High Storage Efficiency Research Articles

Multi-channel rod structure hard carbon for high initial Coulombic efficiency and low-potential sodium storage

Hard carbon is one of the most promising anode materials that can be commercialized on a large scale for sodium ion batteries due to its resource abundance, cost-effectiveness, and high sodium storage capacity. Nevertheless, the improvement of high initial Coulombic efficiency and high energy density remains an urgent problem to be solved for the commercialization of sodium-ion batteries (SIBs). Herein, poplar wood was used as the precursor to prepare multi-channel rod structure hard carbon via one-step high-temperature thermal decomposition. The results showed that the microstructure and surface chemical composition of poplar-derived hard carbon can be adjusted by changing the carbonization temperature. Due to its unique multi-channel rod structure, suitable d(002)-spacing (0.370 nm) and ultra-low specific surface area (8.9 m2 g−1), the PHC-1600 exhibits a high reversible capacity of 325 mA h g−1 at current density of 50 mA g−1. It shows a capacity of 245 mA h g−1 at low-potential plateau (75 %). At the same time, the PHC-1600 also exhibits a high initial Coulombic efficiency of 88.3 % and good electrochemical property. The ex-situ XRD and CV revealed that PHCs is dominated by intercalation mechanism. This work provides a new perspective from biomass for the development of outstanding electrochemical performance anode materials with highly profitable and environment-friendly.

Synthesis, characterization, and investigation of electrochemical hydrogen storage capacity in barium hexaferrite nanocomposite

Hydrogen is considered as one of the most renewable and also efficient fuels. When hydrogen is burned, its only product is water. But the most important issue in the use of hydrogen is its storage. Hydrogen must be stored reversibly in a way that is completely safe and has high storage efficiency. Due to obtaining high-efficiency hydrogen storage, new nanostructures based on barium hexaferrite nanostructures (BaFe12O19) and functionalized multi-walled carbon nanotubes BaFe12O19 nanocomposite (BaFe12O19/MWCNTs) are synthesized via sol-gel combustion method. The nanocomposites are confirmed through Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Brunauer-Emmett-Teller (BET), and vibrating sample magnetometer (VSM) techniques. Further, the hydrogen storage capacity of BaFe12O19/MWCNTs nanocomposite is assessed by chronopotentiometry analysis (CHP). The XRD studies demonstrates that the average crystallite sizes of the BaFe12O19 and nanocomposite are estimated to be about 16.3 and 7.6 nm, respectively. Also, the VSM measurement shows a saturation magnetization values (Ms) of 20.1 and 1.3 emu/g for BaFe12O19 and BaFe12O19/MWCNTs nanocomposite, respectively. The electrochemical consequences indicate that the BaFe12O19/MWCNTs nanocomposite has a high capacity for hydrogen storage and discharge rate. The maximum discharge capacity of BaFe12O19/MWCNTs nanocomposite reaches about 360 mAh/g in the first cycle to approximately 3600 mAh/g after 20 cycles under an optimal current of 2 mA. As a result, the BaFe12O19/MWCNTs nanocomposite showed a good capacity as a promising active ingredient for hydrogen storage devices.

Enzyme Nanosheet-Based Electrochemical Aspartate Biosensor for Fish Point-of-Care Applications.

Bacterial infections in marine fishes are linked to mass mortality issues; hence, rapid detection of an infection can contribute to achieving a faster diagnosis using point-of-care testing. There has been substantial interest in identifying diagnostic biomarkers that can be detected in major organs to predict bacterial infections. Aspartate was identified as an important biomarker for bacterial infection diagnosis in olive flounder (Paralichthys olivaceus) fish. To determine aspartate levels, an amperometric biosensor was designed based on bi-enzymes, namely, glutamate oxidase (GluOx) and aspartate transaminase (AST), which were physisorbed on copolymer reduced graphene oxide (P-rGO), referred to as enzyme nanosheets (GluOx-ASTENs). The GluOx-ASTENs were drop casted onto a Prussian blue electrodeposited screen-printed carbon electrode (PB/SPCE). The proposed biosensor was optimized by operating variables including the enzyme loading amount, coreactant (α-ketoglutarate) concentration, and pH. Under optimal conditions, the biosensor displayed the maximum current responses within 10 s at the low applied potential of −0.10 V vs. the internal Ag/AgCl reference. The biosensor exhibited a linear response from 1.0 to 2.0 mM of aspartate concentrations with a sensitivity of 0.8 µA mM−1 cm−2 and a lower detection limit of approximately 500 µM. Moreover, the biosensor possessed high reproducibility, good selectivity, and efficient storage stability.

