Overview on theoretical simulations of lithium‐Ion batteries and their application to battery separators

www.advenergymat.de 2203874 (1 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH REVIEW Overview on Theoretical Simulations of Lithium-Ion Batteries and Their Application to Battery Separators Daniel Miranda, Renato Gonçalves, Stefan Wuttke, Carlos M. Costa,* and Senentxu Lanceros-Méndez DOI: 10.1002/aenm.202203874 two topics in order to achieve a new generation of environmentally friendlier energy technologies, focusing on clean generation, conversion, and storage.[1] Since the first renewable energy generation system, important developments have been achieved not only in sustainable energy generation but also in energy storage systems. In the latter, electrochemical devices such as lithium-ion batteries are widely used for a large variety of applications, such as small portable electronic devices and electric vehicles, mainly based on their high energy density.[2] Lithium-ion batteries are therefore one of the most relevant energy storage devices due to their advantages when compared to other battery systems as they are cheaper, lighter, show higher energy density, have no memory effect, less self-discharge, higher number of charge/discharge cycles, and improved safety.[3] Lithium-ion batteries are typically based on three main components (anode, cathode, and separator) and two mains’ processes (charge and discharge of the battery). These processes are driven by electrochemical processes, lithium-ion, and electrons movement between electrodes allowing to storage/deliver energy.[4] With the need for further improving lithium-ion batteries as an efficient way to match the ever-developing needs of portable For the proper design and evaluation of next-generation lithium-ion batteries, different physical-chemical scales have to be considered. Taking into account the electrochemical principles and methods that govern the different processes occurring in the battery, the present review describes the main theoretical electrochemical and thermal models that allow simulation of the performance of lithium-ion batteries, including different materials and components (electrodes and separators) and battery geometries. As the separator plays an essential role in the performance and safety of lithium-ion batteries, the recent theoretical simulation work for this battery component are shown, with particular emphasis on morphology, dendrite growth, ionic transport, and mechanical properties. Further theoretical simulations and modeling of this battery component are still required for improving performance, taking into consideration varying geometric parameters such as pore size, porosity, and tortuosity as well as the optimization of the lithium diffusion process and ionic conductivity value. Theoretical simulations of battery separators will play an essential role in the new generation of lithium-ion batteries, allowing the improvement of their performance while reducing experimental probes and time. 1. Introduction The two topics, energy and environment, will be the most relevant global challenges that society will face for the years to come. Thus, the main focus of research and development should be to address these critical challenges by combining the The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aenm.202203874. D. Miranda 2AiSchool of Technology IPCA Barcelos 4750–810, Portugal D. Miranda LASI – Associate Laboratory of Intelligent Systems Guimarães, Portugal R. Gonçalves Center of Chemistry University of Minho Braga 4710-057, Portugal S. Wuttke, S. Lanceros-Méndez BCMaterials Basque Center for Materials Applications and Nanostructures UPV/EHU Science Park, Leioa 48940, Spain S. Wuttke, S. Lanceros-Méndez IKERBASQUE Basque Foundation for Science Bilbao 48009, Spain C. M. Costa Physics Centre of Minho and Porto Universities (CF-UM-UP) University of Minho Braga 4710-057, Portugal E-mail: cmscosta@fisica.uminho.pt C. M. Costa Laboratory of Physics for Materials and Emergent Technologies LapMET University of Minho Braga 4710-057, Portugal C. M. Costa IB-S Institute of Science and Innovation for Sustainability Universidade do Minho Braga 4710-057, Portugal © 2023 The Authors. Advanced Energy Materials published by WileyVCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Adv. Energy Mater. 2023, 13, 2203874 www.advenergymat.de www.advancedsciencenews.com 2203874 (2 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH device technologies, simulation, and computational modeling are essential tools for supporting the development and optimization of batteries and battery components. Computational simulation of lithium-ion batteries has a significant impact on the prediction of the performance of these energy storage systems as well as on the behavior and bonding of elements generated during their use. This area of research promotes a deeper theoretical knowledge of the operation of the devices, and existing theoretical models supporting materials development by modeling the main processes that occur in the operation of the battery.