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Aqueous zinc-ion batteries (ZIBs) represent an attractive choice for energy storage. However, ZIBs suffer from dendrite growth and an irreversible consumption of Zn metal, leading to capacity degradation and a low lifetime. In this work, a zinc–alginate (ZA) hydrogel–polymer electrolyte (HGPE) with a non-porous structure was prepared via the solution-casting method and ion displacement reaction. The resulting ZA-based HGPE exhibits a high ionic conductivity (1.24 mS cm−1 at room temperature), excellent mechanical properties (28 MPa), good thermal and electrochemical stability, and an outstanding zinc ion transference number (0.59). The ZA-based HGPE with dense structure is proven to benefit the prevention of the uneven distribution of ion current and facilitates the reduction of excessive interfacial resistance within the battery. In addition, it greatly promotes the uniform deposition of zinc ions on the electrode, thereby inhibiting the growth of zinc dendrites. The corresponding zinc symmetric battery with ZA-based HGPE can be cycled stably for 800 h at a current density of 1 mA cm−2, demonstrating the stable and reversible zinc plating/stripping behaviors on the electrode surfaces. Furthermore, the quasi-solid-state ZIB with zinc, ZA-based HGPE, and Ca0.24V2O5 (CVO) as the anode, electrolyte, and cathode materials, respectively, show a stable cyclic performance for 600 cycles at a large current density of 3 C (1 C = 400 mA g−1), in which the capacity retention rate is 88.7%. This research provides a new strategy for promoting the application of the aqueous ZIBs with high performance and environmental benignity.

This paper underlined the corrosion behavior of aluminum alloy/galvanized steel joints fabricated by resistance spot welding (RSW) and self-piercing riveting (SPR) exposed to different salt-spray cycles. It was found that the crevice generated by different joining methods has an important impact on corrosion behavior. Compared with the RSW joint, less corrosion happened on the coupled regions in SPR joints because of the higher local pH value resulted from the smaller crevice. The crevice, however, has less impact on the types of corrosion products. Galvanic corrosion happened in the coupled region for both RSW and SPR joints. The Pourbaix diagram explained the stable corrosion products of Al and Zn Fe alloy, which are Al2O3, ZnO, and Fe3O4. Additional corrosion products observed from the experiment included α-Al(OH)3, Ca4Al2(CO3)(OH)12(H2O)5, Zn5(OH)8Cl2(H2O), Zn5(CO3)2(OH)6, and Fe2(OH)2(CO3), which can be understood by considering the metastable corrosion products, Pourbaix diagram, and possible chemical reactions.

Dissimilar material joints are widely used in the vehicle manufacturing industry. These joints are mainly formed by using joining techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Welding (RW) due to their high performance, short cycle time, and adaptability. However, the difference in the equilibrium potential between the dissimilar materials in the presence of electrolytes leads to galvanic corrosion in these joints that can impact the safety and the reliability of the whole system. In this paper, we focus on Al-Fe galvanic corrosion and develop simulation-based machine learning based surrogate model for statistical corrosion analysis of such pairs, thereby enabling the resilience and reliability analysis of dissimilar material joints under corrosion environment. In this study, a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode which considers the underlying crystal microstructure of the Al anode. Geometric and environmental factors such as crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. A comprehensive Uncertainty Quantification (UQ) study is then conducted to understand the overall corrosion behavior of the Fe-Al joints. Electrolyte conductivity is seen to have the largest effect on the material loss and therefore needs to be managed closely for better corrosion control. This study will help with enhancing the reliability and resilience of dissimilar material joints through designing and manufacturing considering corrosion performance.