BH2I-GAN: Bidirectional Hash_code-to-Image Translation using Multi-Generative Multi-Adversarial Nets

Given the benefits of high retrieval efficiency and low storage cost, hashing method has received an increasing attention. In particular, deep learning-based hashing has been widely used in data mining and information retrieval. However, almost all the existing methods only achieve the goal of high retrieval precision, and limit the evaluation of hashing methods to objective aspect. In this paper, we propose a novel bidirectional hash_code-to-image translation model by using multi-generative multi-adversarial nets to reduce storage cost truly and obtain satisfactory user acceptance on the basis of high retrieval precision. Firstly, we propose supervised manifold metric to reduce Hamming distance between similar instances while increasing the Hamming distance between dissimilar instances, which have been proved to be helpful for high retrieval precision and good user acceptance. Then, we utilize multi-generative and multi-adversarial networks to construct hash mapping and inverse hash generation. During inverse generation, theoretical analysis is conducted to show that inverse hash network can avoid unstable training and mode collapse. Besides, we prove that Poisson distribution induced by hash codes can be initialized as generative distribution to fit real distribution. Experimental results show that our method outperforms several state-of-the-art approaches on three popular datasets. Specifically, ours yields average about 9.3% increment in Mean Average Precision(MAP) on three datasets, and achieves over 90% user satisfaction. Besides, it successfully reduces storage cost by 1,634 times in COCO 2017 large-scale dataset.

Benchmark study for the simulation of Underground Hydrogen Storage operations

While the share of renewable energy sources increased within the last years with an ongoing upward trend, the energy sector is facing the problem of storing large amounts of electrical energy properly. To compensate daily and seasonal fluctuations, a sufficient storage system has to be developed. The storage of hydrogen in the subsurface, referred to as Underground Hydrogen Storage (UHS), shows potential to be a solution for this problem. Hydrogen, produced from excess energy via electrolysis, is injected into a subsurface reservoir and withdrawn when required. As hydrogen possesses unique thermodynamic properties, many commonly used correlations can not be simply transferred to a system with a high hydrogen content. Mixing processes with the present fluids are essential to be understood to achieve high storage efficiencies. Additionally, in the past, microbial activity, e.g. by methanogenic archaea, was observed, leading to a changing fluid composition over time. To evaluate the capability of reservoir simulators to cover these processes, the present study establishes a benchmark scenario of an exemplary underground hydrogen storage scenario. The benchmark comprises of a generic sandstone gas reservoir and a typical gas storage schedule is defined. Based on this benchmark, the present study assesses the capabilities of the commercial simulator Schlumberger ECLIPSE and the open-source simulator DuMux to mimic UHS related processes such as hydrodynamics but also microbial activity. While ECLIPSE offers a reasonable mix of user-friendliness and computation time, DuMux allows for a better adjustment of correlations and the implementation of biochemical reactions. The corresponding input data (ECLIPSE format) and relevant results are provided in a repository to allow this simulation study’s reproduction and extension.