[5] In this review, we wish to describe the recent framework and theoretical advances in modeling lithium-ion battery operation. 2. Theoretical Modeling and Simulations of Lithium-Ion Batteries Theoretical models at the macro and micro-scales for lithiumion batteries aim to describe battery operation through the electrochemical model at different battery dimensions and under several conditions. Studies have further implemented coupled models to evaluate thermal, mechanical, and magnetic parameters in correlation with the electrochemical variables. Thermal, mechanical, and magnetic models have been coupled to the electrochemical model, establishing proper interrelations between their corresponding parameters, as illustrated in Figure 1. In most coupled models, the Doyle/Fuller/Newman electrochemical model represents the base model that describes the electrochemical processes (Electrochemical Battery Model). Table 1 shows the main equations of the Doyle/Fuller/ Newman electrochemical model that describe the electrochemical phenomena that occur in the battery components (current collectors, electrodes, and separator) during its operation processes. In the electrochemical model, liquid, solid, and porous phases are considered. The electrodes (cathode and anode) are studied as porous phase and the electrolyte is in a liquid phase and is present in the electrodes and separator pores.[6] In the electrochemical model (Table 1), interpolation functions of several active materials parameters are introduced, such as the solid phase potential value (ϕE) as a function of the lithium-ion concentration in the electrode solid (CE) and the potential value of the solid phase (ϕE) as a function of temperature (T). Regarding the electrolyte, interpolation functions of ionic conductivity (Kl) and lithium-ion concentration at the liquid phase (CL) are also taken into account. At all three cell components (anode, cathode, and separator) occurs the diffusion of lithium-ions through the electrolyte, Figure 1. Different models coupled to the electrochemical model for the simulation of lithium-ion batteries. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.dewww.advancedsciencenews.com 2203874 (3 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH however, the current collectors are a wall impermeable to the electrolyte, so the flow of lithium-ions is null at these limits. The interfaces between the three components show a condition of continuity that is expressed as an equal mass that flows on both sides of the interface. Table 2 shows the boundary conditions adopted for the different equations at the interfaces between the regions that will be presented for the battery model in 1D (Figure 2) where La, Ls, and Lc are the width of the anode, separator, and cathode, respectively. To evaluate the thermal parameters, thermal models are coupled,[8] allowing to measure the internal temperature reached by the battery and three important sources of heat produced by the Table 1. Main equations of Doyle/Fuller/Newman Electrochemical Model of the battery.[6] The identification of the symbols is provided in the nomenclature section. Battery Component Equation Description Electrodes (Cathode and Anode) −∇. (σef,i∇ϕE) = −FaJLi +, i = a, c Electrode potential calculated by the Ohm Law where the current density gradient is substituted by its equivalent in terms of lithiumion flux according to Faraday’s Laws. KFaJ kRT FtCiac ef i Li ϕ −∇ ∇ = + − ∇ ∇ = + + () 2(1 ) ( ln( )), , ,L 0L This equation relates the potential of the electrolyte with the local current density in the cathode and anode (Ohm Law). C tDCatJiac iefi Li ε ∂∂=∇ ∇ + − = ++ ()(1),, L,L 0Diffusion of lithium ions in the electrolyte applied to the cathode and anode (Fick Diffusion Law). C tDC rr C r Li ∂∂=∂ ∂+∂∂ ⎡ ⎣ ⎢⎤ ⎦ ⎥ 2 E2E 2 E Diffusion of lithium ions in the active material (solid phase) DD ef i l i ε = ,brugg KK ef i l i brugg ε = , brugg = 1.5 and i = a, c σef,i= σi (1 − εi− εf,i), i = a, c The effective ionic conductivity and diffusion in the liquid phase, applied to the cathode and anode, and the effective electrical conductivity in the solid phase. These parameters will depend on the porosity of the electrodes, εi, and the Bruggeman coefficient (brugg). Separator KFaJ kRT FtC ef s Li ϕ −∇ ∇ = + − ∇ ∇ + + () 2(1 ) ( ln( )) ,L 0L This equation relates the potential of the electrolyte with the local current density (Ohm Law). C tDC sefs ε ∂∂=∇ ∇.