dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods Phase-field simulations of PTO/STO heterostructures are carried out using the FEniCS software package, version 2019. 2. 0. dev0 (https://bitbucket. org/fenics-project, accessed on 8 April 2021, USA)  [19]. The appearance of the domain structure is described by the overdamped equation, defining the relaxation of polarization, P, to the state minimizing the system's free energy, F , to its ground state with time, t. γ ∂P ∂t = δF δP. (1) Equation (  1 ) is closed by the Poisson equation, taking into account the electrostatic effects ε 0 ε i ∇ 2 ϕ = ∇ • P. (2) Here, ϕ is the electric potential and ε i 10  [20]  is the background dielectric constant due to nonpolar ions. The free energy functional describing the ferroelectric phase transition in a strained PTO has the form of the Ginzburg-Landau functional for a ferroelectric layer subjected to an electric field  [21] , and is written in the form  [22]  (the gradient and electrostatic terms are included): F = FdV, F = a * i (u m , T)P 2 i + a * ij P 2 i P 2 j + a ijk P 2 i P 2 j P 2 k i≤j≤k + 1 2 G ijkl (∂ i P j )(∂ k P l ) + (∂ i ϕ)P i - 1 2 ε 0 ε i (∇ϕ) 2 , ( 3 ) where F is the free energy density; each of the indices i, j, k, l cyclically takes values of 1, 2 or 3 (or x, y, z).
Elixabete Ayerbe is Team Leader in Modelling and Post-mortem analysis in the Materials for Energy Unit of CIDETEC Energy Storage, coordinating the activities related to multiphysics and data-driven models, as well as the parameterization and post-mortem analysis for Li-ion and advanced Li-ion batteries for industrial partners and national and international entities. She is currently coordination H2020 DEFACTO project and represents the modelling activity of CIDETEC in several H2020 EU projects, and leads the research area of Manufacturability in the Battery2030+ initiative.
1) Digital manufacturing framework layer contains data collected from the physical manufacturing plant and deals with the communication network. The communication between a DT and its counterpart in the physical space relies on bi-directional and real-time data sharing. This framework collects data in real time through cost-effective sensors and stores the data in a Cloud space to promote the remote management of the plant. 2) Models' layer describes, provides understanding, and predicts the twin's operation. For this evaluation, we draw a distinction between process and machine models. Process models are used to describe the relationship between the process and structural parameters, while machine models refer to representation of machines and equipment in the manufacturing process chain.
g. , capacity and inner resistance). In this case, industrial data acquisition systems like the Supervisory Control and Data Acquisition system (SCADA) or Manufacturing Execution System (MES) were used. The data was extracted and transformed into a form that was stored and further processed in a data warehouse. Contrarily to a database, which only stores the data, the main function of a data warehouse is to gather, merge and store the data in the way that it would be accessible for further usage. Turetskyy et al. [43]  outlined a holistic approach to represent the entire production cycle of battery cell manufacturing in a datadriven manner. Furthermore, the authors described the combination of automated and manual data acquisition, as well as merging of data from different sources, of different communicational protocols, and of different formats toward accessibility, convenient data management, and visualization.
The AAS forms the digital basis for autonomous systems and AI, and it enables the implementation of digital twins. The AAS comprises a variety of submodels, which describe all the information and functionalities of a given asset. This includes information like the features, characteristics, properties, status, parameters, measurement data, and capabilities of an asset. For developers, a free open-source tool called the AASX Package Editor is available to create and edit Asset Administration Shells for a given use case in XML and JSON formats. The AASX Explorer can be downloaded as compiled software licensed under Eclipse Public License 2. 0 (EPL 2. 0). The Open Platform Communications Unified Architecture (OPC UA),  [62]  created by OPC Foundation, is another example of standardized communication protocols.
Ontologies for Battery Manufacturing An ontology is a data model that encompasses the knowledge about a topic or domain. It describes a domain as a set of concepts and the relationships between them, and provides a common standardization platform for exchange of data and information processing by humans and machines. [72] Once established in an electronically readable form, the ontology provides a formal categorization scheme to facilitate digitalization of industrial technologies and support the integration of Artificial Intelligence (AI) and Big Data approaches. The uses of ontologies in the digitalization of battery manufacturing mainly include: (i) Defining a standard vocabulary for battery classes and properties to support consistency in digital assets, (ii) Facilitating the development of co-simulation frameworks by defining reason-based interfaces between sub-models;  [73]  and (iii) Accelerating AI optimization by allowing machine reasoning over large datasets.
