Revealing the Freezing-Induced Alteration in Microplastic Behavior and Its Implication for the Microplastics Released from Seasonal Ice.

Ice can serve as a significant temporary repository and conveyance mechanism for microplastics (MPs). MPs present in the water column can become entrapped within developing ice formations, subsequently being sequestered and transported by ice floes. With changing temperatures, MPs stored in ice can be released back into the environment, while freezing conditions can alter the properties of MPs, ultimately affecting the fate of MPs in the environment. Freezing of MPs in freshwater ice results in the aggregation of MP particles due to physical compression, leading to an increase in particle size once the MPs are released from the ice. The freezing-induced aggregation enhances buoyancy effects, accelerating the settling/rising velocity of MPs in water. Additionally, freezing can lead to enhanced surface wetting alterations, thus improving the dispersion of hydrophobic MPs. The presence of salt in the water can mitigate the effect of freezing on MPs due to the formation of a brine network within the ice structure, which reduces the pressure on MPs entrapped by ice. In cold regions, numerous MPs undergo freezing and thawing, re-entering the water column.

Entrainment and Enrichment of Microplastics in Ice Formation Processes: Implications for the Transport of Microplastics in Cold Regions.

Sea ice can serve as a temporary sink for microplastics (MPs), and thus, it too can function as a secondary source of and transport medium for MPs. This study aimed to explore the effect of various MP properties and environmental characteristics on the entrainment and enrichment of MPs in ice under varying turbulence conditions. It was found that high rotation speed in freshwater distinctively enhanced the entrainment of hydrophobic MPs in ice, this being attributable to the combined effects of frazil ice and air bubbles. The hydrophobic nature of these MPs caused them to be attracted to the water/air or water/ice interface. However, in saline water, high turbulence inhibited the entrainment of all of the MP types under study. The ice crystals formed a loose structure in saline water instead of congealing, and this allowed the exchange of MPs between ice and water, leading to the rapid expulsion of MPs from the ice. The enrichment factors of all the MPs under study increased in calm saline water compared to in calm freshwater. The results revealed that the entrainment and enrichment of MPs in ice can be critical pathways affecting their fate in cold regions.

Promoting Cross-Regional Integration of Maritime Emission Management: A Euro-American Linkage of Carbon Markets.

Reducing greenhouse gas emissions from maritime transport is an urgent topic. Some regional emissions trading systems (ETSs), buoyed by the globalized market-based measures (MBMs) plan of the International maritime organization, have initially assessed the feasibility of including maritime emissions under compliance obligations. However, including maritime emissions (which are interjurisdictional) in the existing ETSs is controversial, and globalized maritime MBMs remain elusive. Therefore, this study designed a joint bilateral maritime carbon market (BMCM) model based on the European ETS (EU-ETS) and Quebec ETS (QC-ETS). The carbon costs, speed optimization, and marginal abatement costs of three container routes under BMCM were analyzed. The results show that this Euro-American linkage achieves adequate emission coverage on specific routes and generates acceptable carbon costs for charterers. This study yields a positive result for the equal division of ETSs' exercising competence in cross-regional maritime transport and provides evidence for sector-specific ETS links based on quantitative analysis.

A Lightweight Detection Method for Remote Sensing Images and Its Energy-Efficient Accelerator on Edge Devices.

Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1-2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices.

Cleanup of oiled shorelines using a dual responsive nanoclay/sodium alginate surface washing agent.

Oil spills may affect ecosystems and endanger public health. In this study, we developed a novel and dual responsive nanoclay/sodium alginate (NS) washing fluid, and systematically evaluated its application potential in oiled shoreline cleanup. The characterization results demonstrated that sodium alginate combined with nanoclay via hydrogen bonds, and was inserted into the interlayer spacing of nanoclay. Adding sodium alginate reduced surface and interfacial tensions, while increasing the viscoelasticity of the washing fluid. Batch experiments were conducted to investigate oil removal performance under various conditions. Additionally, the factorial design analysis showed that three single factors (temperature, oil concentration, and salinity), and two interactive effects (temperature/salinity; and oil concentration/HA) displayed significant effects on the oil removal efficiency of the NS washing fluid. Compared to the commercial surfactants, the NS composite exhibited satisfactory removal efficiencies for treating oily sand. Green materials-stabilized Pickering emulsion can potentially be used for oil/water separation. The NS washing agent displayed excellent pH- and Ca2+- responsiveness, generating transparent supernatants with low oil concentration and turbidity. Our work opens an interesting avenue for designing economical, high performance, and green washing agents.

DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network.

Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated by smart sensors of user devices, where a central parameter server (PS) coordinates multiple user devices to train a global model. However, relying on centralized topology poses challenges when applying FL in a sensors network, including imbalanced communication congestion and possible single point of failure, especially on the PS. To alleviate these problems, we devise a Dynamic Average Consensus-based Federated Learning (DACFL) for implementing FL in a decentralized sensors network. Different from existing studies that replace the model aggregation roughly with neighbors' average, we first transform the FL model aggregation, which is the most intractable in a decentralized topology, into the dynamic average consensus problem by treating a local training procedure as a discrete-time series.We then employ the first-order dynamic average consensus (FODAC) to estimate the average model, which not only solves the model aggregation for DACFL but also ensures model consistency as much as possible. To improve the performance with non-i.i.d data, each user also takes the neighbors' average model as its next-round initialization, which prevents the possible local over-fitting. Besides, we also provide a basic theoretical analysis of DACFL on the premise of i.i.d data. The result validates the feasibility of DACFL in both time-invariant and time-varying topologies and declares that DACFL outperforms existing studies, including CDSGD and D-PSGD, in most cases. Take the result on Fashion-MNIST as a numerical example, with i.i.d data, our DACFL achieves 19∼34% and 3∼10% increases in average accuracy; with non-i.i.d data, our DACFL achieves 30∼50% and 0∼10% increases in average accuracy, compared to CDSGD and D-PSGD.

Feasibility of vermicomposting for spent drilling fluid from a nature-gas industry employing earthworms Eisenia fetida.

This study investigated the vermicomposting of spent drilling fluid (SDF) from the nature-gas industry mixed with cow dung in 0% (T1), 20% (T2), 30% (T3), 40% (T4), 50% (T5), and 60% (T6) ratio employing Eisenia fetida under a 6 weeks trial. Eisenia. fetida showed better growth and reproduction performances in the first three vermireactors (T1-T3), and the mortality was higher in the vermireactors that contained more spent drilling fluid (≥40%). Vermicomposting results in a decrease in total organic carbon, C/N ratio, and an increase in EC, total nitrogen, total phosphorous, total potassium compared to their initial values. The RadViz and VizRank showed that vermicomposting results in a greater impact on the C/N ratio (15.24-35.48%) and EC (7.29-26.45%) compared to other parameters. Activities of urease and alkaline phosphatase during vermicomposting initially increased and then declined suggesting vermicompost maturity. Also, seed germination, mitotic index and chromosomal abnormality assays using cowpea signified that the vermicomposts T2 is suitable for agricultural use due to the lower phytotoxicity and cytotoxicity. The results indicated that SDF could be converted into good quality manure by vermicomposting if mixed up to 20% with cow dung.

Reconfigurable Antenna Array Direction Finding System Based on a Fast Search Algorithm.

In a traditional antenna array direction finding system, all the antenna sensors need to work or shut down at the same time, which often leads to signal crosstalk, signal distortion, and other electromagnetic compatibility problems. In addition, the direction-finding algorithm in a traditional system needs a tremendous spectral search, which consumes considerable time. To compensate for these deficiencies, a reconfigurable antenna array direction finding system is established in this paper. This system can dynamically load part or all of the antennas through microwave switches (such as a PIN diode) and conduct a fast direction of arrival (DOA) search. First, the hardware structure of the reconfigurable antenna is constructed. Then, based on the conventional spatial domain search algorithm, an improved transform domain (TD) search algorithm is proposed. The effectiveness of the system has been proven by real experiments, and the advantage of the system has been verified by detailed simulations.

Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine.

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

Assessment of regional greenhouse gas emission from beef cattle production: A case study of Saskatchewan in Canada.

The beef cattle production has been considered as one of the largest sources of greenhouse gases (GHGs) emission. A large amount of GHGs including N2O and CH4 from enteric fermentation and manure are discharged to atmosphere during beef-production process. In addition, a substantial amount of GHGs is also emitted from many other related processes such as feed production, transportation, and energy consumption. In this study, an emission assessment model was developed to quantify the amount of regional GHGs produced from the beef cattle production process. A case study was conducted based on the beef production in Saskatchewan, Canada. The results demonstrated that the GHG emissions from the annual marketed beef cattle in Saskatchewan in 2014 were 8.52 × 109 kg CO2-eq in total and the cattle-source GHGs (enteric CH4, manure CH4, and manure N2O emission) accounted for more than 90% of the total emission. Sensitivity analysis showed that the most critical factors influencing the GHG emission included feedlot manure handling system, cattle diet, feed additives, maximum methane producing capacity (Bo), and climate (temperature, precipitation, and potential evapotranspiration). The potential impacts of climate change on GHG emission from beef cattle production in Saskatchewan were also investigated. An overall decrease in the GHG emission can be observed due to the climate change, which are 3.67%, 4.96%, and 6.63% for 2020-2039, 2040-2059, and 2060-2099, respectively.