( ) L,L Lithium-ion diffusion in the electrolyte. DD ef s l s ε = ,brugg KK ef s l s ε = ,brugg The effective diffusion and conductivity of the ions in the separator General Equations Equation Description Faraday’s law (electrodes) ∇ iE= −FaJLi +Faraday’s law express the relationship between the insertion/extraction of lithium ions into the electrodes with the electrical charge flow. Relation between the lithium ions flux and the current density in the electrodes. Faraday’s law (electrolyte) ∇ iL= FaJLi +Relation between the lithium ions flux and the current density in the electrolyte (Faraday’ s Law). Total current density (electrodes and electrolyte) iE+ iL=ITOTAL Conservation of charge. The current density is preserved between the electrode and the electrolyte. Butler–Volmer equation (kinetics) JFu RT Fu RT i ηη {} =− ⎛ ⎝⎞ ⎠−−− ⎛ ⎝⎞ ⎠ exp () 2exp () 2 00 ikC C C C iac ii ii Si S aj ci ai =− = ααα ()()(),, 0, E, max E, E, L ,,, Kinetics of the heterogeneous reaction at the electrode/electrolyte interface, described by the Butler–Volmer equation. Variable overpotential η =ϕE −ϕL−u0The variable over-potential relates the potential of the electrodes/ electrolyte and the open circuit voltage. Overall mass balance. C tMR ∂∂=−∇ +. LOverall mass balance. Term of the reaction Ra VtJ=− − +++ (1 ) 0Li Reaction term of the mass balance equation. Mass transport process MDdlnC dlnC Cit F =− − ∇ + + (1 ) L LLL0 0 Mass transport flux. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. 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The following are the main equations governing the thermal behavior of the battery.[12–15] Equation (1) shows the energy balance in all battery components through the first law of thermodynamics: d d,,, p,i 2 2 2 2total,i ρλλ =∂ ∂+∂ ∂+=CT t T x T yQiasc lii (1) where ρ, Cp,I, and λi are the density of battery components, heat capacity at constant pressure of battery components, and thermal conductivity of battery components, respectively. The Qtotal,i is the total heat generation rate at each battery component. For electrodes, the Qtotal,i is the sum of all heat produced by electrodes such as the total reaction heat generation (irreversible), Qreaction,i, total reversible heat generation (reversible), Qreversible,i, and total ohmic heat generation, Qohmic,i, and is expressed by Equation(2): ,, total,i reaction,i reversible,i ohmic,i =+ + =QQ Q Q iac (2) The total reversible heat generation (reversible), Qreversible,i, is related to the reversible entropy loss in electrodes, and the total reaction heat generation (irreversible), Qreaction,i, is related to concentration polarization, activation polarization, and ohmic polarization. Equations(3) and (4) allow to determine, respectively, the reversible and irreversible heat generation produced by the electrodes. ,, reversible,i =∂ ∂=Q FaJT U Tiac (3) ,, reaction,i E L ϕϕ () =−−=QFaJ Uiac (4) with the parameters T, U, ϕE, ϕL, F, U, and dU/dT, which represent the temperature, the open-circuit voltage, the potential of the electrodes, the potential of the electrolyte, the Faraday’s constant, the open-circuit voltage and the coefficient of open-circuit voltage varying with temperature, respectively. At the electrodes, the total ohmic heat generation, Qohmic,i, corresponds to the heat dissipated, produced by the internal resistance associated with the charge transport and is defined by the following Equation(5): Table 2. Summary of the boundary conditions or limits of the mathematical model 1D adopted by.[7] Boundary Conditions Battery Component Governing Law x= 0 x=Lax=La+Lsx=La+Ls+Lc Electrolyte Li+ Diffusion C x ∂ ∂=0 LContinuity Continuity C x ∂ ∂=0 L Ohm’s Law ϕL=ϕL,0 Continuity Continuity x ϕ ∂ ∂=0 L Electrodes Ohm’s Law ϕE= 0 x ϕ ∂ ∂=0 E ϕE=ϕE,0 x I ϕσ ∂ ∂=− ETOTAL Governing Law r= 0 r=RS Li+ Diffusion r = 0 C r ∂∂=0 Er = RS C r j D ∂∂=− + ELi Li Figure 2. Schematic representation of the 1D model applied to lithiumion batteries. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. 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Regarding separator, the total heat generation, Qtotal,i, corresponds only to the contribution related to the total ohmic heat generation, Qohmic,i, produced by the internal resistance associated with the flow of lithium-ions, as shown in Equation(6): , total, ohmic, ==QQ is ii (6) where the total ohmic heat generation, Qohmic,i, produced at the separator is defined by the following Equation(7): 21ln 21ln , ohmic, ef , L 2 ef , L 2 ef ,L f, L ϕϕ ϕ ϕ () () () () =∂ ∂ ⎛ ⎝ ⎜⎞ ⎠ ⎟+∂ ∂ ⎛ ⎝ ⎜⎞ ⎠ ⎟+− ∂ ∂ ∂ ∂ +− ∂ ∂ ∂ ∂= + + Qk xky kRT Ftc xx kRT Ftc yy is ii i ii ei i (7) The electrochemical parameters of electrodes such as open circuit potential, U, reaction rate coefficient, kLi, and diffusion coefficient of Li ions, DLi, as a function of temperature are shown in Equations(8)–(10), respectively, d d,, ref , ref () =+− ⎡ ⎣ ⎢⎤ ⎦ ⎥= UU TT U Tiac ii (8) 11 298.15 ,, Li t298,15 E/ , () =− ⎛ ⎝⎞ ⎠= − kT k e Tiac i R ak i (9) 11 298.15 ,, Li t298,15 E/ ad , () =− ⎛ ⎝⎞ ⎠= − DT D e Tiac i R i (10) with the parameters Ead,i and Eak,i, which represent the activation energy for diffusion and the activation energy for reaction of electrodes, respectively. Regarding separator/electrolyte and liquid phase of both electrodes (anode