[82] ypical commercial solutions focus on implementing workflows to gather battery data from different characterization equipment, which may have different standards and formats for storing data depending on the manufacturer. This data is then parsed and stored into a semantic query language (SQL), or similar database to support semantic querying of large datasets. Featurizers are often included to extract out relevant indicators of battery performance from the dataset and may be combined with ML-driven analytics or physics-based models. The research battery data community is creating similar frameworks to support digitalization of battery discovery, design, and development. This has resulted in a collection of loosely complimentary software to address different challenges in the field. These include examples such as Kadi4Mat, Galvanalyser, BEEP, PyBaMM, and the Battery Archive.
Kadi4Mat  [83]  is developed by the Karlsruhe Institute of Technology (KIT) to support the easy access, exchange, and interoperability of materials data. Kadi4Mat is a web-based application that combines the features of an electronic laboratory notebook (ELN) and data repository. The web-based node editor enables users to define simple workflows that can be executed locally. The infrastructure, shown in Figure  3 , includes tools to upload, manage and exchange data, set flexible metadata schemes, and establish automated workflows. Although ostensibly geared toward the field of material science, the development of Kadi4Mat is supported by some of the leading German projects and clusters on battery research including FestBatt  [84]  and POLiS. [85] Kadi4Mat is open-source software, currently available for download and use in the community under Apache License 2.
0. Galvanalyser is a database solution for battery test data, which collects raw data from a variety of sources, parses it into a common format, and stores the data in a single PostgreSQL database. [87] Developed by researchers at Oxford University, Galvanalyser streamlines the process of gathering and querying data from different testing equipment. It supports access to the data through a web-based interface, which allows the user to search for datasets with certain properties and visualize them within the app. Open-source Python packages, like the battery evaluation and early prediction software package (BEEP)  [88]  and Python battery mathematical modelling (PyBaMM)  [89]  offer additional functionalities. BEEP is a framework for the management and processing of high-throughput battery cycling data, while PyBaMM is designed to allow for the simulation of cycling protocols in silico.
The ARTISTIC project offers free online services to launch manufacturing simulations from an Internet browser. [92, 93] Other example is the DEFACTO project,  [94]  whose target is to create a basis for the digitalization of the battery manufacturing process, by developing multiphysics and multiscale modelling tools to improve the understanding of cell material behaviour and cell manufacturing process, and their impact on battery cell ageing. A further example of a commercial tool that stochastically generates electrode mesostructures is the software GeoDict. [95] 8].
Thus, these analytics are important to be combined with a DT to reduce system downtime, improve production efficiency, and perform predictive quality maintenance. At the machine level, defect images can be identified using optical systems. In the production of goods, AI can be used to increase the performance of a production plant by means of collected production data or to find an optimal parameter configuration. [171, 172] In this sense, a system that correlates the monitored process parameters with the quality characteristics, and independently learns to adjust the process variables to achieve the quality characteristics, is not yet known in battery cell production. [173] Thus, such system would offer great potential with respect to fast and flexible commissioning or retrofitting of production facilities.
Encompassing high throughput experimentation, ML and physical modeling constitutes also an approach that has been proposed by Duquesnoy et al. [97]  to develop digital twins of the electrode manufacturing process. The authors proposed an electrode stochastic generator able to predict how manufacturing parameters impact the electrode mesostructure in 3D. This is possible because the generator is informed by using both experimental and physics-based data (the latter arising from CGMD and DEM simulations as described above). Such generator can produce a massive number of electrode mesostructures that can be characterized in terms of textural and physical properties, like the surface area of contact between AM and pores, tortuosity of the porous media, conductivity, etc. This allows building a dataset of type "input = manufacturing parameters, outputs = electrode properties", which can be used for training ML models able to predict electrode properties from manufacturing parameters in the form of classifications or regressions.
Another relevant initiative is the OntoCommons initiative  [203]  which is dedicated to the standardization of data documentation across all domains related to materials and manufacturing. OntoCommons takes a similar approach to the Industrial Ontology Foundry  [204]  (IOF) by first defining a top-level reference ontology, which acts as a source to grow industrial domain ontologies that use common terms and follow common principles. Battery domain ontologies BattINFO and BVCO, previously mentioned, are based on this initiative. The digitalization of battery field is gradually following the same trend observed in many other sectors, where the growth of these kind of projects and efforts is increasing. At European level, besides the project ARTISTIC  [91]  funded by the European Research Council and the project DEFACTO  [94]  funded by the H2020 programme, both mentioned above, the project eLAB: Big Data in battery production by RWTH,  [205]  boosted by the Platform Industrie 4.
