fuel

ABSTRACT In order to address the future demands for large-scale and efficient hydrogen storage and utilization, while considering the need of power dispatch and peak shaving, in this article, based on the cycle part load models and the conceptual design of the main components, the R-Graz cycle design parameters have been reoptimized, and subsequently, an analysis of the part load behavior and performance characteristics was conducted. The results show that within the load range discussed in this paper, i.e., 50% to 100%, the components of the R-Graz cycle are well matched, enabling high-efficiency operation. The net cycle efficiencies are 64.4% and 71.0% for 50% and 100% load respectively. Within this wide load range, the performance of the R-Graz cycle surpasses that of the Graz cycle consistently. When the load decreases, key parameters of the R-Graz cycle’s main components, such as rotational speed, pressure, temperature, mass flow rate and split ratio also decrease. Performance analysis under varying load conditions reveals that the maximum temperature in the cycle is a critical parameter affecting the efficiency of the R-Graz cycle. The research findings of this article lay the foundation for the practical application of the R-Graz cycle.

MsLPCC: A Multimodal-Driven Scalable Framework for Deep LiDAR Point Cloud Compression

LiDAR sensors are widely used in autonomous driving, and the growing storage and transmission demands have made LiDAR point cloud compression (LPCC) a hot research topic. To address the challenges posed by the large-scale and uneven-distribution (spatial and categorical) of LiDAR point data, this paper presents a new multimodal-driven scalable LPCC framework. For the large-scale challenge, we decouple the original LiDAR data into multi-layer point subsets, compress and transmit each layer separately, so as to ensure the reconstruction quality requirement under different scenarios. For the uneven-distribution challenge, we extract, align, and fuse heterologous feature representations, including point modality with position information, depth modality with spatial distance information, and segmentation modality with category information. Extensive experimental results on the benchmark SemanticKITTI database validate that our method outperforms 14 recent representative LPCC methods.

Assessment of supply-demand relationships considering the interregional flow of ecosystem services.

Accurate assessment of ecosystem service (ES) supply, demand, and flow is essential for identifying and enhancing the ES supply-demand relationship and promoting regional sustainable development. Based on the InVEST model, supply-demand ratio, coupling coordination analysis, breakpoint and field strength model, and GIS spatial analysis method, we evaluated the supply and demand of water yield, food supply, carbon storage, and soil conservation service in the Loess Plateau in 2000 and 2020 and analyzed the supply-demand relationship before and after considering the interregional ecosystem service flow (ESF). The results showed that (1) from 2000 to 2020, the supply and demand of the four types of ESs in the Loess Plateau increased. Before considering ESF, the surplus degree in water yield, food supply, and soil conservation increased, and carbon storage decreased. In most counties, the coupling coordination between the supply and demand of the soil conservation is mostly extreme incoordination and moderate incoordination, and other types of ESs are mostly reluctant coordination and moderate incoordination. The degree of incoordination in water yield and soil conservation have eased, while food supply and carbon storage have strengthened. For the comprehensive supply-demand relationship of ES, the degree of surplus and coordination increased, with most counties were in a state of surplus and coordination. (2) Water yield and soil conservation services flow primarily to the western and northwestern portions of the Loess Plateau, with a decrease in the number of flow paths but an increase in the total flow rate for the former and a decrease in flow paths and total flow rate for the latter. The food supply and carbon storage flow in all directions and the total flow rate increases, with a significant increase in the number of flow paths for carbon storage. (3) After considering ESF, the supply-demand relationship of each type of ES and the comprehensive ES supply-demand relationship are changed, in which the degree of surplus and coordination of deficit counties are significantly improved, and some counties even become surplus or improve the level of coordination. After considering ESF, the supply-demand ratio changes even more relative to the degree of coupling coordination. This study is of great significance for identifying the cross-regional transfer pattern of ES, understanding in-depth the dynamic supply-demand relationship of ES, and mitigating the mismatch between supply and demand of ES. It provides a scientific and objective theoretical basis for promoting regional sustainable development.