and cathode), the diffusion coefficient as a function of temperature, Di(T), is established by Equation(11): 10 Li 0.22 4.43 54 229 5 ,,, () = () −−− −− ⎛ ⎝ ⎜⎞ ⎠ ⎟ ⎛ ⎝ ⎜⎞ ⎠ ⎟ ⎛ ⎝ ⎜⎞ ⎠ ⎟= DT cTc iasc (11) where c is the concentration of lithium-ion. Finally, all boundaries between the battery and the external environment, the heat flux transfer between the battery and the external environment, is described by Equation(12): ,,, external λ () −∇= − =ThT Tiasc i (12) where h is the heat transfer coefficient. In recent years, with the replacement of the typical liquid electrolyte with a solid electrolyte, a new type of battery designated solid-state lithium-ion batteries appears. The investigation of this type of lithium-ion battery has been increasing. Then, the main electrochemical equations that govern the phenomena of solid-state lithium-ion battery[9] will be presented. Regarding the separator with the presence of a solid electrolyte, the transport of lithium-ions (Li+) and negative ions (n−) is described by the Nernst-Planck equation, as shown in Equation(13): .. ... , Li, E ϕ =− ∇ +⎛ ⎝ ⎜⎞ ⎠ ⎟∇= +− NDc zF RT Dc in iii i i (13) where Di is the diffusion coefficient of ions (negative and positive) and zi is charge of ions. In the solid electrolyte occurs the ionization reaction which causes the presence of mobile Li-ions and uncompensated negative charges (n−). The dissociation rate for this reaction is designated by kdiss, and the recombination reaction rate by krec. In this context, lithium-ion recombination/dissociation rate is described by Equation(14): Li diss 0,Li Li rec Li () () =−−++++−rkc c kcc n (14) where cLi+ an cnare concentration of lithium-ion and concentration of negative ion, respectively. The relation between the dissociation reaction rate (kdiss) with recombination reaction rate (krec) is shown in the following Equation(15): Li diss 0,Li Li rec Li () () =−−++++−rkc c kcc n (15) Equation(16) shows the fraction of total lithium dissociated at equilibrium, δ: Eq Eq 0 δ == +− cc c Li n (16) Within the scope of electrodes, the transport of solid lithium through the positive electrolyte (Fick’s law) is described by the following Equation(17): . Li ,solid Li =− ∇ND c Li (17) where DLi, solid is the diffusion coefficient for solid lithium through the electrolyte at positive electrode. At the electrode/electrolyte interface, the kinetics reactions at the negative electrode (Equation(18)) and positive electrode (Equations (19) and (20)) described by the Butler–Volmer kinetics equations are: .. 0, /1/ =⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟+ ⎛ ⎝ ⎜⎞ ⎠ ⎟ α αα () () () + + −− i neg F k c cee neg Li Li neg negFn RT neg Fn RT (18) c0, /1/ () =+ αα () () () −− iie e c Fn RT Fn RT cc (19) . .1 0, Li,max Li Li Li,max Li,min 0, Li Li,min Li,max Li,min α () () () () =− − ⎛ ⎝ ⎜⎞ ⎠ ⎟− − ⎛ ⎝ ⎜⎞ ⎠ ⎟− α + + iFk ccc ccc cc cc cc Li c c (20) where cLi,max and cLi,min are the maximum and minimum concentrations of lithium in the solid electrode, respectively. Finally, the overpotential, η, is defined by Equation(21): Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. 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Different physical levels—molecular scale, nanoscale, microscale, mesoscale, and macroscale—of the batteries operation, which are studied with different models and theoretical approaches, are presented in Figure 3.[10] Theoretical models are based on equations that reflect the physical and electrochemical principles that govern the different processes and phenomena that define the performance and life cycle of lithium-ion batteries. Computer simulation methods have encompassed a wide range of spatial and temporal scales as represented in Figure3. Multiscale calculation methods, microscale methods (firstprinciples (FP) calculations,[11] molecular dynamics (MD),[12] quantum mechanics (QM),[13] and Monte Carlo (MC)[14]), mesoscale (phase-field (PF) method,[15] and force-field (FF) approach),[16] and macroscale approaches (Finite Element Methods (FEM)[5c]) have been applied in the area of rechargeable lithium-ion batteries. With the development of computational efficiency, simulations based on density functional (DFT) theory and MD have been applied to predict molecular interaction,[17] interfacial reactions,[18] ion transport mechanisms,[19] and to study material properties to improve battery performance, leading to the development of material databases. Some of these databases are: Cambridge Structural Database,[20] Inorganic Crystal Structure Data-base (ICDS),[21] Open Quantum Materials Database,[22] and Materials Project database,[23] among others. However, with the investigation of more complex materials, theoretical studies of DFT and MD become ineffective due to the high computational demand,[24] and the enormous obstacles that DFT and MD face with the increase in system size and necessary precision. The lag between the results obtained in the simplified theoretical models and the reality makes it difficult to apply computer simulations to describe the complex interface processes that occur in batteries, such as the solid electrolyte interface (SEI) formation, thermodynamics and kinetics of internal reactions, ionic transport behavior at liquid-solid or solid-solid interfaces, among others. Computer simulations, when resorting to simplified theoretical models in order to lower the computational cost, often neglect a high amount of data on battery materials available in databases.