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“Thick electrode using high voltage cathode” can be considered, since this research focuses on high voltage cathode materials.

International audience; Using a thick NMC811 (LiNi0.8Mn0.1Co0.1O2) electrode as an example, we present a macro-to nanoscale 2D and 3D imaging analysis approach coupled with 4D (space + time) computational modeling to probe its degradation mechanism in a lithium-ion bat-tery cell. Particle cracking increases and contact loss between parti-cles and carbon-binder domain are observed to correlate with the cell degradation. This study unravels that the reaction heterogene-ity within the thick cathode caused by the unbalanced electron con-duction is the main cause of the battery degradation over cycling. The increased heterogeneity in the system will entail more cathode regions where the degree of active material utilization is uneven, leading to higher probabilities of particle cracking. These findings shed light on the crucial role of the electronic and ionic transporta-tion networks in the performance deterioration of the thick cathode. They also provide guidance for cathode architecture optimization and performance improvement.

Transitioning toward more sustainable materials and manufacturing methods will be critical to continue supporting the rapidly expanding market for lithium-ion batteries. Meanwhile, energy storage applications are demanding higher power and energy densities than ever before, with aggressive performance targets like fast charging and greatly extended operating ranges and durations. Due to its high operating voltage and cobalt-free chemistry, the spinel-type LiNi0.5Mn1.5O4 (LNMO) cathode material has attracted great interest as one of the few next-generation candidates capable of addressing this combination of challenges. However, severe capacity degradation and poor interphase stability have thus far impeded the practical application of LNMO. In this study, by leveraging a dry electrode coating process, we demonstrate LNMO electrodes with stable full cell operation (up to 68% after 1000 cycles) and ultra-high loading (up to 9.5 mAh/cm2 in half cells). This excellent cycling stability is ascribed to a stable cathode-electrolyte interphase, a highly distributed and interconnected electronic percolation network, and robust mechanical properties. High-quality images collected using plasma focused ion beam scanning electron microscopy (PFIB-SEM) provide additional insight into this behavior, with a complementary 2-D model illustrating how the electronic percolation network in the dry-coated electrodes more efficiently supports homogeneous electrochemical reaction pathways. These results strongly motivate that LNMO as a high voltage cobalt-free cathode chemistry combined with an energy-efficient dry electrode coating process opens the possibility for sustainable electrode manufacturing of cost-effective and high-energy-density cathode materials.

A facile wet-chemical method to coat nano-LiCoO2 particles is investigated and established in this study. In this newly developed wet-chemical method, Al(NO3)3 is used as the Al source to form Al2O...

In this study, the long-term cycle behavior of nano-LiCoO2 cathodes over 500 cycles at high rates is investigated. Furthermore, the postmortem analysis of the cycled nano-LiCoO2 cathodes using a combination of X-ray absorption spectroscopy and X-ray diffraction is also conducted to shed light on the capacity decay mechanisms. It is found that crystalline nano-LiCoO2 after 500 charge/discharge cycles at 10C becomes amorphous, exhibits Co2+ species rather than the expected Co3+ at the fully lithiated condition, has longer Co–O bond length than that of pristine nano-LiCoO2 and micro-LiCoO2, and exhibits 53% specific capacity loss over 500 cycles at 10C. In addition, slow Li-ion intercalation and deintercalation at the electrode/electrolyte interface are found to be the rate-limiting step for nano-LiCoO2 during discharge and charge, respectively. Thus, to achieve the high-rate capability of LiCoO2, not only LiCoO2 particle sizes should be reduced to nanoscales but also the Li-ion intercalation and deintercala...

X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials' absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models., 33 pages, 5 figures

Summary The richness of anionic redox chemistry in the solid state offers new opportunities and a possible paradigm shift in energy storage. The excess capacity that goes beyond conventional theoretical values is attributed to the anionic redox in Li-rich transition metal oxide cathodes for Li-ion batteries. Their electrochemical behavior is thermodynamically determined by structural evolution. To better understand the electrochemical dependence on structural factors, we have induced structural modifications in pristine and electrochemically activated Li1.144Ni0.136Co0.136Mn0.544O2 through high-pressure treatment. A unique cyclical change of structural reordering is observed in the anionic redox-activated material during operando pressure sweep, characterized by a periodic evolution of superlattice peak intensity in synchrotron X-ray diffraction patterns. During the structural reordering period, the bulk compressibility of the material decreases, even becoming negative. These insights elucidate the structural flexibility and metastability of anionic redox-based materials, which can undergo large compressions and structural modifications while delivering good electrochemical properties.