X-ray Pulsar-Based Navigation Considering Spacecraft Orbital Motion and Systematic Biases.

The accuracy of X-ray pulsar-based navigation is greatly affected by the Doppler effect caused by the spacecraft orbital motion and the systematic biases introduced by the pulsar directional error, spacecraft-borne clock error, etc. In this paper, an innovative navigation method simultaneously employing the pulse phase (PP), the difference of two neighbor PPs (DPP) and the Doppler frequency (DF) of X-ray pulsars as measurements is proposed to solve this problem. With the aid of the spacecraft orbital dynamics, a single pair of PP and DF relative to the spacecraft's state estimation error can be estimated by using the joint probability density function of the arrival photon timestamps as the likelihood function. The systematic biases involved to the PP is proved to be nearly invariant over two adjacent navigation periods and the major part of it is eliminated in the DPP; therefore, the DPP is also exploited as additional navigation measurement to weaken the impact of systematic biases on navigation accuracy. Results of photon-level simulations show that the navigation accuracy of the proposed method is remarkably better than that of the method only using PP, the method using both PP and DF and the method using both PP and DPP for Earth orbit.

Spectral-Spatial Feature Extraction of Hyperspectral Images Based on Propagation Filter.

Recently, image-filtering based hyperspectral image (HSI) feature extraction has been widely studied. However, due to limited spatial resolution and feature distribution complexity, the problems of cross-region mixing after filtering and spectral discriminative reduction still remain. To address these issues, this paper proposes a spectral-spatial propagation filter (PF) based HSI feature extraction method that can effectively address the above problems. The dimensionality/band of an HSI is typically high; therefore, principal component analysis (PCA) is first used to reduce the HSI dimensionality. Then, the principal components of the HSI are filtered with the PF. When cross-region mixture occurs in the image, the filter template reduces the weight assignments of the cross-region mixed pixels to handle the issue of cross-region mixed pixels simply and effectively. To validate the effectiveness of the proposed method, experiments are carried out on three common HSIs using support vector machine (SVM) classifiers with features learned by the PF. The experimental results demonstrate that the proposed method effectively extracts the spectral-spatial features of HSIs and significantly improves the accuracy of HSI classification.

[The Fluorescence Detection of Oil Pollutants Based on Self-Weighted Alternating Trilinear Decomposition].

The oil pollutants detector is designed in this paper. The pulse xenon lamp is used as light source; the step type multi-mode pure silica fiber is chosen to transmit the excitation and emission light. The asymmetric Czemy-Turner light path of high precision grating monochromator is adopted. The detector is applied to determine the fluorescence spectrum of diesel, gasoline and kerosene. The optimal excitation /emission wavelengths are: 290/330 nm (diesel)，270/300 nm (gasoline) and 280/330 nm (kerosene). The detection limits are: diesel (0.025 mg·L-1), gasoline (0.042 mg·L-1) and kerosene(0.054 mg·L-1). The relative errors are: diesel(2.55%), gasoline(2.06%) and kerosene(1.71%). Experiment results show that the designed detector has high accuracy of measurement. The different concentration of diesel, gasoline and kerosene mixed solution is configured, and three dimensional fluorescence spectra being measured. The self-weighted alternating trilinear decomposition is adopted to decompose the spectrum data. The predicted concentration and recovery rate show that self-weighted alternating trilinear decomposition has high resolution for mixed oil substance.

[Study on Refined Oil Identification and Measurement Based on the Extension Neural Network Pattern Recognition].

There are four major problems related to fuel consumption, "large consumption", "low quality", "lack of front-end clean" and "lack of end emission control", which needs to address urgently for our country. More than 60 percent of the air pollution is due to the burning of coal and oil in our country, so the haze problem depends on how much we can deal with energy issues. We should achieve the identification and measurement of gasoline, diesel, kerosene and other refined oil products rapidly and accurately, which is important for the implementation of air pollution monitoring and controlling. in order to characterize the type information of the refined oil accurately and to improve the efficiency of the network model identification, it is effective to use principal component analysis method which could achieve the data dimension reductionwhile reducing the complexity of the problem. With principal component analysis of the most commonly used three-dimensional fluorescence spectra based on excitation-emission matrix (Excitation-Emission Matrix, EEM) data, we could obtain finer, deeper characteristic parameters. During the process of classification, it could avoid the "over-fitting" phenomenon because of the application of the cross-validation method, A neural network capable of both qualitative and quantitative analysis is designed. The neural network pattern recognition result becomes feedback to the input of the concentration network, together with the relative slope, the comprehensive background parameters, and the relative fluorescence intensity, we could achieve the measurement of the concentration of the corresponding types, then use the extension neural network pattern recognition technology to achieve identification and measurement of kerosene, diesel, gasoline and other refined oil products. The results of the study show that the average recognition rate reaches 0.99, the average recovery rate of concentration reaches 0.95, the average time of pattern recognition is 2.5 seconds and this time is 48.5% of the time used by PARAFAC model analysis method. The method significantly improves the operation speed with ideal application effect . It should be pointed out that, in order to ensure the accuracy and precision of the analysis, we should make corresponding calibration samples for specific analytes in terms of the analysis of complex mixtures such as refined oil, pesticides, tea, etc.