[25] In this context, with the emergence of Artificial Intelligence (AI), more specifically the branch of Machine Learning (ML), new opportunities arise within the scope of theoretical and experimental studies in the area of lithiumion battery development.[26] Compared to the traditional oneby-one approach, high-throughput technologies can generate a large and high-quality database in a short time at low cost. The design, selection, and integration of materials determined by ML also demonstrate to be a suitable approach with relevant contributions also in relation to other simulation technologies. ML technologies allow exploring new resources for DFT calculations and new potentials for MD simulations, significantly improving the study of phenomena that occur at amorphous and complex interfaces and structures. ML has a high potential to explore and reveal valuable information from experimental and theoretical data sets, improving prediction in the behavior of ionic conductivity, solid–solid interfaces, Figure 3. Different physical-chemical scales in the modeling of battery materials and battery operation. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.dewww.advancedsciencenews.com 2203874 (7 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH liquid–solid interfaces, and battery life, as well as allowing to optimize and accelerate research procedures in the area of Li-ion batteries. Further, ML may facilitate the combination of multiscale simulations.[27] ML statistically interprets data contained in a database to produce reliable and repeatable decisions and results.[28] Generally, any ML-based approach starts with building an adequate and complete dataset. Subsequently, the ML model must be trained and evaluated for accuracy. In the most common case, supervised methods are obtained using a portion of the dataset to train the algorithm (training stage), where the predictive ability is evaluated by comparing the values predicted by the method and the remaining data that were not used in the training stage, being designated as the test stage. After obtaining the ideal model, the supervised ML algorithm will be used to predict the results.[29] ML algorithms are classified as supervised learning, unsupervised learning, or reinforcement learning methods.[30] Supervised learning methods use pre-treated data sets to define certain variables as inputs and others as outputs. This procedure does not occur in the case of unsupervised learning ML algorithms, whose objective is to find patterns in the dataset. In ML-supervised learning, it is possible to achieve regression and classification, where a classification is an ML approach that analyzes the dataset into classes, while regression analyzes the data as continuous values.[29] In ML supervised learning, the classes used can be obtained from ML unsupervised learning. Figure 4 shows the ML algorithms used in supervised learning, unsupervised learning, and reinforcement learning methods applied in lithium-ion battery research. Some ML algorithms applied in lithium-ion battery research to obtain predictive models include:[29] artificial neural network (ANN), Decision Tree (DT), Random Forest (RF), Boosting and Bagging Approaches, Support Vector Machine (SVM), U-Net, k-Nearest neighbors (kNN), Naive Bayes (NB) Classification, Generative Models and Inverse Design (class of unsupervised ML algorithms), K-Means and Gaussian Mixture. Briefly, in the first step, data can be collected from existing databases resulting from experimental measurements and/or theoretically obtained values from computer simulations. Then, the data are submitted to the process of data cleaning and data engineering (Feature Engineering) which is based on the extraction and selection of data. Briefly, the original data can be converted into samples to train the ML model; the mapping relationship between the conditional attributes and the decision attributes can be simulated by selecting the appropriate and optimal ML algorithm. Finally, the models obtained from the ML will allow to be explored with the aim of predicting new results for the development of rechargeable batteries. In many cases, there is an interaction between conventional theoretical simulations (DFT, MD, and FEM) with ML methods. DFT/MD-assisted ML methods have been developed that contribute to DFT/MD-based predictions. FEM-assisted ML prediction methods have been applied, showing great predictive potential, however, FEM-assisted ML is still in the implementation phase, a path still to be explored.