Experimental Investigation of Injection and Production Cycles for Limestone Reservoirs via Micro-CT: Implications for Underground Gas Storage

Global demand for underground gas storage (UGS) is steadily increasing, with the limestone-based UGS system situated in the Sichuan Basin of China gathering considerable interest in recent years. However, studies focusing on the fundamental mechanisms of the injection-production process in these systems are limited. Moreover, existing studies utilizing physical experimental methods frequently fall short in effectively visualizing micro-flow or incorporating real core samples from the reservoir. To address these gaps, we performed a coreflood experiment, integrating micro-Computed Tomography (CT) scanning to investigate mechanisms of fluid flow and storage capacity during the injection and production cycles in limestone reservoirs. Our approach involved utilizing core plugs with artificially engraved fracture-vuggy structures, which mimic the characteristics of the reservoir. Micro-CT scans were performed to visualize the microscopic changes in fractured-vuggy structures and the distribution of irreducible water during each cycle. This study reveals that increased cycles correspondingly affect gas storage capacity, particularly by expanding it in relative larger vuggy structures while reducing it in finer fissure network structures. The amount of irreducible water decreases after injection-production cycles, likely being expelled alongside the extracted dry gas. This plays a critical role in expanding the storage capacity in larger vuggy systems. Conversely, there is a decrease in storage capacity within fissure network systems, as the irreducible water is replaced by gas. This leads to a reduction in the opening force of the fine conduit. The dense matrix has a very limited effect on the flow mechanism and its influence on storage capacity. Overall, these findings offer practical insights for optimizing injection and production strategies in limestone UGS systems within the Sichuan Basin, contributing to a deeper understanding and efficient utilization of this vital infrastructure.

Uncertainty-Aware Online Learning of Dynamic Thermal Control in Data Center with Imperfect Pretrained Models

The growing demand for cloud computing and storage necessitates the expansion of Data Centers, thereby increasing energy consumption and environmental footprint. The data-driven dynamic thermal control presents a promising solution to mitigate this issue, as it can offer improved thermal control strategies and address the intricate and stochastic nature of the thermal environment. Nonetheless, existing solutions typically exhibit deficiencies in terms of reliability and practicability. To this end, this paper proposes a feasible uncertainty-aware online learning method with reliable dynamics models and controllers. Specifically, Deep Learning models with the capacity for uncertainty quantification are employed to account for the imperfections in pre-trained models, hence providing reliable predictions. The Cross-Entropy Method and Monte Carlo trajectory sampling solve optimization problems while considering uncertainties. It is mathematically analyzed that the uncertainty quantified by the system dynamics model can be captured with sufficient trajectory samples, rendering a reliable controller. A practical framework is proposed and tested on a widely used Data Center model to mitigate the insufficient capacity of Deep Learning models to quantify uncertainties. The results demonstrate its superior performance during the initial training stage, achieving a 66% reduction in violations and 3.76% power savings compared to the default controller. Continuous active online learning enhances its adaptability, resulting in an 80% reduction in violations and a 6% energy saving. The thermal control strategies are thus improved by the proposed new solutions and insights.

SSTU: Swin-Spectral Transformer U-Net for hyperspectral whole slide image reconstruction

Whole Slide Imaging and Hyperspectral Microscopic Imaging provide great quality data with high spatial and spectral resolution for histopathology. Existing Hyperspectral Whole Slide Imaging systems combine the advantages of the techniques above, thus providing rich information for pathological diagnosis. However, it cannot avoid the problems of slow acquisition speed and mass data storage demand. Inspired by the spectral reconstruction task in computer vision and remote sensing, the Swin-Spectral Transformer U-Net (SSTU) has been developed to reconstruct Hyperspectral Whole Slide images (HWSis) from multiple Hyperspectral Microscopic images (HMis) of small Field of View and Whole Slide images (WSis). The Swin-Spectral Transformer (SST) module in SSTU takes full advantage of Transformer in extracting global attention. Firstly, Swin Transformer is exploited in space domain, which overcomes the high computation cost in Vision Transformer structures, while it maintains the spatial features extracted from WSis. Furthermore, Spectral Transformer is exploited to collect the long-range spectral features in HMis. Combined with the multi-scale encoder-bottleneck-decoder structure of U-Net, SSTU network is formed by sequential and symmetric residual connections of SSTs, which reconstructs a selected area of HWSi from coarse to fine. Qualitative and quantitative experiments prove the performance of SSTU in HWSi reconstruction task superior to other state-of-the-art spectral reconstruction methods.