In recent years, projects have been proposed to utilize salt caverns as a storage method for supercritical CO2 (s-CO2) and have been carried out around the world, which can effectively reduce the anthropogenic greenhouse gases (GHG) concentration. Careful and rational design of the salt cavern are required to guarantee the structural stability and reliability of the cavern when in service. In this study, physics-based FE models were first developed to simulate the salt cavern construction process and predict the creep deformation of the cavern wall during s-CO2 storage. In addition, Gaussian process (GP) based surrogate models were constructed from FE simulation results to estimate the properties of salt caverns with cheaper calculations. Then, to obtain a reliable and robust design, an RBDO framework was employed with the assistance of an adaptive fidelity enhancement technique to scan through the high-dimensional design space, which provides a swift and efficient search for the optimal conditions while considering the uncertainties in the salt cavern construction process. This research is one of the first to account for uncertainties within the salt cavern design process, and the results show that uncertainties of variables can strongly affect the system reliability and the volume of the salt cavern.

Silicon-based anode is a promising candidate for next generation lithium-ion batteries (LIBs) with improved energy and power density. However, the practical application of Si anode is hindered by their major reliability issue that Si experiences significant volume change during its lithiation/delithiation cycles, leading to high stress, degradation, and pulverization of the anode. With the development of advanced electrode fabrication technologies, structured Si anodes with delicately designed architectures have been proposed. This study focuses on five triply periodic minimal surface (TPMS) based 3D bi-continuous porous Si anodes, which consist of the nano structured metal scaffolds and conformally coated Si layers and explores their lithiation-induced stresses via numerical methods. The multi-physics based finite element (FE) models are firstly built to simulate the deformation and stress of Si anodes during lithiation processes. Afterwards, the Gaussian Processes (GP) based surrogate model is developed to assist the design optimization of the Si anodes within the design space. It is found that, the inverse FCC and diamond surface-based Si anodes show better performances with the lowest stress concentration. In addition, with the decrease of Si phase volume fraction and increase of scaffold fraction, the stresses can be further reduced.

Magnetic field sensor devices have been widely used to track changes in magnetic flux concentration, and the Hall sensors are promising in many engineering applications. Design optimization of the Hall effect sensor is required to ensure the quality and capability of the device when in service. Even though there has been empirical models established from experiments to guide the design of the Hall effect sensor, the underlying relationship in Hall effect sensor design parameters and corresponding performances has not been looked into thoroughly. This paper presents a physics-informed machine learning technique to optimize the geometry design of Hall magnetic sensors for a low offset and high sensitivity characteristic. Multi-physics based finite element models were first developed to simulate and predict the Hall voltage, offset voltage and sensor sensitivity of different Hall effect sensors with various geometries. In addition, to improve the design efficiency, Gaussian Process (GP) based surrogate models were constructed from multiphysics-based simulation results to effectively investigate the Hall sensor performances with an adaptive sampling strategy. Three types of geometries of Hall sensor were studied and optimized with the proposed physics-informed GP model, the obtained results were consistent with the empirical experimental result.

Self-Piercing Riveting (SPR) is one of the most commonly used methods for joining dissimilar materials in the automotive industry. These joints are popular due to their adaptability, high performance and short cycle time. However, since these joints involve two dissimilar materials, they are susceptible to galvanic corrosion in the presence of an electrolyte which is driven by the difference in the equilibrium potential of the metals. This can affect the safety and resilience of these joints. In this paper, we focus on galvanic corrosion in Al-Fe SPR joints. A Machine learning (ML) based surrogate model, which is based off of FE simulations, for statistical corrosion analysis is developed. This model enables the resilience and reliability analysis of SPR joints under corrosion environment. In this study, first a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode of a SPR joint. This model takes into account the effect of the crystal microstructure of the Al anode and the precipitation of the corrosion product. Several geometric and environmental factors including crevice gap, roughness of anode, conductivity, pH and the temperature of the electrolyte that effect corrosion are investigated. A thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al SPR joints using a novelistic Probabilistic Confidence-Based Adaptive Sampling (PCAS) technique. PCAS is used to train the surrogate model by identifying the critical sampling points and thus reducing the overall computational costs. It is found that the electrolyte temperature has the largest effects on the material loss and needs to be managed closely for better corrosion control. By understanding the corrosion performance and resultant uncertainty impact on SPR joints, the reliability and resilience of these joints can be improved.