Challenges and Opportunities The process of digitalization is characterized by the transfer of analogue product features into digital values to enable an electronical and informational transfer, storage, and processing of the data. Figure  10  shows a representation of the digital twin of the battery manufacturing plant that mimics the real manufacturing plant and is capable to make intelligent decisions over the plant. In the figure, the main three pillars of the DT can be identified: the real manufacturing plant (left side of the figure), the virtual replica (right side of the figure) and the connection of data and information (represented by lines) that ties the virtual replica and the real plant together. When operating, massive data may be extracted through the use of sensors from the physical assets, which can be transferred to the cloud for information systems to process.
Then, big data analytics can support system management and optimization including supervision and control, having the means to interact with the physical asset through the actuators. By a successful integration of digitalization approaches in an automated production line, the overall costs of the battery cell can be significantly reduced. Hereafter, we summarize the main challenges to be overcome to move toward digitalization of the LIB cell manufacturing plant. Firstly, the use of sensors and actuators can increase the quality of the produced batteries, as well as the monitoring and control of the whole process. At this moment, their main purpose is mostly limited to basic safety functions, defect detection, automatic calibration of workpieces or simple measurement methods. However, as highlighted in the previous sections, the networking of intelligent sensors will enable enhanced functionality of existing production facilities.
Examples of norms and standards providing properties for submodels of the Administration Shell  [198]. and data processing needs to be integrated in an efficient and sustainable manner. This full digital representation of the production system, including the sensors and actuators and the semi-finished products of the battery cell and of course the final product battery cell itself, will enable the prediction of the impact of changes in production on the structure of the battery components and consequently on the final cell performance. Within the DT of a LIB manufacturing plant, the flow of data begins with the measurement of quantities via sensors. In the next step, data must be integrated into a common framework for data storage and processing to extract the necessary insight. This represents a challenge for a battery gigafactory, as the data generated is both voluminous and heterogeneous.
Such explicit consideration of the carbon additive and the polymeric binder as separate materials will permit a deeper analysis of their respective roles during the electrode operation. On the other hand, the expected large deployment of ML models for battery manufacturing faces a new challenge associated with the availability of public databases. Such databases are needed for appropriate training and testing of such models. Academic initiatives, such as the ARTISTIC project,  [91, 92]  is already making significant amount of battery manufacturing experimental and modeling data openly available and offer free online services for battery manufacturing simulations from an Internet browser. While this trend of giving open access to data helps in generating large volumes of data, and thus in building data-driven models, it is felt that it should be followed by the academia and industry as a whole in order to facilitate and accelerate the data-driven models implementation.
Another challenge constitutes the systematically reporting of quantified uncertainties and statistical errors in the experimental databases. This is important to develop ML models able to make predictions with high degree of confidence. Still, ML models will be as good as the quality of the data used to train them. An important challenge here is the setting of standards for battery manufacturing data reporting. In this regard, a recent text mining study by El-Bouysidy et al. [212]  carried out on 13000 scientific articles on lithium ion and sodium ion batteries found that many articles do not report important manufactured electrode properties that are needed to reproduce reported experiments. Computational workflows integrating at the same time physics-based and data-driven models to simulate the different battery manufacturing steps also constitute another challenge.
In this scenario, battery manufacturers will likely be asked to mint the BIGP for their cells, which again underscores the need for common data structures to support interoperability, not only internally within a gigafactory, but across the entire battery value chain. All in all, the next big leap in smart battery manufacturing requires cooperation and compromise among scientists and manufacturers. The biggest roadblock to proper Industry 4. 0 implementation is the ability for machines and equipment to work together and share data. This can be avoided through standardization of machine-to-machine communication, IIoT communication and considering software database and communication protocols for MPR, ERP, MES, etc. Once this is achieved, then battery manufacturing will turn into a fully modular and interchangeable set of software and hardware.
• Building flexible digital twin capable to be adapted to new and disruptive manufacturing and advanced chemistries. • A reliable working of any DT will require data arising from different components, such as sensors and models; therefore, advanced communication technologies for keeping the data always synchronized will be required to make sure the two twins remain synchronized, data remains protected and secured. • Additionally, we would like to highlight the importance of standardization. In a fully connected and interactive battery manufacturing plant, different physical assets will be interacting with each other, and the corresponding digital twins will also have to interact with the physical assets. To facilitate these interconnections, there will be a need for standards cutting across different domain areas.
Acknowledgements E. A. , M. B. , S. C. , A. A. F. , and J. R. acknowledge support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 957213  ( BATTERY2030PLUS ). E. A. , S. C. , and A. A. F. acknowledge support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 957189  ( BIG-MAP ). E. A. acknowledges support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 875247  (DEFACTO). A. A. F. acknowledges the European Union's  Horizon 2020 research and innovation program  for the funding support through the  European Research Council  (grant agreement No. 772873 , " ARTISTIC " project). A. A. F. acknowledges the  Institut Universitaire de France  for the support.