[31] It is important to emphasize the fundamental role of the interaction and contribution of all current three workflow cycles: experimental discovery cycle for batteries, computation model cycle, and ML learning cycle (Figure 5). These three work cycles should synergistically interact with each other in order to improve the overall battery research. In the development of studies to predict and optimize the performance of lithium-ion batteries, the ML Cycle, the Computational Model Cycle, and the Experimental Discovery Cycle domains interact with each other, as represented in Figure5. The results obtained in the experimental battery domain and in computational simulation (FEM, DFT, among others) are introduced in databases that will be used in ML. After the implementation of the learning algorithm, new predictions and optimization results are obtained that will be validated through the experimental component, leading to an improvement of the empirical knowledge for the development of new prototypes. Further, experimentally obtained results allow to validate the models developed in the field of Computational Simulation which in turn will also produce theoretical results to understand and guide the Experimental Design and the development of advanced materials and prototypes. The combination of databases and ML approach has been applied to design and predict material properties of electrodes, including crystallinity, chemical stability, and voltage, from the atomic scale to mesoscale. Bearing this in mind, ML can be applied to design new solid-state electrolytes (organic and Figure 4. Schematic representation of ML methods and algorithms. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.de www.advancedsciencenews.com 2203874 (8 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH inorganic) with fast Li-ion transport and mechanical properties, providing an opportunity for exploring material properties at a lower cost and accelerating the material discovery processes. There are an increasing number of works based on the applications of ML in battery research, essentially with respect to the characterization and discovery of new materials with predicted specific properties. The discovery and optimization of materials applied to batteries have been a powerful tool for analyzing experimental and theoretical data and, from them, extracting the relationships between their structures and functional properties, with a wide range of review articles addressing this topic.[27,32] ML methods have been applied to predict and develop materials for rechargeable battery electrodes, solid electrolytes, and liquid electrolytes. For the electrode dimensions and structure, ML simulations have been performed to find optimal designs that allow highest possible combination of capacity and power output. For improving power performance, a homogeneous electrochemical activity between the electrodes in the 3D design is essential. In the case of materials for battery electrodes, ML methods have been applied to predict voltage profiles of a wide range of active materials for Li-, Mg-, Ca-, Al-, and Zn-ion batteries through the implementation of different algorithms.[33] About 5000 electrode materials were proposed to be used in sodium and potassium ion batteries based on voltage profile diagrams and comparing them to the DFT calculations (a website was developed for the estimation of the voltage of electrode materials).[34] Research has been also performed for the design of new organic materials for electrodes based on the prediction of quantum mechanical quantities (redox potentials) and electronic properties (electron affinity, lowest unoccupied molecular orbital (LUMO), highest occupied molecular orbital (HOMO)) through artificial neural networks using the quasi-Newton method.[35] Calculations of lattice constants for fully lithiated and delithiated structures have been also performed to design low-strain cathode materials, based on ML models combining ab initio calculations and partial least squares (PLS) analysis with the Quantitative Structure–Activity Relationship (QSAR) formulations, allowing to predict the percentage change in volume of spinel and layeredtype oxides (Figure 6a).[36] Further, significant achievements have been presented in predictive studies related to crystalline cathode systems,[37] the configuration energy of LiNiO2 (LNO) and LiNi0.8Co0.15Al0.05O2 (NCA) cathodes,[38] the redox potentials of electrodes,[35] and to the impact of manufacturing parameters on the final characteristics of the electrodes (porosity and mass loading).[39] Takagishi etal.