Creating thin (100 nm) hydrophobic coatings that are durable in wet conditions remains challenging. Although the dropwise condensation of steam on thin hydrophobic coatings can enhance condensation heat transfer by 1000%, these coatings easily delaminate. Designing interfaces with high adhesion while maintaining a nanoscale coating thickness is key to overcoming this challenge. In nature, cell membranes face this same challenge where nanometer-thick lipid bilayers achieve high adhesion in wet environments to maintain integrity. Nature ensures this adhesion by forming a lipid interface having two nonpolar surfaces, demonstrating high physicochemical resistance to biofluids attempting to open the interface. Here, developing an artificial lipid-like interface that utilizes fluorine-carbon molecular chains can achieve durable nanometric hydrophobic coatings. The application of our approach to create a superhydrophobic material shows high stability during jumping-droplet-enhanced condensation as quantified from a continual one-year steam condensation experiment. The jumping-droplet condensation enhanced condensation heat transfer coefficient up to 400% on tube samples when compared to filmwise condensation on bare copper. Our bioinspired materials design principle can be followed to develop many durable hydrophobic surfaces using alternate substrate-coating pairs, providing stable hydrophobicity or superhydrophobicity to a plethora of applications.

As enablers of electric vehicles, lithium-ion batteries are drawing much attention for their high energy density and low self-discharge. However, “range anxiety” has remained a significant hindrance to its further development. As an alternative to increasing capacity, fast charging seems a reasonable solution. However, challenges remain due to the conflict between high charging rate and excessive capacity loss. In the past, enormous efforts have been carried out to resolve the dispute between high charging rates and large capacity losses by either improving the battery design or optimizing the charging/discharging protocols. In contrast, this study proposes a novel control co-design framework with adaptive surrogate modeling to address the challenges and to generate the systematic optimal battery design and the corresponding charging protocol simultaneously. The proposed method is ideal for lithium-ion battery systems to offer the improved performances as compared with traditional sequential optimization approaches due to the integration of strong coupling effects between electrode design and control optimization. The integrated adaptive surrogate modeling technique allows model reduction for efficient optimal control and simulation solutions. Meanwhile, it preserves an accurate mapping from the first-principle model to the reduced-order model. A hybrid model like this captures the multiscale nature of the cell, that is, micro-scale parameters affect the macro-scale behavior. It reduces the computational cost significantly. The battery co-design problem is formulated as a nested problem, where the inner-loop solves an open-loop optimal control problem and the outer-loop optimizes the plant design variables. The results show that system-level optimal design can be obtained for minimized charging time at various levels of health requirement.

Silicon is one of the commonly used semiconductors for various industrial applications. Traditional silicon synthesis methods are often expensive and cannot meet the continuously growing demands for high-purity Si; electrodeposition is a promising and simple alternative. However, the electrodeposited products often possess nonuniform thicknesses due to various sources of uncertainty inherited from the fabrication process; to improve the quality of the coating products, it is crucial to better understand the influences of the sources of uncertainty. In this paper, uncertainty quantification (UQ) analysis is performed on the silicon electrodeposition process to evaluate the impacts of various experimental operation parameters on the thickness variation of the coated silicon layer and to find the optimal experimental conditions. To mitigate the high experimental and computational cost issues, a Gaussian process (GP) based surrogate model is constructed to conduct the UQ study with finite element (FE) simulation results as training data. It is found that the GP surrogate model can efficiently and accurately estimate the performance of the electrodeposition given certain experimental operation parameters. The results show that the electrodeposition process is sensitive to the geometric settings of the experiments, i.e., distance and area ratio between the counter and working electrodes; whereas other conditions, such as the potential of the counter electrode, temperature, and ion concentration in the electrolyte bath are less important. Furthermore, the optimal operating condition to deposit silicon is proposed to minimize the thickness variation of the coated silicon layer and to enhance the reliability of the electrodeposition experiment.

