need batteries that are cheaper, safer, and more energy dense. [2] The World Economic Forum projects that the annual battery production revenue will grow to 300 billion dollars per year by 2030. [3] This demand from the market is compounded by the ambitious goals of the Paris Agreement on climate change, which require the transition to renewable energy conversion and storage technologies. [4] The development and large-scale production of sustainable high-performance batteries is one of the most intensely pursued technical research topics in the world today. Bold action is needed to meet this challenge. A coordinated and comprehensive effort spanning research and industry must be undertaken to develop battery technologies that achieve the strict performance standards set by the market in a sustainable and cost-effective way.Battery data plays an essential role in accelerating the development of new materials, cell designs, models, operating protocols, and manufacturing processes. [5] Recent advances in artificial intelligence (AI) methods such as, machine learning (ML) promise to greatly accelerate and improve insights into battery manufacturing, performance, and lifetime. [6][7][8][9] AI methods require vast amounts of data to Battery research initiatives and giga-scale production generate an abundance of diverse data spanning myriad fields of science and engineering. Modern battery development is driven by the confluence of traditional domains of natural science with emerging fields like artificial intelligence and the vast engineering and logistical knowledge needed to sustain the global reach of battery Gigafactories. Despite the unprecedented volume of dedicated research targeting affordable, high-performance, and sustainable battery designs, these endeavours are held back by the lack of common battery data and vocabulary standards, as well as, machine readable tools to support interoperability. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. A battery ontology offers an effective means to unify battery-related activities across different fields, accelerate the flow of knowledge in both human-and machine-readable formats, and support the integration of artificial intelligence in battery development. Furthermore, a logically consistent and expansive ontology is essential to support battery digitalization and standardization efforts, such as, the battery passport. This review summarizes the current state of ontology development, the needs for an ontology in the battery field, and current activities to meet this need.

Batteries & Supercaps www.batteries-supercaps.org Review doi.org/10.1002/batt.202200224 Lithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-theart, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.

In a comprehensive study, we demonstrate the performance and typical application scenarios for laboratory-based nano-computed tomography in materials research on various samples. Specifically, we focus on a projection magnification system with a nano focus source. The imaging resolution is quantified with common 2D test structures and validated in 3D applications by means of the Fourier Shell Correlation. As representative application examples from nowadays material research, we show metallization processes in multilayer integrated circuits, aging in lithium battery electrodes, and volumetric of metallic sub-micrometer fillers of composites. Thus, the laboratory system provides the unique possibility to image non-destructively structures in the range of 170–190 nanometers, even for high-density materials.

Several transition metal ions, like Fe , Co , Ni , and Zn complex to the ditopic ligand 1,4-bis(2,2':6',2''-terpyridin-4'-yl)benzene (L). Due to the high association constant, metal-ion induced self-assembly of Fe , Co , and Ni leads to extended, rigid-rod like metallo-supramolecular coordination polyelectrolytes (MEPEs) even in aqueous solution. Here, we present the kinetics of growth of MEPEs. The species in solutions are analyzed by light scattering, viscometry and cryogenic transmission electron microscopy (cryo-TEM). At near-stoichiometric amounts of the reactants, we obtained high molar masses, which follow the order Ni-MEPE≈Co-MEPE