An official website of the United States government
Cost-effective production of low cobalt Li-ion battery (LIB) cathode materials is of great importance to the electric vehicle (EV) industry to achieve a zero-carbon economy. Among the various low cobalt cathodes, Ni-rich lithium nickel cobalt manganese oxide (NCM/NMC)-based layered materials are commonly used in EVs and are attracting more attention of the scientific community due to their high specific capacity and energy density. Various synthesis routes are already established to produce Ni-rich NCM cathodes with uniform particle size distribution and high tap density. Continuous production of highly pure Ni-rich cathode materials with uniformity in inter/intra-particle compositional distribution is critically required. On the other hand, cation mixing, particle cracking, and parasitic side reactions at higher voltage and temperature are some of the primary challenges of working with Ni-rich NCM cathodes. During the past five years, several advanced modification strategies such as coating, doping, core–shell, gradient structure and single crystal growth have been explored to improve the NCM cathode performance in terms of specific capacity, rate-capability and cycling stability. The scientific advancements in the field of Ni-rich NCM cathodes in terms of manufacturing processes, material challenges, modification techniques, and also the future research direction of LIB research are critically reviewed in this article.
more »
« less
Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images
Abstract High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2(NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.
more »
« less
- PAR ID:
- 10504832
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Rechargeable aqueous Zn−MnO2batteries are promising for stationary energy storage because of their high energy density, safety, environmental benignity, and low cost. Conventional gravel MnO2cathodes have low electrical conductivity, slow ion (de‐)insertion, and poor cycle stability, resulting in poor recharging performance and severe capacity fading. To improve the rechargeability of MnO2, strategies have been devised such as depositing micrometer‐thick MnO2on carbon cloth and blending nanostructured MnO2with additives and binders. The low electrical conductivity of binders and sluggish ion (de‐)insertion in micrometer‐thick MnO2, however, still limit the fast‐charging performance. Herein, we have prepared porous carbon fiber (PCF) supported MnO2cathodes (PCF@MnO2), comprised of nanometer‐thick MnO2uniformly deposited on electrospun block copolymer‐derived PCF that have abundant uniform mesopores. The high electrical conductivity of PCF, fast electrochemical reactions in nanometer‐thick MnO2,and fast ion transport through porous nonwoven fibers contribute to a high rate capability at high loadings. PCF@MnO2, at a MnO2loading of 59.1 wt %, achieves a MnO2‐based specific capacity of 326 and 184 mAh g−1at a current density of 0.1 and 1.0 A g−1, respectively. Our approach of block copolymer‐based PCF as a support for zinc‐ion cathode inspires future designs of fast‐charging electrodes with other active materials.more » « less
-
Abstract Nickel‐rich layered materials LiNi1‐x‐yMnxCoyO2are promising candidates for high‐energy‐density lithium‐ion battery cathodes. Unfortunately, they suffer from capacity fading upon cycling, especially with high‐voltage charging. It is critical to have a mechanistic understanding of such fade. Herein, synchrotron‐based techniques (including scattering, spectroscopy, and microcopy) and finite element analysis are utilized to understand the LiNi0.6Mn0.2Co0.2O2material from structural, chemical, morphological, and mechanical points of view. The lattice structural changes are shown to be relatively reversible during cycling, even when 4.9 V charging is applied. However, local disorder and strain are induced by high‐voltage charging. Nano‐resolution 3D transmission X‐ray microscopy data analyzed by machine learning methodology reveal that high‐voltage charging induced significant oxidation state inhomogeneities in the cycled particles. Regions at the surface have a rock salt–type structure with lower oxidation state and build up the impedance, while regions with higher oxidization state are scattered in the bulk and are likely deactivated during cycling. In addition, the development of micro‐cracks is highly dependent on the pristine state morphology and cycling conditions. Hollow particles seem to be more robust against stress‐induced cracks than the solid ones, suggesting that morphology engineering can be effective in mitigating the crack problem in these materials.more » « less
-
Abstract Carbonaceous (e.g., limestone) and aluminosilicate (e.g., calcined clay) mineral additives are routinely used to partially replace ordinary portland cement in concrete to alleviate its energy impact and carbon footprint. These mineral additives—depending on their physicochemical characteristics—alter the hydration behavior of cement; which, in turn, affects the evolution of microstructure of concrete, as well as the development of its properties (e.g., compressive strength). Numerical, reaction-kinetics models—e.g., phase boundary nucleation-and-growth models; which are based partly on theoretically-derived kinetic mechanisms, and partly on assumptions—are unable to produce a priori prediction of hydration kinetics of cement; especially in multicomponent systems, wherein chemical interactions among cement, water, and mineral additives occur concurrently. This paper introduces a machine learning-based methodology to enable prompt and high-fidelity prediction of time-dependent hydration kinetics of cement, both in plain and multicomponent (e.g., binary; and ternary) systems, using the system’s physicochemical characteristics as inputs. Based on a database comprising hydration kinetics profiles of 235 unique systems—encompassing 7 synthetic cements and three mineral additives with disparate physicochemical attributes—a random forests (RF) model was rigorously trained to establish the underlying composition-reactivity correlations. This training was subsequently leveraged by the RF model: to predict time-dependent hydration kinetics of cement in new, multicomponent systems; and to formulate optimal mixture designs that satisfy user-imposed kinetics criteria.more » « less
-
Abstract Engineering a material's work function is of central importance for many technologies and in particular electron emitters used in high‐power vacuum electronics and thermionic energy converters. A low work function surface is typically achieved through unstable surface functional species, especially in high power thermionic electron emitter applications. Discovering and engineering new materials with intrinsic, stable low work functions obtainable without volatile surface species would mark a definitive advancement in the design of electron emitters. This work reports evidence for the existence of a low work function surface on a bulk, monolithic, electrically conductive perovskite oxide: SrVO3. After considering the patch field effect on the heterogeneous emitting surface of the bulk polycrystalline samples, this study suggests the presence of low work function (≈2 eV) emissive grains on SrVO3surface. Emission current densities of 10–100 mA cm–2at ≈1000 °C, comparable to commercial LaB6thermionic cathodes, indicative of an overall effective thermionic work function of 2.3–2.7 eV are obtained. This study demonstrates that perovskites like SrVO3may have intrinsically low work functions comparable to commercialized W‐based dispenser cathodes and suggests that, with further engineering, perovskites may represent a new class of low work function electron emitters.more » « less