Sponge-like form-stable phase change materials with embedded graphene oxide for enhancing the thermal storage efficiency and the temperature response in transport packaging applications

Hydrated salt-based form-stable phase change materials (FPCMs) with appropriate phase transition temperatures display great potentials in the thermal management of transport packaging. In this study, a series of novel hydrated salt-containing sponge-like FPCMs supported by superporous hydrogel (SPH) was fabricated through a foam polymerization method. N,O-carboxymethyl chitosan (CMCS) and graphene oxide (GO) were chosen as modifiers. The surfaces and inter structures of the FPCMs were examined by scanning electron microscopy (SEM) and optical microscopy (OM). Differential scanning calorimetry (DSC) was used to measure the thermal storage efficiencies of the FPCMs. The temperature responses and the temperature-regulation properties of the FPCMs were tested by a thermal conductivity measurement apparatus, a temperature recorder and an infrared thermography. The mechanical properties of the FPCMs were obtained by a texture analyser. DSC analysis demonstrates that the sponge-like FPCMs with teeming ionic groups can make them load and grasp more PCMs to acquire high energy storage efficiencies. The FPCM with GO&CMCS-modified SPH achieved the highest effective enthalpy percentage (87.8 %) and melting enthalpy (169.0 J/g), which was 43 % higher than that of nonsuperporous hydrogels (NSPH)-supported FPCM without CMC and GO. Meanwhile, the interconnected channels and the enhanced thermal conductivity of the GO-embedded FPCM with SPH led to a 42 % decrease in time to charge when compared with FPCM with NSPH. Besides, the hardness of FPCMs with SPH was remarkable enhanced by over 140 % comparing to FPCMs with NSPH since the internal pores of the SPH could absorb the impact energy. Moreover, the mechanical properties of the FPCMs were further improved when CMCS and GO were incorporated into the matrix. Additionally, thermal images show that the SPH-supported FPCMs, especially modified by CMCS and GO, exhibit a temperature-regulation property, which is beneficial in transport packaging for extending the time to discharge. As a result, the sponge-like FPCMs with embedded GO was an excellent candidate used for energy storage and thermal regulation in transportation.

Advanced geothermal energy storage systems by repurposing existing oil and gas wells: A full-scale experimental and numerical investigation

Advanced Geothermal Energy Storage systems provides an innovative approach that can help supply energy demand at-large scales. They operate by injection of heat collected from various sources into an existing well in low temperature subsurface to create an artificial and sustainable geothermal reservoir to enable electricity generation. Very few studies investigated this approach, but those relied on hypothetical scenarios. Therefore, this study focuses on the field-scale investigation of an Advanced Geothermal Energy Storage in the low-temperature Illinois basin. A systematic preliminary data analysis was conducted to probe thermal energy storage properties of the subsurface. A full-scale field test was performed to delineate the subsurface thermal processes. Results from the field test were used to calibrate a two-dimensional axisymmetric numerical model for a single push/pull well. Various operational schemes were simulated for using the validated numerical model. The results indicated that the energy storage efficiency might reach up to 82% and the extracted fluids could generate an electrical power of 5.74 MW in five years for a simultaneous monthly injection and production scheme with an initial 90 days charging period. The proposed system which benefits from existing infrastructure, offers an economical and environmental approach for thermal storage with high storage efficiency.

Thermodynamic and economic analysis of a novel gravity‐enhanced compressed air energy storage system

AbstractIn this paper, a novel energy storage technology of a gravity‐enhanced compressed air energy storage system is proposed for the first time, aiming to support the rapid growth of solar and wind capacity. With air storage formed by the shaft well, gravity piston, and seal membrane, the proposed system could achieve constant operating pressure, high storage efficiency, and large storage capacity. Electricity charged into this system is simultaneously transformed and stored in the thermal, pressure potential, and gravitational potential forms. The operating properties of the proposed system are simulated and analyzed with the commercial software Thermoflex V29. Results show that both the storage pressure and the well radius have essential influences on the performance of the newly proposed system. The theoretical energy storage efficiency of the proposed system is in a scope of 63.58%–65.50% with a storage pressure range of 40–100 bar. Economic analysis based on the time‐of‐use price policy in China suggests that the optimum levelized cost of storage (LCOS) happens at the storage pressure of 70 bar and a well radius of 8 m. Besides, considering different plant cost factors that reflect the total investment of a power plant, the optimum LCOS is 1.09–1.26 CNY/kWh. Moreover, the specific exergy cost of the discharged electricity is assessed to be 0.51 CNY/kWh or so, which is significantly more profitable compared with the given electricity price of 1.15 CNY/kWh at off‐peak.