[40] used ANN simulations to predict the charge/discharge specific resistance in porous structures by analyzing the effects of porosity, components volume fraction, calendering process, and electrolyte compatibility parameters in the electrode preparation. It was observed that the packing process should be around 50%, that small active material particles are more suitable, and that their volume fraction should be between 0.5 and 0.8. 3D U-Net architecture simulation was used to create an accurate 3D LIB electrode image taking into account the electrode particles (active particles and binder) and the pore phases. The originated database and further improvement of this database by the scientific community will allow to further improve electrodes.[41] ML simulation have been applied to study the separator structures of LIBs. The prediction of the mechanical behavior of the separators was studied by combining FEA and ML strategies. The proposed method shows that applying a unidirectional force during mechanical stress allows to predict the growth of Figure 5. The three workflow cycles of battery research: experimental discovery cycle for batteries, computation model cycle, and ML learning cycle. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.dewww.advancedsciencenews.com 2203874 (9 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH the voids, important to understand the failure mechanism of thermal runaway of the battery when subjected to an external force.[42] ML has been also applied to the optical inspection of the separators, preventing in this way structure defects. The objective is to distinguish between defect classes as non-quality optical effects and faults.[43] Within the scope of the development of liquid and solid electrolytes, studies have been increasingly carried out with the application of ML methods in order to predict coordination energies and melting temperatures of solvent molecules (Figure 6b),[44] predict the diffusion barrier of lithium ions from a set of compositions,[45] prediction of the ionic conductivity of nanocomposite polymer electrolyte systems (PEOLiPF6-EC-CNT),[46] prediction of the transport characteristics of lithium-ions and identification of the descriptors that are responsible for the high conductivity of lithium-ions in garnetstructured oxides,[47] evaluation of possible reactions and thermodynamic stability of Li|Li7La3Zr2O12 (LLZOM, M = dopant) interfaces under various chemical conditions,[48] prediction of the low-temperature ionic conductivity of a large variety (72) of compounds,[49] reproduction of 3D electrode structures (Figure 6c),[50] and effect of electrolyte channel geometrical parameters,[51] among others. With respect to overall battery performance, ML methods have been applied to predict the lifespan of batteries and to monitor the state of health of lithium-ion batteries, accurately predicting the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) parameters and allowing to implement intelligent battery management system (BMs). As an example, a set of ML algorithms such as DT have been implemented to analyze battery lifespan (Figure 6d).[52] A powerful ML-based tool has been also proposed to develop safety envelopes for lithium-ion pouch battery cells.[53] Regarding the prediction of battery performance, a model based on the Extreme Learning Machine (ELM) method was proposed to predict the evolution of battery temperature, voltage, and power.[54] Further, some studies have faced the challenge of developing linear models that accurately predict the cycle life of commercial lithium iron phosphate (LFP)/graphite cells.[55] Also, a multikernel support vector machine (MSVM) model based on polynomial and radial kernel functions was proposed to predict the battery remaining useful life (RUL).[56] In order to obtain more Figure 6. a) Predicted cathode volume changes for partial least squares as a function of ab initio-derived, where the data from LiX2O4 spinel is represented by blue circles and LiXO2 layer structure by red diamonds. Reproduced with permission.[36] Copyright 2017, Elsevier. b) Weight diagram of descriptors obtained from the top 25 combinations of descriptors, for the melting point prediction. Reproduced under the terms of the CC BY 3.0 license.[44] Copyright 2018, The authors, published by Royal society of chemistry. c) Periodic microstructure generated for Li-ion cathode (left) and SOFC anode (right) with flux maps generated from steady-state diffusion simulations in TauFactor. Reproduced under the terms of the CC BY license.[50] Copyright 2020, The authors, published by Springer Nature. d) Prediction accuracy comparison between the different ML algorithms calculated from the same data. Reproduced with permission.[52] Copyright 2019, John Wiley and Sons. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.de www.advancedsciencenews.com 2203874 (16 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH It was observed that the CR affects the mechanical properties and performance of the battery from the microscale to the macroscale. Thus, at CR = 40%, the ionic conductivity decreased by 87.5% leading to a decrease in battery performance.