Single lamellae identification The intrinsic constitutive response of the annulus under UA loading was experimentally determined by Skaggs et al. [38]  and Holzapfel et al. [39]  on single annulus lamellae stretched along the fibers main direction. Fig. 4  shows the computed results for the identification of the OCF properties for the different disc regions. The identification was performed numerically using the MATLAB software to minimize the following objective function: F obj stre ss = 1 ρ ρ h =1 P h mod -˜ P h exp ˜ P h exp 2 ( 13 ) in which the letters with an overbar denote the experimental data with ρ the number of considered data points. Table  1  provides the obtained values for the OCF properties. Note that the regional dependence of the single lamellae stiffness is explained by a difference in the initial undulated state and the OCF content  [38, 39].
Disc construction and boundary conditions A lot of finite element disc models were developed in the literature  [ 26,27 , 30-33 , 52 ]. Beyond fundamental differences in the constitutive representation of the disc tissues, the time of model design and the time of calculation remain the main common weak points of all previous disc models. In the final objective to create time and cost-efficient patient-specific models of human discs, the present disc modeling strategy is purely analytical and again coded in MATLAB. The volume element of multi-lamellae, constitutively designed in the Cartesian space ( x , y , z ), was transferred to the Cylindrical space ( r , θ, z ) in order to consider the full disc geometry as illustrated in Fig. 11  a. From the theoretical point of view, the constitutive equations were re-written within a finite-strain kinematic framework in Cylindrical coordinates.
Stopped-Flow Assays The HU permeability of RBC variants was determined by using a stopped-flow spectrophotometer (SFM400, BioLogic, France) at 21 • C. Equal volumes of solutions A and B were used to mix RBCs (1% hematocrit in a PBS buffer) with a HU solution. For inhibition experiments, dimethylurea (DMU) (Sigma-Aldrich, St. Louis, MO, USA), a specific urea transporter B (UT-B) inhibitor  [17] , was added to solutions A and B at the same concentration. Time courses of the 90 • -scattered light intensity (λ exc of 530 nm) changes of RBCs were measured to follow the cell volume kinetics. Data from three time courses were averaged and fitted to single exponential functions by using the Simplex procedure of the BIOKINE software (BioLogic, Seyssinet-Pariset, France). The solute permeability coefficients (P s , cm/s) were calculated using the following equation: P s = k exp × V/S, where V/S is the volume-to-surface ratio and k exp is the exponential time constant fitted to the erythrocytes swelling phase of light scattering which corresponds to solute entry.
Lock masses were used to ensure mass accuracy below 5 ppm. Data were acquired with the Thermo Xcalibur software (ThermoFisher Scientific, Waltham, MA, USA). The peak areas of ruxolitinib were determined using the Thermo TraceFinder software (ThermoFisher Scientific, Waltham, MA, USA), identified by the exact mass of a singly charged ion and by the known retention time on the HPLC column.
Molecular Dynamics of ABCG2 in Membrane Environment We used the ABCG2 Cryo-EM structure (PDB ID: 6eti) as initial conformation for molecular dynamics (MD) simulations. The structure was resolved in the presence of the KO143 derivative (MZ29) acting as an inhibitor of ABCG2 efflux by plugging the lower cavity from the intracellular site  [46]. We removed ligands and the nucleotide binding domain (NBD) from the initial structure and used the transmembrane domain of the protein (TMD) to build a membrane system. The TMD position in the lipid membrane was obtained using the OPM database. The protein was inserted into the POPC bilayer using the CHARMM-GUI utility  [47]  and solvated with addition of 0. 15 M concentration of NaCl neutralizing system charge. We used the CHARMM36 force field [48] for model parametrization and performed MD simulations in an explicit solvent (type 3p  [49] ) using GROMACS 2016.
Ruxolitinib Docking Procedure The identified ABCG2 conformers together with crystal structures (PDB IDs: 6eti and 6hbu) were used to dock a ruxolitinib molecule. PDBQT files were generated using AutoDockTools v1. 5. 6 for further utilization in AutoDock VINA v1. 1. 2 [52]. The binding sites were identified using a docking box covering the whole transmembrane domain (with dimensions of 78 × 58 × 90 Å 3 ) and increased exhaustiveness parameter (equal to 20) to compensate for the large search space volume. For each of the eight conformers, we found 20 poses and repeated docking procedure 20 times changing the random seed (3200 docking poses in total). Once two main binding sites were determined, ruxolitinib docking was performed to each binding pocket individually using a docking box of 20 × 20 × 30 Å 3 in order to make our results more precise.