In situ cathode-electrolyte interphase enables high cycling stability of Co-free Li-rich layered cathodes

Despite the extensive research in Li-rich layered oxides (LLOs), which are promising candidates for high-energy density cathodes, their cycle life still cannot meet the real-world application requirements. The poor cycle performance arises from the electrolyte decomposition at high voltage, resulting in damage and subsequent surface-initiated conversion of the cathode from layered to spinel phase. This problem is even more challenging for Co-free LLO cathodes. Here, we report a one-pot synthesis of in situ carbonate-coated nanostructured Co-free LLO (Li2CO3@LLO) through a polyol-assisted method. This inorganic coating suppresses oxygen release, provides good Li–ion transport, and protects the cathode from adverse reactions with the electrolyte. The obtained material exhibits excellent long-term stability, with 76% capacity retention after 1000 cycles at a 0.2 C rate without any Co addition, demonstrating a path forward for using LLOs as a next-generation Li–ion battery cathode

Toward understanding the effects of solution heat treatment, Ag addition, and simultaneous Ag and Cu addition on the microstructure, mechanical properties, and corrosion behavior of the biodegradable Mg–2Zn alloy

In this study, the effects of adding silver (0.2 and 0.6 wt%) and copper (0.1 wt%) antibacterial elements, on the microstructure, mechanical properties, and degradation behavior of the as-cast Mg–2Zn alloy were investigated. The obtained results indicate that both Ag and Cu showed significant grain refinement effects in the as-cast condition. The MgZn precipitates were formed in the as-cast Mg–2Zn–0.2Ag alloy, which contained a small amount of Ag. Increasing the Ag content to 0.6 wt% resulted in formation of the Mg54Ag17 phase. Simultaneous addition of 0.2 wt% Ag and 0.1 wt% Cu to the Mg–2Zn alloy caused the ternary Mg(Zn,Cu) precipitates to form. Solution-treated Mg–2Zn and Mg–2Zn–0.6Ag alloys had a single-phase microstructure, while some Mg(Zn,Cu) precipitates remained in the Mg–2Zn–0.2Ag–0.1Cu alloy after solution treatment. Shear punch test showed 15, 12, and 23% increases in ultimate shear strength values of the as-cast Mg–2Zn–0.2Ag, Mg–2Zn–0.6Ag, and Mg–2Zn–0.2Ag–0.1Cu alloys compared to the Mg–2Zn alloy, respectively. The hydrogen evolution rate of the as-cast Mg–2Zn–0.2Ag, Mg–2Zn–0.6Ag, and Mg–2Zn–0.2Ag–0.1Cu alloys were found to be 38, 90 and 70% higher than the as-cast Mg–2Zn alloy, respectively. However, the solution treatment reduced the degradation rate significantly. Hence, it was found in this investigation that adding Ag and Cu elements would be so effective for improving different properties of the Mg–Zn alloys by using appropriate solution heat treatment

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring

Atomistic study of the effect of crystallographic orientation on the twinning and detwinning behavior of NiTi shape memory alloys

Understanding the effect of crystallographic orientation on the twinnin/detwinning mechanisms in NiTi shape memory alloys at an atomistic scale can help to control and tune the mechanical properties and failure behavior of such materials. In this work, we employed classical molecular dynamics (MD) and density functional theory (DFT) computational methods to better understand how twinning and detwinning occurs through a combination of slip, twin, and shuffle on 〈0 1 0〉, 〈1 1 0〉, and 〈1 1 1〉 crystallographic orientations under uniaxial tensile test. Elastic constants including Young's Modulus (E), Bulk modulus (B), Poisson’s ratio (ν), and Shear Modulus (G) are obtained and computed for resultant stress-induced martensite variants as a function of crystallographic orientation using DFT calculations. In addition, computational nanoindentation tests are carried out using MD simulations to evaluate the effect of crystallographic orientation on the twinning and detwinning characteristics in martensite in NiTi alloys under sphere indenter, both qualitatively and quantitatively. Based on a careful polyhedral template matching (PTM) and dislocation analysis (DXA) by taking into account the textures, it is determined that the microscopic stress-strain and load-displacement responses strongly depend on the crystallographic orientation. Our findings reveal that the size of twinned and detwinned zones in martensite increases in the order of 〈1 1 1〉 < 〈0 1 0〉 < 〈1 1 0〉. Based on DFT results, against 〈1 1 1〉 direction, abrupt changes in the free energy-strain curves occurs at 4% strain in 〈0 0 1〉, and 8% strain in 〈1 1 0〉 directions. The twinning and detwinning mechanisms are controlled by monoclinic martensite (B19′) → orthorhombic martensite (B19) phase transformation in 〈1 1 0〉 orientation and by body-centered orthorhombic martensite (BCO) → an intermediate structure (B19″) → monoclinic martensite (B19′) phase transformation in 〈0 0 1〉 orientation. Finally, the predicted orientation-dependent critical energy release rate is analyzed to examine the effect of the twinning and detwinning process on the fracture toughness of the material. Our results show that reducing the density of twins results in increasing the critical energy release rate. Therefore, the fracture stress intensity increases in the order of 〈0 0 1〉 < 〈1 1 0〉 < 〈1 1 1〉.The authors would like to declare their appreciation to the Iran National Science Foundation (INSF) for support of the research under Grant 96006124.Peer reviewe