${\mathrm{Na}}_{2}{\mathrm{Co}}_{2}{\mathrm{TeO}}_{6}$ has recently been proposed to be a Kitaev-like honeycomb magnet. To assess how close it is to realizing Kitaev quantum spin liquids, we have measured magnetization and specific heat on high-quality single crystals in magnetic fields applied along high-symmetry directions. Small training fields reveal a weak but canonical ferrimagnetic behavior below 27 K, which suggests that the previously established zigzag antiferromagnetic order, with collinear moments pointing along the zigzag chains, must be supplemented by $\mathbf{q}=0$ (e.g., N\'eel type in the absence of structural modulations) moment canting. Moderate fields in the honeycomb plane suppress the thermal transition at 27 K, and seem to partly reverse the moment canting when applied perpendicular to the zigzag chains. In contrast, out-of-plane fields leave the transition largely unaffected, but promotes another transition below 10 K, possibly also related to canting reversal. The magnetism in ${\mathrm{Na}}_{2}{\mathrm{Co}}_{2}{\mathrm{TeO}}_{6}$ is highly anisotropic and close to tipping points between competing phases.

Band topology, namely the global wavefunction structure that gives rise to the properties observed in the bulk and on the surface of crystalline materials, is currently a topic under intense investigation for both fundamental interest and its technological potential1–4. While topological band crossing in three dimensions was first studied for electrons in semimetals4–10, the underlying physical idea is not restricted to fermions11–15 and similar band structures of electromagnetic waves have been observed in artificial structures16. Fundamental bosonic excitations in real crystals, however, have not been observed to exhibit any counterparts. Here we use inelastic neutron scattering to reveal the presence of topological spin excitations (magnons) in a three-dimensional antiferromagnet, Cu3TeO6, which features a unique lattice of magnetic spin-1/2 Cu2+ ions17. Further to previous works on this system17,18, we find that the Cu2+ spins interact over a variety of distances, with the ninth-nearest-neighbour interaction being particularly strong. While the presence of topological magnon band crossing is independent of model details15, the far-reaching interactions suppress quantum fluctuations and make the magnon signals sharp and intense. Using accurate measurement and calculation, we visualize two magnon bands that cross at Dirac points protected by (approximate) U(1) spin-rotation symmetry. As a limiting case of topological nodal lines with Z2-monopole charges15,19, these Dirac points are new to the family of experimentally confirmed topological band structures. Our results render magnon systems a fertile ground for exploring novel band topology with neutron scattering, along with distinct observables in other related experiments. A neutron scattering study of the three-dimensional antiferromagnet Cu3TeO6 uncovers evidence for topological crossings in the magnon spectra of this system.

In this paper, we document a novel machine learning-based numerical framework to solve static and dynamic portfolio optimization problems, with, potentially, an extremely large number of assets. The framework proposed applies to general constrained optimization problems and overcomes many major difficulties arising in current literature. We not only empirically test our methods in U.S. and China A-share equity markets, but also run a horse-race comparison of some optimization schemes documented in (Homescu, 2014). We record significant excess returns, relative to the selected benchmarks, in both U.S. and China equity markets using popular schemes solved by our framework, where the conditional expected returns are obtained via machine learning regression, inspired by (Gu, Kelly & Xiu, 2020) and (Leippold, Wang & Zhou, 2021), of future returns on pricing factors carefully chosen.

The rapidly growing technological demand for lithium-ion batteries has prompted the development of novel cathode materials with high energy density, low cost, and improved safety. High voltage spinel, LiNi0.5Mn1.5O4 (LNMO), is one of the most promising candidates yet to be commercialized. The two primary obstacles for this material are the inferior electronic conductivity and fast capacity degradation in full cells due to the high operating voltage. By systematically addressing these limitations, we successfully develop a thick LNMO electrode with areal capacity loadings up to 3 mAh·cm−2. The optimized thick electrode is paired with a commercial graphite anode at both the coin cell and pouch cell level, achieving a full cell capacity retention as high as 72% and 78%, respectively, after 300 cycles. We attribute this superior cycling stability to careful optimizations of cell components and testing conditions, with a specific focus improving electronic conductivity and high voltage compatibility. These results suggest precise control of materials quality, electrode architecture and electrolyte optimization can soon support the development of a cobalt-free battery system based on a thick LNMO cathode (>4 mAh·cm2), which will eventually meet the needs of next-generation Li-ion batteries with reduced cost, improved safety, and assured sustainability.