Portable integrated photo-charging storage device operating at 3 V

The integration of an energy harvesting device and an energy storage device into one unit has been widely studied as a distributed power source. Herein, we propose a high-voltage-driven photo-charging storage device, integrating a series-connected perovskite solar cell and an ionogel-based solid supercapacitor. The photo-charging storage device exhibits high overall efficiencies of 13.17 % and 9.87 % at 1 mA cm−2 and 20 mA cm−2, respectively, attributed to its high storage efficiency of over ∼ 70 % at all discharge current densities. This high discharge current density of 20 mA cm−2 is the record value for all reported integrated solar cells and electric double-layer capacitor systems. Moreover, the photo-charging storage device maintains 78.6 % of its overall efficiency after 100 cycles under AM 1.5 G illumination and exhibits remarkable cycle performance under indoor light illumination. To understand the interfacial behavior of the photo-charging storage device, impedance spectroscopy is performed for the first time. In addition, to avoid the decomposition of the perovskite solar cell active layer with moisture, an encapsulation process is conducted, which enables the device to remain operational at 3 V after 8 weeks. These results underline the high potential of this photo-charging storage device as a portable power source.

Efficient Delay-Aware Task Scheduling for IoT Devices in Mobile Cloud Computing

The development of Internet of Things (IoT) technology depends on technologies such as high-efficiency storage and high computing power. Mobile cloud computing (MCC) technology will be an important foundation for the development of IoT. The efficient scheduling of tasks in IoT devices in MCC environment is challenging. The requirements for task scheduling in MCC are becoming more and more complex. As the core problem in MCC, task scheduling aims to allocate tasks reasonably, achieve optimal scheduling strategies, and complete tasks effectively. In this paper, efficient delay-aware task scheduling algorithm (EDTSA) is proposed, with the optimization goal of minimizing task running time. The matching of tasks and virtual machines is modeled as a bipartite graph. The problem is divided into multiple subproblems to solve the optimal solution separately. The combined solution is used as the initial solution of the local search algorithm. The efficiency of the local search depends on the quality and nature of the initial solution. We can generate multiple initial solutions according to different division criteria. The initial solution is the combination of the optimal solutions of the subproblems, so the quality of the initial solution has been greatly improved and generating multiple initial solutions according to the division can reduce the probability of falling into the local optimal solution. This algorithm also divides the neighborhood to reduce unnecessary searches. Finally, we verify the efficiency and practicability of the algorithm through experiments.

Effect of Tungsten Oxide Structure on Enhancing the V(II)/(VIII) Reaction and Inhibiting H2 Evolution for All-Vanadium Redox Flow Battery

Renewable energy sources require high-efficiency storage systems to allow for its integration within the electrical grid and to supply that energy when needed. All-vanadium redox flow batteries (VRFBs) are very promising for this application because they can store high amounts of energy, are designed to work for long periods, and their solutions can be used indefinitely.1 VRFBs have various advantages when combined with renewable energy sources. The relatively high cost of VRFBs limits their widespread deployment. Enhancing the kinetics of the electrochemical reactions is needed to increase the power density and energy efficiency of the VRFB, and hence decrease the kWh cost of VRFBs. Different carbon materials are used as electrodes in VRFBs (carbon felt, paper and cloth), and each of these materials affects the performance of the battery differently either through sluggish kinetics or a high chance for parasitic reactions catalyzation.2 Modification of these electrodes by carbon-based3 or metal oxides2 nanomaterials can increase the power density of the battery and suppress the parasitic reactions. Some metal oxides like tungsten oxide4 can enhance vanadium reactions alone or when used as composites with nanocarbon materials.This work aims at enhancing the VRFBs negative half-cell reaction (V2+/V3+) kinetics and inhibiting the hydrogen evolution parasitic reaction. Carbon cloth electrodes were modified, using different tungsten oxide nanostructures such as nanowires, nanoflakes, and nanospheres, and tested at different loadings. The results suggest that the change in tungsten oxide structure would lead to a change in the electrode overall performance as a result of changing the electrical conductivity, the wettability of the electrodes, the coverage of the carbon cloth, and the number and nature of the active sites available to catalyze the V2+/V3+ reaction, with the tungsten oxide nanowires showing to be the best electrode modifier.