Effect of nanobubbles on the mobilization of microplastics in shorelines subject to seawater infiltration.

The widespread presence of microplastics (MPs) in the ocean has varying degrees of impact on ecosystems and even human health. Coastal tidal zones are crucial in controlling the movement of MPs, which are influenced by waves and tidal forces. Meanwhile, natural nanobubbles (NBs) in the ocean can affect the hydrodynamic properties of the tidal zone. The mobilization of MPs in coastal tidal zones under the effect of NBs has been less studied. In this study, we explored natural NBs' influence on the mobilization of MPs in shorelines subject to seawater infiltration. Using glass beads as a substrate, a coastal porous environment was constructed through column experiments, and the pump-controlled water flow was used to study the transport of MPs subject to seawater movement within the substrate. The infiltration of MPs under continuous and transient conditions, as well as the upward transport induced by flood tide, were considered. The role of salinity in the interactions between NBs, MPs, and substrates was evaluated. Salinity altered the energy barriers between particles, which in turn affected the movement of MPs within the substrate. In addition, hydrophilic MPs were more likely to infiltrate within the substrate and had different movement patterns under continuous and transient flow conditions. The motion of the MPs within the substrate varied with flow rate, and NBs limited the vertical movement of MPs in the tidal zone. It was also observed that NBs adsorbed readily onto substrates, altering the surface properties of substrates, particularly their ability to attach and detach from other substances.

Preparation and Properties of Gel Polymer Electrolytes with Li1.5Al0.5Ge1.5(PO4)3 and Li6.46La3Zr1.46Ta0.54O12 by UV Curing Process.

Poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP)-based gel polymer electrolytes (GPEs) are considered a promising electrolyte candidate for polymer lithium-ion batteries (LIBs) because of their free-standing shape, versatility, security, flexibility, lightweight, reliability, and so on. However, due to problems such as low ionic conductivity, PVDF-HFP can only be used on a small scale when used as a substrate alone. To overcome the above shortcomings, GPEs were designed and synthesized by a UV curing process by adding NASICON-type Li1.5Al0.5Ge1.5(PO4)3 (LAGP) and garnet-type Li6.46La3Zr1.46Ta0.54O12 (LLZTO) to PVDF-HFP. Experimentally, GPEs with 10% weight LLZTO in a PVDF-HFP matrix had an ionic conductivity of up to 3 × 10-4 S cm-1 at 25 °C. When assembled into LiFePO4/GPEs/Li batteries, a discharge-specific capacity of 81.5 mAh g-1 at a current density of 1 C and a capacity retention rate of 98.1% after 100 cycles at a current density of 0.2 C occurred. Therefore, GPEs added to LLZTO have a broad application prospect regarding rechargeable lithium-ion batteries.

Spotlight on the vertical migration of aged microplastics in coastal waters.

Coastal waters are complex and dynamic areas with unique environmental attributes that complicate the vertical migration of microplastics (MPs). The MPs that enter coastal waters from diverse sources, including terrestrial, riverine, oceanic, and shoreline inputs undergo various aging pathways. In this study, the variations in the physiochemical characteristics of MPs undergoing various aging pathways and their vertical migration under dynamic conditions subjected to the effects of different MP characteristics and coastal environmental features were comprehensively explored. Opposite effects of aging on the vertical migration of hydrophobic and hydrophilic MPs were observed, with aging appearing to promote the dispersion of hydrophobic MPs but enhance the vertical migration of hydrophilic ones. The positive role of salinity and the negative role of humic acid (HA) concentrations on MP vertical migration were identified, and the mechanisms driving these effects were analyzed. Notably, intense turbulence not only promoted the floating of positively buoyant MPs but also reversed the migration direction of negatively buoyant MPs from downward to upward. Aging-induced changes in MP characteristics had a limited effect on MP vertical migration. The inherent characteristics of MPs and the surrounding environmental features, however, played major roles in their vertical migration dynamics. ENVIRONMENTAL IMPLICATION: Microplastics (MPs) have emerged as a significant global environmental concern and the coastal zones are the hotspots for MP pollution due to their high population density. This study comprehensively investigated the variations in the physiochemical characteristics of MPs undergoing various aging pathways. Their vertical migration patterns under dynamic conditions subjected to the effects of different MP characteristics and coastal environmental features were revealed. The roles of turbulence and MP density in their migration were identified. The findings of this study have important implications for understanding the transport and determining the ecological risks of MPs in coastal waters.