As one of the significant enablers of portable devices and electric vehicles, lithium-ion batteries are drawing much attention for their high energy density and low self-discharging rate. A major hindrance to their further development has been the “range anxiety”, that fast-charging of Li-ion battery is not attainable without sacrificing battery life. In the past, much effort has been carried out to resolve such a problem by either improve the battery design or optimize the charging/discharging protocols, while limited work has been done to address the problem simultaneously, or through a control co-design framework, for a system-level optimum. The control co-design framework is ideal for lithium-ion batteries due to the strong coupling effects between battery design and control optimization. The integration of such coupling effects can lead to improved performances as compared with traditional sequential optimization approaches. However, the challenge of implementing such a co-design framework has been updating the dynamics efficiently for design variations. In this study, we optimize the charging time and cycle life of a lithium-ion battery as a control co-design problem. Specifically, the anode volume fraction and particle size, and the corresponding charging current profile are optimized for a minimum charging time with health-management considerations. The battery is modeled as a coupled electro-thermal-aging dynamical system. The design-dependent dynamics is parameterized thru a Gaussian Processes model, that has been trained with high-fidelity multiphysics simulation samples. A nested co-design approach was implemented using direct transcription, which achieves a better performance than the sequential design approach.

Social networks are permeating all aspects of social life. As the number of users of social networks continues to expand and social relationships continue to enrich, the security risks faced by social network environments are becoming increasingly apparent. Most of the existing methods need to manually extract some descriptive features, which leads to the performance of the model depends largely on the quality of the extracted features. Aiming at the problem, this paper proposes an attention-based graph neural network, which uses the graph convolutional operator to capture the aggregation patterns in social networks automatically. We verified and discussed this proposed hypothesis on the cresci2017 dataset. Experimental results show that our attention based GNNs are better able to capture malicious account behavior than previous non-learning methods.

The traditional way of designing materials, including experimental measurement and computational simulation, are not efficient. Machine learning is considered a promising solution for material design in the recent years. By observing from previous data, machine learning finds patterns, learns from the patterns and predict the material properties. In this study, machine learning methods are used for discovering new cathode with better properties, includes crystal system learning and the property prediction. K-Folder cross-validation is used for finding the best training data with a limited dataset, nevertheless increasing the percentage of training data would ultimately result in better performance on prediction. It is found that, random forest gives the highest average accuracy in crystal system classification, meanwhile, extra randomized tree algorithm provides a higher averaged coefficient of determination and lower mean squared error in the regression model predicting electrical properties of cathodes. The random forest algorithm is chosen from a wide range of machine learning algorithms with the implementation of Monte Carlo validation. Based on the feature importance evaluation, oxygen contents are found to have the highest effects in determining capacity gravity and volume change in properties prediction.

Protein-based drug carriers are promising candidates for efficient drug delivery among the available potential colloidal carrier systems, due to their low cytotoxicity, abundance, renewability, diverse functional groups and interactions, and high drug loading capacity, etc. In this study, molecular dynamics (MD) simulations are performed to study the mechanisms of 11S molecule of soy protein as drug delivery vehicle to attach allyl isothiocyanate (AITC) and doxorubicin (DOX) drugs. The intermolecular interactions between protein and drugs are investigated; and the loading capacities of the protein molecules are calculated and compared with experiments. It is found that, for the AITC system, both nonpolar and polar residues of protein have the ability to adsorb AITCs; particularly, the polar residues serve as the primary active sites for the stable attachment of the drug molecules through the electrostatic (dipole-dipole) interactions. For the DOX system, however, the main driving force become the π-π stacking (the van der Waals interactions) among the aromatic rings of DOX and protein. In addition to pristine protein, different denaturation processes are found to be able to increase the exposure of active sites, therefore, enhance the loading efficiency of the protein carriers.