Contrastive Quantization with Code Memory for Unsupervised Image Retrieval

The high efficiency in computation and storage makes hashing (including binary hashing and quantization) a common strategy in large-scale retrieval systems. To alleviate the reliance on expensive annotations, unsupervised deep hashing becomes an important research problem. This paper provides a novel solution to unsupervised deep quantization, namely Contrastive Quantization with Code Memory (MeCoQ). Different from existing reconstruction-based strategies, we learn unsupervised binary descriptors by contrastive learning, which can better capture discriminative visual semantics. Besides, we uncover that codeword diversity regularization is critical to prevent contrastive learning-based quantization from model degeneration. Moreover, we introduce a novel quantization code memory module that boosts contrastive learning with lower feature drift than conventional feature memories. Extensive experiments on benchmark datasets show that MeCoQ outperforms state-of-the-art methods. Code and configurations are publicly released.

An Autonomous Storage Optimization LRM Generation Strategy Based on Cascaded NURBS

Automatic control of intelligent vehicles depends heavily on accurate lane-level location information. The traditional digital map cannot meet the automatic driving requirements, and it is very difficult for lane-level road maps (LRMs) to achieve a good balance between the representation accuracy and data storage volume. To tackle this problem, we obtain the recursive definition of a lane line by analyzing road lanes using the fractal geometry theory. Subsequently, we construct a recursive feedback system and model it. On this basis, an autonomous storage optimization LRM generation strategy based on cascaded NURBS is devised. According to the input data density, this framework generates an LRM with the minimum data storage volume within the error constraint. It is mainly composed of two modules: (1) a random fractal and (2) an adaptive iteration memory optimizer, based on cascaded NURBS error feedback. First, the whole lane is divided into several meta lane line modules. Second, data sampling/interpolation is carried out iteratively according to the NURBS fitting error and symmetry of road geometry. In this way, the storage space used to store lane lines and other information is automatically optimized. Last, a simulation experiment is carried out by using a dataset obtained from a park. The simulation results show that the proposed method can achieve high accuracy and high storage efficiency. Under the constraint of a 0.1 m curve fitting error, the average memory demand of the lane line elements is less than 2.4 bytes/m.

A Monolithic Silicon‐Mesoporous Carbon Photosupercapacitor with High Overall Photoconversion Efficiency

AbstractOff‐grid devices require autonomous power supply systems that can be achieved via coupling a solar cell with a supercapacitor in one integrated multifunctional device that is able to harvest energy from the environment, store, and release it on demand. Herein, a straight‐forward approach is proposed to realize a monolithic photosupercapacitor rechargeable under illumination by integrating a silicon (Si) solar cell with an electrochemical double‐layer capacitor (EDLC) based on mesoporous N‐doped carbon nanospheres (MPNC) in a three‐electrode configuration, i.e., via a shared electrode. The optimized porous structure of the MPNC‐EDLC electrodes results in a high storage efficiency of 95% and a superior performance compared to an activated carbon based EDLC. When monolithically integrated with a highly compatible and technologically robust Si solar cell in a photosupercapacitor, fast charging up to 0.63 V (2.5 V for a module of four photosupercapacitors connected in series) in less than 5 s is attained even under weak illumination conditions, with an outstanding peak overall efficiency of 11.8%. These results show high potential toward the development of photorechargeable and decentralized power sources for deployed smart electronic devices.

Probabilistic Statistics-Based Endurance Life Prediction of Bridge Structures.

