General Motors (Poland)

Prediction of solute clustering kinetics in aged multicomponent alloys requires a quantitative understanding of complex vacancy-cluster interactions across multiple scales. Here, we develop an integrated computational framework combining on-lattice kinetic Monte Carlo (KMC) simulations, absorbing Markov chain models, and mesoscale cluster dynamics (CD) to investigate these interactions in Al-Mg-Zn alloys. The Markov chain model yields vacancy escape times from solute clusters and identifies a two-stage behavior of the vacancy-cluster binding energy. These binding energies are used to estimate residual vacancy concentrations in the Al matrix after quenching, which serve as critical inputs to CD simulations to predict long-term cluster evolution kinetics during natural aging. Our results quantitatively demonstrate the significant impact of quench rate on natural aging kinetics. Results provide insights to guide alloy chemistry, quench rates, and aging time at finite temperatures to control the evolution of solute clusters and eventual precipitates in aged multicomponent alloys.

With the rapid growth of electric transportation, the demand for Lithium-ion batteries has surged. To meet this need, numerous "GigaWatt" factories have been established over the past decade. The introduction of new materials, components, and manufacturing methods requires different approaches for effective assessment and quality control. As the industry shifts from lab-scale research to large-scale production, selecting the right characterization and measurement techniques becomes more complex.In this talk, we will overview the development flow of advanced battery technologies in the industry. We’ll explore the differences between lab-scale research, medium-level development, and large-scale manufacturing. Additionally, we’ll discuss how lab-scale physical and chemical methods can be adapted to help the process of electrode fabrication in higher manufacturing scale.

It is well known that batteries age over time. Many aging processes that result in battery cell degradation over life are electrochemical in nature and linked to the side reactions present in the battery cell during operation or during rest. Batteries are electrochemical devices, and as such, the overpotential that occurs when driving electrochemical processes inside the battery results in deterioration of the active material and interfaces, result in a reduced usefulness for the targeted application. Different aging mechanisms occur simultaneously in batteries, and an interplay between environmental conditions and operating strategy impact the prevalence of each mechanism. Growth of the Surface Electrolyte Interphase (SEI) is the primary cause of aging in lithium-ion batteries at early stages of life. The electrolyte chemistry and mechanical stress of active materials have a major impact on the thickening of the SEI over time. Usually, the SEI forms on the anode, which is primarily composed of graphite and occasionally combined with silicon or other higher energy density materials. The ions find it more difficult to pass through thicker SEIs due to the transport resistance present in the layer. The SEI expands as the battery ages, contributing to an internal resistance rise. Furthermore, aging can be further accelerated by lithium plating when the anode's electrochemical potential is equal to or less than that of metallic lithium, or when other thermodynamically favorable conditions are met.In this work, our three-dimensional microstructure based (3DMS) model is modified to capture aging phenomena of Li-ion battery [1]. The mechanisms of SEI growth and lithium plating are the primary focus. All equations that explain the growth of SEI and how the lithium is deposited on the surface were modified from Liu and Lu [2] and Pannala et. al [3]. The predictions of local thickness growth of SEI and plating on the anode active material and others electrochemical variables versus times will be reported. The experimental data of aging profiles at different C-rate, capacity fades as function of the cycle number, and the overall electrode capacity fade test [4] will be used for model validation. These simulations can be extended to battery cell level simulations and system level simulations to drive virtual design and analysis of the battery cell and determine if design changes need to be made in order to increase the likelihood of the battery meeting the end-of-life requirements. [5]

The increasing demand for energy storage systems such as Li-ion batteries requires an increasing supply of critical minerals such as graphite. Graphite is the predominant anode material due to its high theoretical capacity, electrochemical stability and long cycle life. In an effort to develop sustainable supply chains to meet the increasing demand, alternative carbon feedstocks for synthetic graphite are needed. One emerging feedstock is coal, owing to its abundance and cost.A comprehensive study of several coal-derived graphites synthesized from anthracite, bituminous and coal pitch will be presented in this work. Material properties such as BET surface area, morphology and microstructure are examined through SEM imaging, XRD and RAMAN spectroscopy. The electrochemical performance in half cells such as initial capacity loss and gravimetric capacity will be presented for all of the coal-derived graphites investigated. Single layer pouch cell data will be discussed for selected materials and compared to a commercial MesoCarbon, MicroBead (MCMB) graphite.The White Forest bituminous coal shows the most promise as a feedstock for synthetic graphite production. It has been demonstrated that this material has comparable performance to the MCMB graphite used in this study and has superior capacity retention. Commentary will also be provided on the future steps needed to optimize the coal-derived graphites presented in this work.