Lithium cobalt oxide (LCO) cathode is one of the most commonly used positive active materials for lithium ion batteries (LIBs). However, one reliability issue that limits its applications is the presence of moisture adsorbing on the LCO cathode surface, which may react with the electrolyte and lead to detrimental effects. In this study, a novel LCO thin film cathode is proposed, by adding a layer of high diffusion resistant (003) phase of LCO as the protective coating onto the diffusion favorable (110) phase, in order to simultaneously achieve good electrochemical performance and chemical stability of the LIB. A multi-physics-based finite element model is built to investigate the performance of the cathode and the influences of the design and operation variables, including the layout the two crystal phases, the fraction of each phase and the lithiation C rate. In addition, a Gaussian Process based surrogate model is developed, using the simulated results from the FE model as training data, to efficiently explore the design space of the cathode. It is found that, the 110//003 layout cathode could provide high capacity and good rate performance; meanwhile, the 003//110 design may lead to a largely reduced capacity, especially at high lithiation C rates.

Silicon-based anodes are one of the promising candidates for the next generation high-power/energy density lithium ion batteries (LIBs). However, a major drawback limiting the practical application of the Si anode is that Si experiences a significant volume change during lithiation/delithiation, which induces high stresses causing degradation and pulverization of the anode. This study focuses on crack initiation within a Si anode during the delithiation process. A multi-physics-based finite element (FE) model is built to simulate the electrochemical process and crack generation during delithiation. On top of that, a Gaussian process (GP)-based surrogate model is developed to assist the exploration of the crack patterns within the anode design space. It is found that the thickness of the Si coating layer, TSi, the yield strength of the Si material, σFc, the cohesive strength between Si and the substrate, σFs, and the curvature of the substrate, ρ, have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes and provides fundamental insights as building blocks to investigate more complex internal geometries.

Supported by life cycle assessment, plant fiber reinforced composites (PFRCs) have been acknowledged environmentally superior to man-made glass and carbon fibers reinforced composites (GFRCs and CFRCs). Endowed with an intrinsic multi-scale cell wall and lumen structure, plant fibers are expected to yield better energy absorption performances in their reinforced composites. In this research, the energy absorption properties: dielectric , thermal and acoustic properties, of PFRCs were evaluated via vector network analyzer, dielectric spectrometer, steady-state hot plate and acoustic impedance tube. It was demonstrated that PFRCs were beneficial for multi-functional applications, possessing lower dielectric constant and dielectric loss, lower thermal conductivity and higher sound absorption coefficient in comparison with man-made equivalents (carbon fiber and glass fiber reinforced composites). More specifically, the physiochemical structures of plant fibers (lumen, cell wall and crystalline structure) were correlated to analyze these energy absorption behaviors of PFRCs.

Silicon is a promising candidate for the negative electrode in lithium-ion battery. However, silicon-based electrodes experience large volume changes during the lithiation-delithiation process, which may lead to their failure and hinder their application. Delamination of the silicon layer from the current collector substrate is one of the critical failure modes; its effects on the performance degradation of Si anode need to be better understood. In this study, a multi-physics based finite element model is established to investigate the impact of Si layer delamination, where an artificial insulating interface is introduced to simulate the delamination phenomenon. It is found that delamination could result in a large amount of residual lithium within Si phase at the end of the delithiation process, leading to the capacity degradation of the Si anode. Furthermore, depth of delamination, Si layer thickness and charging/discharging C-rate are three critical influencing factors for the performances of the Si anode. With the increase of depth of delamination and Si layer thickness, the remaining useful capacity of the Si anode will gradually reduce. In addition, under high C-rate operating conditions, the capacity loss of the anode will be largely exaggerated by the presence of the delamination.