With the massive construction of bridge infrastructures, bridge health monitoring systems have gradually matured in application and research, but previous research has primarily focused on structural damage detection and bridge safety warnings based on valid data. The structural details of steel bridge panels and structural systems are determined by the coupling effects of many intrinsic and extrinsic uncertainties, such as material properties, structural characteristics, manufacturing processes, and random traffic loads. The evaluation of fatigue is a difficult task. This article first builds a big data platform, utilizing its high-efficiency parallel computing capability and highly fault-tolerant distributed file system to achieve second-level monitoring data processing; ensuring real-time data cleaning, data analysis, and safety warning; and building a big data analysis and processing platform with high reliability, high availability, high storage efficiency, and high scalability of bridge health monitoring. The big data platform chooses HDFS for offline data storage and Spark for data analysis and modelling after comparing and analysing the benefits and drawbacks of various big data technologies. Kafka is used for caching real-time data, and Spark-streaming is used for reading data and real-time processing. Finally, the platform's superiority and reliability in terms of offline computing performance, real-time online performance, scalability, and fault tolerance are confirmed through experimental analysis; the optimal data cleaning method is derived by comparing and analysing monitoring data noise, jump point, and drift phenomena. This part of the research is based on bridge temperature data with stable signals and bridge strain data with fluctuating signals, taking into account the influence of different data types; the corresponding data missing repair algorithms are proposed for different types of data to form a complete and general data patching method process. The probabilistic fracture mechanics theory, in comparison to the traditional deterministic fatigue assessment method, can better reflect the essential uncertainty of fatigue problems and is an effective way to assess the fatigue performance of orthotropic steel bridge decks. The goal of data patching is to ensure data recovery accuracy of over 90%, with no patching repair required for monitoring data with too much missing data. The endurance life of bridge structures is predicted using a big data probabilistic statistics approach based on a variety of factors such as material properties, construction characteristics, manufacturing processes, and random traffic loads.

A Framework of Blockchain Technology in Intelligent Water Management

At present, water resource information management in China is mainly a centralized model, and there exist some problems such as high cost, low efficiency, and data storage insecurity. Blockchain technology provides a good solution which can create an efficient trust mechanism among the links in the process of water resource utilization. It guarantees the security of the data, avoiding the sudden collapse of the central institutions caused by some normal operations of the entire system. Based on a decentralization blockchain, we propose a decentralized water resource information management system for the whole process of “supply-use-consumption-discharge,” which improves the traditional water data storage. Specifically, the monitoring and business data are encrypted by the blockchain and are transmitted using a peer-to-peer network. Moreover, the centralized management mode is changed and part of the management work is dispersed to each node. Thus, decisions and measures can be made and implemented quickly after discovering problems to improve the efficiency of information transmission and management. In addition, two typical blockchain-based application scenarios for water resource management are designed. A blockchain-based approach makes issuing and monitoring water abstraction permits more convenient and obtaining license information more secure and verifiable. A reliable mechanism for tracing water quality ensures the accuracy and reliability of water quality information, enables the detection of locations with inadequate water quality, and clarifies people’s responsibility, thus guaranteeing the water safety of the residents.

Research on Application of Computer Big Data Technology in Real-time Monitoring System of Bridge Structure Safety

The bridge structure safety real-time monitoring system based on big data evaluates the bridge safety status and real-time warning under the comprehensive consideration of various factors that affect the service performance of the bridge. The system platform uses a distributed file system with high fault tolerance and a parallel data processing engine with high computational efficiency. It has high reliability, availability and storage efficiency, and is easy to expand. Furthermore, the multi-factor analysis method is used to fully explore the implicit correlation between the real-time data of the various sensors of the bridge, and the bridge service performance evaluation model is accurately established by analysing the data correlation, and real-time warning of the bridge safety status is given. In addition, the system platform uses a variety of model verification methods to evaluate the effectiveness of the model to ensure the reliability of the big data analysis method.

Enhanced relaxor behavior and high energy storage efficiency in niobium substituted (Ba0.85Ca0.15)(Zr0.1Ti0.9)O3 ceramics