A two-stage risk-based framework for dynamic configuration of a renewable-based distribution system considering demand response programs and hydrogen storage systems

Distribution feeder reconfiguration (DFR) in distribution systems can enhance operating conditions by reducing losses and improving voltage indices. This paper introduces a dual-stage risk-based framework that concurrently tackles day-ahead reconfiguration and microgrid scheduling within the distribution network. To control the negative aspects of uncertainties, the proposed framework integrates energy storage systems (ESS/HSS) and demand response programs (DRPs) at the network level, enhancing adaptability. The initial stage of the proposed model employs the AC power flow model, utilizing loss and voltage deviation functions as objective benchmarks for network reconfiguration. The scheduling is meticulously executed per interval, deriving optimized structures under diverse scenarios with the aid of a case reduction technique (CRT) to streamline solutions. The ultimate solution employs the grey wolf optimization (GWO) algorithm and CPLEX solver in the first and second stages respectively. Outcomes from applying this approach to the adjusted 118-bus network manifest improved operational conditions and voltage quality through reconfiguration. Impressively, the integration of ESS/HSS and DRPs yields a substantial 22.34% reduction in total operating costs, a conclusion substantiated by the numerical findings. Furthermore, by leveraging the CRT, a remarkable 56.17% reduction in problem-solution time is achieved.

Pairing Non‐Solvating Cosolvent with Weakly Solvating Solvents for Facile Desolvation to Enable EC‐Free and High‐Rate Electrolyte for Lithium Ion Batteries

AbstractThe advancement of lithium‐ion batteries (LIBs) calls for superior electrolyte design to satisfy the booming demands of energy storage markets. Despite being typically indispensable in modern electrolytes, ethylene carbonate (EC) exhibits strong affinity with Li+ and derives organic‐rich SEI, leading to sluggish interfacial dynamics and unsatisfied rate capability. Here, we construct a kinetically favorable electrolyte based on linear carbonates (DMC) and fluoroethylene carbonate (FEC) with functional non‐solvating cosolvent, fluorobenzene (FB). Specifically, DMC and FEC, as weak binding solvents, show facile interfacial kinetics. FB enables a loose coordination chemistry through dipole‐dipole interaction, further lubricating Li+ desolvation and transport through SEI at interphase, which accelerates electrochemical kinetics and enables improved rate performance (257 mA h g−1 is achieved for graphite at 6 C). Moreover, the optimized electrolyte shows decent graphite compatibility, long‐term stability (75 % capacity retention after 350 cycles for NCM622/graphite pouch cells at 1 C) and wide‐temperature adaptability (−40~160 °C). This work reveals that dipole‐dipole interaction between non‐solvating cosolvents and solvents can effectively lower desolvation energy barrier and ultimately enable good rate capability, which provides a new avenue for designing EC‐free electrolyte in high‐rate LIBs.

Quantifying energy flexibility potential of ground electric vehicles in an airport with real behavior data

Towards sustainable airports, vehicle electrification can not only reduce direct carbon emissions, but also provide great energy flexibility to integrate renewable energy. However, it remains challenging to quantify their potential, due to a lack of understanding of various vehicles’ complicated behavior in an airport. Hereby, a large-scale investigation into the behavior and energy consumption of all types of vehicles was performed in a Chinese hub airport. From the overall perspective of an airport, a behavior data-based simulation was conducted to evaluate their accessible storage (EAS) under different conditions, including charging infrastructure and flight schedule. For the full-potential case with a normal flight schedule, the airside vehicles have the largest share of EAS (60 %), followed by the landside vehicles in the ground transportation center (30 %) and the staff parking lots (10 %). The overall EAS is almost as large as the energy storage demand to realize a fully solar-supplied airport. Nevertheless, the current capacity is limited to only 15 % of the full potential by electrification rate and charging behavior. This study fills the gap of lacking behavior data of the vehicles in airports. The findings shed light on the role of vehicle electrification in airport decarbonization and provide guidelines for future practice.