Hexagonal boron nitride (h-BN) nanomaterials, such as boron nitride nanotubes, boron nitride nanofibers, and boron nitride nanosheets, are among the most promising inorganic nanomaterials in recent years. Their unique properties, including high mechanical stiffness, wide band gap, excellent thermal conductivity, and thermal stability, suggest many potential applications in various engineering fields. In particular, h-BN nanomaterials have been widely used as functional fillers to fabricate high-performance polymer nanocomposites. Like other nanomaterials, prior to their utilization in nanocomposites, surface modification of h-BNs is often necessary in order to prevent their strong tendency to aggregate, and to improve their dispersion and interfacial properties in polymer nanocomposites. However, the high chemical inertness and resistance to oxidation of h-BNs make it rather difficult to functionalize h-BNs. The methods frequently used to oxidize graphitic carbon nanomaterials are not quite successful on h-BNs. Therefore, many novel approaches have been studied to modify h-BN nanostructures. In this review, different surface modification strategies were discussed including various covalent and non-covalent surface modification strategies through wet or dry chemical routes. Meanwhile, the effects of these surface modification methods on the resulting material structures and properties were also reviewed. At last, a number of theoretical studies on the reactivity of h-BNs with different chemical agents have been conducted to explore new surface modification routes, which were also addressed in this review.

Semantic segmentation network is able to detect multi-scale traffic sign effectively. However, probabilities that different scale features participate in decision are equal in the network. In this work, we propose a local multi-scale attention module based on channel-attention, which extracts local features generated by dilated convolution and weights scale features. We demonstrate that adopting local multi-scale attention module on a semantic segmentation network achieves a better performance for locating multi-scale traffic signs than state-of-art convolutional networks by the experimental results on a Chinese traffic sign dataset.

Silicon-based anode is one of the promising candidates for the next generation lithium ion batteries (LIBs) to achieve high power/energy density. However, the major drawback limiting the practical application of Si anode is that Si experiences significant volume change during its lithiation/de-lithiation cycles, which induces high stress and causes degradation and pulverization of the anode. This study focuses on the crack initiation performances of Si anode during the de-lithiation process. A multi-physics based finite element (FE) model is built to simulate the electrochemical process and crack generation during de-lithiation. On top of that, a Gaussian Processes (GP) based surrogate model is developed to assist the exploration of the crack initiation performances within the anode design space. It is found that, the thickness of the Si coating layer TSi, the yield strength σFc of Si material, the cohesive strength between Si and substrate σFs, and the curvature of the substrate ρ have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes.

The current study, inspired by the immunosuppressive property of rapamycin (Rapa) and the benefit of microspheres both as drug delivery system and cell carriers, was designed to develop an efficient Rapa delivery system with tunable controllability to facilitate its local administration. A capillary-based two-phase microfluidic device was designed to prepare monodisperse poly(lactide-co-glycolide) (PLGA) microspheres to load Rapa (PLGA-Rapa-M). The physical and chemical properties of PLGA-Rapa-M were characterized, and the Rapa loading capacity and release profile were explored. Chondrocytes were chosen as a cell model to evaluate the adhesion and proliferation on these microspheres. Controllability over the microsphere properties was illustrated. The PLGA-Rapa-M is averagely 63.91 μm in size with a narrow size distribution and a CV of 2.44%. The encapsulation efficiency of Rapa within microspheres via the current microfluidics was around 98%, and Rapa loading could be easily varied with a maximum value of ∼20%. The PLGA-Rapa-M has a sustained Rapa release duration of ∼3 months. These microspheres could not only successfully be used for Rapa sustained release but also as cell carriers for cell therapy since they can support the attachment/proliferation of chondrocytes. Hence, improved therapeutic index could be expected by using the current developed Rapa-release system.

Silicon-based lithium ion battery anodes are attracting considerable attention due to their high theoretical capacity. However, the significant volume change of silicon during lithiation/de-lithiation cycles restricts its application. A novel bi-continuous Si anode, designed to mitigate the effect of cycling-induced volume changes, is investigated in this study. The effects of the design parameters and the operating conditions of the anode are explored via multi-physics simulation model. It is found that, with the novel anode structure, battery charging C rate has little impact on the mechanical properties. However, the anode structural design parameters would largely influence the stress distribution on the anode. Thus, a Gaussian Processes (GP) based surrogate model is developed to assist the design optimization of the Si anode, while using the simulated results from a multiphysics simulation model as training data. An optimized Si anode design can then be extracted efficiently based upon the developed surrogate model.