The microstructure, dielectric properties, relaxor behavior, and energy storage efficiency of un-substituted and niobium (Nb) substituted (Ba0.85 Ca0.15)(Zr0.1 Ti0.9) 1-x NbxO3 (for x = 0, 0.02 and 0.05) samples prepared by the solid-state reaction method has been studied in detail. All the samples exhibited perovskite structure with no trace of impurity. Composition-dependent phase transition was also observed on the addition of Niobium. At room temperature, the co-existence of rhombohedral and tetragonal phases is observed in the unsubstituted samples. As the composition changes from x = 0.02 to x = 0.05, a structural change from tetragonal to cubic is observed. A remarkable reduction in grain size, from 90 μm (for x = 0) to 1.21 μm for (x = 0.05), is observed with the addition of Niobium. This result suggests that Niobium acts as a grain growth inhibiter in barium calcium zirconium titanate (BCZT) ceramics. The effect of Niobium on transition temperature is studied from the temperature-dependent dielectric permittivity graph. It was clear that the transition temperature shifted to a lower temperature region, and for x = 0.05, at a very low temperature (−23 °C/250 K) the tetragonal to cubic transition was observed. At x = 0.05, the temperature-dependent dielectric permittivity showed a broadened curve, indicating a diffuse phase transition. The diffuse phase transition in Nb substituted samples is explained by Uchino and Nomura modified Curie Weiss law.Moreover, the observations on temperature-dependent dielectric permittivity measurements at various frequencies suggest that the substitution of Nb5+ induces relaxor behavior. The energy storage efficiency of unsubstituted and Nb substituted samples was calculated from the polarization versus electric field graph. A high storage efficiency of 84% was obtained for the Nb substituted sample (x = 0.05) at 12 kV cm−1. Enhanced relaxor behavior and increased storage efficiency were observed in (Ba0.85 Ca0.15)(Zr0.1 Ti0.9) 1-x NbxO3 at x = 0.05. Thus we suggest that these are promising materials for energy storage applications.

Promoted methane hydrate formation in -SO3--rich hydrogel clathrate

Natural gas hydrate (NGH) technology holds great promise in natural gas storage and transportation. Utilization of promotor can effectively improve NGH formation kinetics. In this work, we constructed -SO3--rich hydrogel clathrate (poly(styrenesulfonate-co-acrylamide), (PSS-co-AAm)) as promoter for methane hydrate formation, with aims of achieving rapid formation rate as well as high storage efficiency. Within swollen PSS-co-AAm hydrogel particles at swelling ratio of 120 g g−1, induction time was 141.0 ± 121.6 min, hydrate growth rate was 0.1773 ± 0.0152 mmol mL−1 min−1, and storage capacity was 110.84 ± 1.01 v/v, indicating that PSS-co-AAm strongly promoted hydrate formation kinetics. This was principally because PSS-co-AAm could accelerate hydrate formation via abundant -SO3- groups, and meanwhile increase mass transfer via enlarged gas–liquid interface. More importantly, PSS-co-AAm could slightly improve hydrate compactness and completely avoid foaming problem, and meanwhile scarcely cause severe pollution during utilization. The promoting efficiency of PSS-co-AAm were vulnerable to swelling ratio, and the swelling ratio in the range of 60 ∼ 120 g g−1 was preferable. This hydrogel-based promoter was effective and eco-friendly for methane hydrate formation, which might be benefit for large-scale industrialization of NGH technology.

Secure biometric hashing against relation-based attacks via maximizing min-entropy

With its irreversibility, low computational cost, and high storage efficiency, biometric hashing is widely used in privacy-preserving biometric recognition systems, yet its security has been recently challenged due to the emergence of relation-based attacks (RA). To address this issue, we model and analyse the RA, and discover that maximizing the conditional min-entropy of the signs of inter-class distance differences in the original space and hash space can minimize the leakage of the distance relation on the original biometric. Consequently, we develop a Secure Biometric Hashing scheme against Relation-Based Attacks (SBH-RA). SBH-RA maximizes the conditional min-entropy by using the cosine function as a distance mapping function. Meanwhile, it learns hash codes by a classification loss and quantization loss to ensure the accuracy of recognition. Our study demonstrates that SBH-RA not only offers higher security but also yields comparable or even superior recognition performance over existing biometric hashing methods experimentally and theoretically. Given 1024 bits hash codes from SBH-RA, it brings a decrease of 21% in Equal Error Rate on face dataset LFW compared with widely used Biohashing. Besides, even under white-box attacks, the probability of a successful attack is smaller than 1.69×2−346.