Kinked silicon nanowires-enabled interweaving electrode configuration for lithium-ion batteries

A tri-dimensional interweaving kinked silicon nanowires (k-SiNWs) assembly, with a Ni current collector co-integrated, is evaluated as electrode configuration for lithium ion batteries. The large-scale fabrication of k-SiNWs is based on a procedure for continuous metal assisted chemical etching of Si, supported by a chemical peeling step that enables the reuse of the Si substrate. The kinks are triggered by a simple, repetitive etch-quench sequence in a HF and H2O2-based etchant. We find that the inter-locking frameworks of k-SiNWs and multi-walled carbon nanotubes exhibit beneficial mechanical properties with a foam-like behavior amplified by the kinks and a suitable porosity for a minimal electrode deformation upon Li insertion. In addition, ionic liquid electrolyte systems associated with the integrated Ni current collector repress the detrimental effects related to the Si-Li alloying reaction, enabling high cycling stability with 80% capacity retention (1695 mAh/gSi) after 100 cycles. Areal capacities of 2.42 mAh/cm2 (1276 mAh/gelectrode) can be achieved at the maximum evaluated thickness (corresponding to 1.3 mgSi/cm2). This work emphasizes the versatility of the metal assisted chemical etching for the synthesis of advanced Si nanostructures for high performance lithium ion battery electrodes

Ab initio study of lithium dynamics in silicon-based materials for battery applications

Silicon-based materials are promising candidates to replace commercial graphite-based anodes in lithium ion batteries. Silicon-based anodes can possess 10 times more capacity than graphite, but with a humongous volume expansion of 300%. At the atomic scale, the rate of lithium migration plays a key role in controlling the volume expansion. Thus, understanding the lithium atom migration and the structural evolution of Si atoms at atomic scale is essential to mitigate volume expansion and achieve maximum capacity. In this thesis, we approached this problem from two ends: 1) Studying diffusion of lithium in bulk silicon and lithiated LiSi from first-principles methods within the transition state theory framework, and 2) Analysing delithiation dynamics through first-principles followed by an empirical potential to study large supercells. Our approach provides a better agreement with the experimental data as compared to the previous experimental or theoretical data with useful insight on the diffusion mechanism. The impact of quantum tunnelling effects and anharmonicity on lithium diffusion have also been estimated for the first time and are moderate for Si and negligible for the LiSi case. A novel first-principles-based approach has been developed to study the step-by-step delithiation of lithium atoms, which can capture the change in the local environment of silicon during delithiation. The analysis of the local environment of delithiated Si suggests that most of the delithiated silicon atoms (75%) returns back to the tetrahedral coordination. The characterization of delithiated silicon obtained by empirical potential shows strong resemblance with amorphous Si.(FSA - Sciences de l'ingénieur) -- UCL, 202

Toward high-performance energy and power battery cells with machine learning-based optimization of electrode manufacturing

International audienceThe optimization of the electrode manufacturing process is important for upscaling the application of Lithium -Ion Batteries (LIBs) to cater for growing energy demand. LIB manufacturing is important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery applications by proposing a data-driven approach supported by a deterministic machine learning-assisted pipeline for bi-objective optimization of the electrochemical performances. This pipeline allows the inverse design of the process parameters to adopt to manufacture electrodes for energy or power applications. This work is an analogy to our previous work that addressed the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations, and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic model to implement fast optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes

Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations

The optimization of the electrodes manufacturing process is critical to ensure high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. LIB electrode manufacturing is a complex process involving multiple steps and parameters. We have shown in our previous works that 3D-resolved physics-based models constitute very useful tools to provide insights into the impact of the manufacturing process parameters on the textural and performance properties of the electrodes. However, their high-throughput application for electrode properties optimization and inverse design of manufacturing parameters is limited due to the high computational cost associated with these models. In this work, we tackle this issue by proposing a generalizable and innovative approach, supported by a deterministic machine learning (ML)-assisted pipeline for multi-objective optimization of LIB electrode properties and inverse design of its manufacturing process. Firstly, the pipeline generates a synthetic dataset from physics-based simulations with low discrepancy sequences, that allows to sufficiently represent the manufacturing parameters space. Secondly, the generated dataset is used to train deterministic ML models to implement a fast multi-objective optimization, to identify an optimal electrode and the manufacturing parameters to adopt in order to fabricate it. Lastly, this electrode was successfully fabricated experimentally, proving that our modeling pipeline prediction is physical-relevant. Here, we demonstrate our pipeline for the simultaneous minimization of the electrode tortuosity factor and maximization of the effective electronic conductivity, the active surface area, and the density, all being parameters that affect the Li+ (de-)intercalation kinetics, ionic, and electronic transport properties of the electrode

Kinked silicon nanowires-based electrode configuration for lithium-ion batteries

Silicon has been in the spotlight of the next generation anode materials due to its distinctive Li-related features such as the ability to form Li rich compounds, corresponding to an exceptional capacity of 3579 mAh/g at low working voltages. In exchange, many engineering concerns are associated to the structural deformation during lithium alloying that can lead to material pulverization as well as limited cycling life. We detail on an anode configuration based on interconnected kinked Si nanowires (k-SiNWs) fabricated by metal assisted chemical etching. A chemical peeling step is introduced to facilitate the separation of the etched k-SiNWs from their originating Si substrate. The three-dimensional (3D) interconnected k-SiNWs-based anode materials are assembled, using a conventional vacuum filtration technique, with multi-walled carbon nanotubes. The k-SiNWs are expected to be more robust to lithiation-induced stresses as they typically behave like microsprings. In addition, the kinks provide interlocking joints resulting in a fairly resilient anode material. Furthermore, the evaluation of the mechanical properties of the anode assemblies revealed a foam-like architecture that benefits from high porosity. These 3D Si-based assemblies were galvanostatically cycled in conventional electrolytes and ionic liquids. The electrochemical evaluation of the 3D Si-based anode assemblies showed valuable cycling life in ionic liquids compared to conventional electrolytes, retaining 70% of the initial capacity and displaying an average Coulombic efficiency of 97.5% after 50 cycles. Further performance improvements were obtained by coating the k-SiNWs with a 33 nm Ni coating. The exemplary mechanical behavior and electrochemical robustness were assigned to the kinked morphology of the SiNWs