Silicon anode is the most promising candidate for next generation lithium ion batteries. A major drawback limiting its application is the significant volume change during lithiation-delithiation process, which may cause material pulverization and capacity degradation. A novel 3D bi-continuous nanoporous structured Si anode, consisting of porous metal scaffolds and thin Si coating layers, was proven to be an effective method to tackle this issue; however, uncertainty and non-uniformity, inherited from the fabrication process, will be inevitably introduced as important considerations for the performances of the Si anode. In this paper, uncertainty quantification (UQ) analysis is performed on the structured Si anode system to evaluate the influences of various design variables on its performances and to find the design optimization strategy. The biggest hurdle in the UQ study is the computational cost; to mitigate this challenge, a Gaussian Process based surrogate model is constructed using finite element simulation results as training data. It is found that the performances of the anode are rather sensitive to the geometric parameters, i.e. scaffold non-uniformity and Si layer thickness, whereas the mechanical properties of the materials are relatively less important. Furthermore, the optimal design is proposed to minimize the stress concentration in the Si anode.

Silicon-based anodes are considered to be one of the most competitive candidates for next generation high power/energy density lithium ion batteries. A main drawback limiting their practical application is significant volume changes in Si during lithiation-delithiation, which induce high stresses and cause degradation and pulverization of the anode. A novel structured silicon anode, consisting of a metal scaffold coated with a layer of Si, has been introduced to address this issue and to improve the cycle life performance of Si anodes. In this paper, the impact of non-uniform metal scaffold structures on the mechanical performances of the Si anode is investigated. A multi-physics based finite element (FE) model is developed, where the geometric of the non-uniform structured anode is built based on experimental measurements. The stress distribution and deformation of the anode after full lithiation are predicted and compared with experimental results. It is found that large tensile stress concentrations are shown on the surfaces of Si layers; the volume-expansion induced stress is sensitive to the degree of non-uniformity of the scaffold. Sharp edges on the scaffold could result in high compressive stress. Rounding these edges is found to be a promising approach to reduce stress concentrations and improve the structural integrity of the anode.

Feature selection has become a key challenge in machine learning with the rapid growth of data size in real-world applications. However, existing feature selection methods mainly focus on numeric data, which will lead to quality loss when handling classification problems involving categorical variables. In this paper, we proposed an improvement of Bayesian Classifier with the sparse regression technology. To the best of our knowledge, this is the first attempt to extend sparse regression for directly process of categorical variables. We implemented the idea for the case of weighted naive Bayes classifier. The introduction of L1 regularized learning ensures the algorithm to retain only a minimal subset of variables for model building, while at the same time achieves a near-optimal decision hyper-plane, which leads to excellent performance in case of high dimensional or small sample size situations. We carried out benchmark test on five UCI benchmark categorical data sets, which proved that the proposed algorithm have competitive performances over the original weighted naive bayes classifier and several state-of-the-art feature selection methods including L1 logistic regression and SVM-RFE.

Sisal fiber reinforced composites with different amounts of resin inside the fiber lumens were prepared. The effects of resin penetration into fiber lumens on the mechanical and water-absorption properties of the composites were studied. Acoustic emission (AE) was used to characterize the fracture mechanisms of the composites by analyzing the different AE signals released during the failure process of the composites. Tensile, flexural and impact strength were all improved with the increasing of the amount of resin inside the fiber lumens due to the changes of the fracture modes. No significant effects on the tensile and flexural modulus were observed. The water absorption resistance of the composites could be improved when fiber lumens were filled with resin. The micro-failure morphologies of the composites possessing different amounts of resin inside the lumens were revealed with the aid of scanning electronic microscopy (SEM). It was found that the resin inside the lumens of the fibers strengthened the bonding between the micro fibrils within a single sisal fiber. Meanwhile, crack bridging effect of the penetrated resin could hinder the crack propagation to ensure fibers reaching their failure strain as much as possible.