<div class="section abstract"><div class="htmlview paragraph">Optimization of the operating conditions for the proton exchange membrane fuel cell (PEMFC) is a challenging part as these are multi-input problems, however optimization is essential to achieve maximum stack efficiency, cost and weight reduction, and fuel utilization. In this article, an analytical model of the fuel cell is obtained by considering the Butler-Volmer and Nernst equations. Effect of operating pressure, temperature on the cell output voltage (<i>E<sub>cell</sub></i>), stack power (<i>P<sub>st</sub></i>), and stack efficiency (<i>η<sub>st</sub></i>) is analyzed to understand the behavior of the fuel cell at various operating conditions. It has been observed that the <i>P<sub>st</sub></i> increases with the increase in current density (<i>i</i>) whereas the <i>η<sub>st</sub></i> reduces with the increase in <i>i</i>. Hence, it is required to optimize the <i>P<sub>st</sub></i> and <i>η<sub>st</sub></i> so that maximum power can be extracted from the fuel cell stack without compromising in its efficiency and performance. For the multi-objective optimization study, eight input parameters are considered: operating pressure (<i>P</i>), operating temperature (<i>T</i>), transfer coefficient (<i>α</i>), internal resistance (<i>R<sub>i</sub></i>), catalyst specific area (<i>a<sub>c</sub></i>), catalyst loading (<i>L<sub>c</sub></i>), current density (<i>i</i>) and amount of reaction fuel, i.e., <i>H</i><sub>2</sub> (<span class="formula inline"><math display="inline" id="M1"><msub><mover accent="true"><mi>m</mi><mo>̇</mo></mover><msub><mi>H</mi><mrow><mn>2</mn><mtext mathvariant="italic">reacted</mtext></mrow></msub></msub></math></span>). Multi-objective genetic algorithm solver embedded with MATLAB is used for the optimization of the objective functions (<i>P<sub>st</sub></i> and <i>η<sub>st</sub></i>). The PEMFC provides <i>E<sub>cell</sub></i> = 0.67 V, <i>P<sub>st</sub></i> = 51 kW, and <i>η<sub>st</sub></i> = 57% while operating at the optimum operating conditions. The results indicate that the optimization of input parameters can lead to the better performance of the PEMFC as compared to the base operating condition (25<sup>°</sup><i>C</i> and 1 atm). The study can guide the engineers to select the appropriate operating conditions for a fuel cell to get better performance.</div></div>

A thin, porous, separator-supported reference electrode disposed between an anode and cathode is proposed as a practical, implementable solution for measuring individual electrode potentials in a commercial lithium ion cell. Its precise placement at the midpoint of the cell stack and permeable layer design allow for a simple formula for the calculation anode and cathode potential, while affecting the electrochemical performance of the cell only to the extent of adding one extra separator.In this study, we introduce a separator-supported reference electrode for both laboratory use and real-time, direct-feedback battery control systems in electric vehicles. Commentary on the development, manufacturing, implementation, and use for automotive grade battery cells will also be provided.References U. Janakiraman, T. R. Garrick, and M. E. Fortier, J. Electrochem. Soc., 167, 160552 (2020).T. R. Garrick, Y. Zeng, J. B. Siegel, and V. R. Subramanian, J. Electrochem. Soc., 170 113502 (2023)B. J. Koch, J. Gao, A. Zhang, R. Taha, and T. R. Garrick, Journal of The Electrochemical Society, (Under Review).T. R. Garrick, J. Gao, X. Yang, and B. J. Koch, J. Electrochem. Soc., 168, 010530 (2021).J. Gao, B. J. Koch, and T. R. Garrick, (2023) US Patent App US20230091154A1.H. Zhang, T. R. Garrick, and B. J. Koch, (2024) US Patent App US20240072565Brian J. Koch et al 2024 J. Electrochem. Soc. 171 123505

As the shift to renewables continues, new sources of critical materials such as graphite are needed. One potential feedstock being investigated is coal. Coal-derived graphite shows promise as an anode material for Li-ion batteries1. In this work, a white forest coal-derived graphite will be studied, and its physical and electrochemical properties will be presented. In an effort to reduce the particle size and modify the coal-derived graphite, ball milling is used. Through this additional processing, the physical and electrochemical properties of the graphite are changed. These differences will be presented in this work.The physical properties that will be examined for the milled and unmilled graphites include microstructure such as d-spacing and crystallite size, BET surface area and morphology through SEM imaging. The electrochemical performance of the materials will also be discussed. The performance will be measured through initial capacity loss (ICL), gravimetric capacity, rate capability and cycle life.

High-capacity Ni-rich cathodes hold considerable promise in advancing both high-energy Li-ion batteries (LIBs) and all-solid-state Li batteries (ASSLBs). Yet, lattice volume changes induced by the H2–H3 phase transition lead to structural degradation. It has been demonstrated that doping approaches can enhance structural stability. Nevertheless, the selection of an appropriate dopant is of considerable importance for the design of high-performance Ni-rich materials with high-entropy doping. Furthermore, the feasibility of high-entropy doped Ni-rich cathodes in ASSLBs has not been reported to date. In this study, leveraging the chemically competitive doping mechanism of Mg, Al, Ti, Nb, and Mo elements, we propose a high-entropy doped LiNi0.8Co0.1Mn0.05Mg0.01Al0.01Ti0.01Nb0.01Mo0.01O2 (HE-NCM) to stabilize Ni-rich cathodes. In-situ X-ray diffraction confirms that the synergistic effect of multiple dopants in high-entropy doping significantly alleviates the H2–H3 phase transition and reduces lattice volume changes, which contribute to the absence of microcracks and improved bulk/interface stability. In ASSLBs, the HE-NCM@LiNbO3 maintains an outstanding capacity retention of 84.5% even after 1700 cycles, demonstrating the feasibility of high-entropy doped Ni-rich cathodes when matched with a sulfide solid-state electrolyte. The application of high-entropy doping methodology for the development of advanced rechargeable batteries has been shown to provide a novel perspective on the improvement of the structural robustness and interfacial compatibility of Ni-rich cathode materials.

The emergence of machine learning (ML) techniques has significantly improved the accuracy and efficiency in materials characterization. This paper reviews the application of ML algorithms in microstructural analysis and defect detection processes of aluminum castings. By leveraging ML methods, multiple ML models were trained to automatically identify and classify different types of casting defects and microstructural features. Advanced image processing techniques, combined with convolutional neural networks (CNNs), enable the detection of casting defects such as shrinkage porosity, oxides and multiscale microstructure features (i.e., eutectic phases and secondary dendrite arm spacing of aluminum). This study highlights the advantages of the developed ML models in the accuracy and reduction of measurement time in the lab and reducing the reliance on manual analysis and subjective judgment. The findings emphasize the significant impact of ML techniques on metallurgical research and industrial applications, enhancing the reliability and performance of material analysis tools.