Poly (Vinylidene Fluoride-Hexafluoropropylene)-Lithium Titanium Aluminum Phosphate-Based Gel Polymer Electrolytes Synthesized by Immersion Precipitation for High-Performance Lithium Metal Batteries.

Gel polymer electrolytes (GPEs) have high safety and excellent electrochemical performance, so applying GPEs in lithium batteries has received much attention. However, their poor lithium ion transfer number, cycling stability, and low room temperature ionic conductivity seriously affect the utilization of gel polymer electrolytes. This paper successfully synthesized flexible poly (vinylidene fluoride-hexafluoropropylene)-lithium titanium aluminum phosphate (PVDF-HFP-LATP) gel polymer electrolytes using the immersion precipitation method. The resulting GPE has a porous honeycomb structure, which ensures that the GPE has sufficient space to store the liquid electrolyte. The GPE has a high ionic conductivity of 1.03 ×10-3 S cm-1 at room temperature (25 °C). The GPE was applied to LiFePO4/GPE/Li batteries with good rate performance at room temperature. The discharge specific capacity of 1C was as high as 121.5 mAh/g, and the capacity retention rate was 94.0% after 300 cycles. These results indicate that PVDF-HFP-LATP-based GPEs have the advantage of simplifying the production process and can improve the utility of gel polymer lithium metal batteries.

A stochastic resonance etection algorithm based on orthonormalized basis function for magnetic anomaly detection.

To address the problem that the performance of the detector in airborne magnetic anomaly detection (MAD) is terrible, a stochastic resonance (SR) detection algorithm based on orthonormalized basis function (OBF-SR) is proposed for MAD under low signal-to-noise ratio conditions. The signal contaminated by noise is first preprocessed by the OBF method, where the sum of the three components in the OBF space is selected as the SR system input. Then, a parallel SR system with different initial states is designed to detect the signal. Finally, the simulation analysis of MAD methods is performed to draw a comparison between the OBF-SR method, the typical SR method, and the OBF method. The results show that the OBF-SR method outperforms the SR and OBF methods in the detection probability and detection range under the same conditions.

Multilevel spatial confinement of transition metal selenides porous microcubes for efficient and stable potassium storage.

Recently, potassium-ion batteries (PIBs) have been considered as one of the most promising energy storage systems; however, the slow kinetics and large volume variation induced by the large radius of potassium ions (K+) during chemical reactions lead to inferior structural stability and weak electrochemical activity for most potassium storage anodes. Herein, a multilevel space confinement strategy is proposed for developing zinc-cobalt bimetallic selenide (ZnSe/Co0.85Se@NC@C@rGO) as high-efficient anodes for PIBs by in-situ carbonizing and subsequently selenizing the resorcinol-formaldehyde (RF)-coated zeolitic imidazolate framework-8/zeolitic imidazolate framework-67 (ZIF-8/ZIF-67) encapsulated into 2D graphene. The highly porous carbon microcubes derived from ZIF-8/ZIF-67 and carbon shell arising from RF provide rich channels for ion/electron transfer, present a rigid skeleton to ensure the structural stability, offer space for accommodating the volume change, and minimize the agglomeration of active material during the insertion/extraction of large-radius K+. In addition, the three-dimensional (3D) carbon network composed of graphene and RF-derived carbon-coated microcubes accelerates the electron/ion transfer rate and improves the electrochemical reaction kinetics of the material. As a result, the as-synthesized ZnSe/Co0.85Se@NC@C@rGO as the anode of PIBs possesses the excellent rate capability of 203.9 mA h g-1 at 5 A g-1 and brilliant long-term cycling performance of 234 mA h g-1 after 2,000 cycles at 2 A g-1. Ex-situ X-ray diffraction (Ex-situ XRD) diffraction reveals that the intercalation/de-intercalation of K+ proceeds through the conversion-alloying reaction. The proposed strategy based on the spatial confinement engineering is highly effective to construct high-performance anodes for PIBs.