[123b] High pressure has been also introduced in the electrochemical-mechanical model for studying the compressibility and ionic conductivity of the polyethylene separator. It was shown that the ionic conductivity decreases in the porous structure, the intercalation and deintercalation at the separator interfaces of the electrodes become more accelerated, and therefore, the Li concentration gradient within the electrodes increases with pressure and enhances the Li deposition on the electrode surface during the charging behavior, resulting in the aging process.[153] Inhibition of lithium dendrites can be also controlled by the separator. The effect of separator pore size in lithium dendrite growth has been evaluated by the phase field method (PFM) and fifth regimes of dendrites growth were verified, as shown in Figure13b, serving as a guideline to design improved separators. Dendrites growth depends on the separator chemistry, morphology, and transport properties.[151] The dendrite growth in separators has been also addressed by phase-field simulations as a function of pore size showing that separators with smaller pore sizes are beneficial to smoother electrodeposition.[154] Further, it has been also demonstrated that tortuosity significantly affects the growth rate of dendrites, resulting in a shorter lifetime in the battery. Also, it was observed that the different dendrite growth rates are related to the degree of heterogeneity of the separator with same bulk physical properties.[155] In addition, a nano-shield (NS) protected separator has been developed by coating SiO2 nanoparticles, the FEM theoretical simulations, and experiments demonstrate that the NS suppresses Li dendrites growth because of the formed narrow Figure 12. Discharge capacity a) for batteries with different separators at 50C-rate and b) for each battery for all C-rates. Color mapping of ionic current density for c) conventional, d) pillar, and e) zig-zag separators. f) Electrolyte current density across a line for each separator. Reproduced with permission.[141] Copyright 2019, Elsevier. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.dewww.advancedsciencenews.com 2203874 (17 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH channel and mitigates short-circuiting by the reduction of the voltage intensity due to surface effects.[156] In conclusion, the separator plays an essential role in improving battery performance and its proper selection, design, and interaction with the electrolyte solution provide improved ion transport paths between the electrodes. 4. Conclusions Lithium-ion battery technology has strongly supported modern technology development by powering small portable and electronic devices and, more recently, electric vehicles. Lithium-ion batteries already represent a reliable and widely used energy storage system, where their functioning/behavior at different levels is described by fundamental laws of physics such as, mass transport, electromagnetism, and thermodynamics. The performance of Li-ion batteries must be nevertheless further improved in terms of energy and power density, by relying on a deeper understanding of their operation principles. In this scope, theoretical simulation at different levels is playing an increasing role in designing, optimizing, and predicting battery performance. The theoretical simulation of the battery at different levels from the sub-atomic (nano) scale to the macro-scale allows the selection, optimization, and prediction of different properties including the selection and amounts of active material in the electrodes, geometrical effects, or properties of the separator membranes, the performance of each component being described through the different equations that govern its functioning. In lithium-ion batteries, the battery separator is an important component that affects their behavior, being within the scope of recent theoretical simulation works focusing on separator parameters such as morphology, ion transport, mechanical properties, and dendrites growth. The perspectives for further theoretical works on battery separators are focused on the effect of the interface with the electrodes, ion concentration gradients, and varying morphologies to prevent dendrites growth, among others. Considering the recent works for this component, it results evident that more theoretical works are still needed addressing the former issues, supported by the increasing number of available data and ML algorithms. Theoretical simulation will allow a decrease in resources and time consumption in next-generation battery development, leading to a more sustainable and rapid evolution of energy storage systems. Theoretical simulation will thus play a central role in the selection of materials, improving the safety and performance of the new generation of lithium-ion batteries with a focus on sustainability. Future battery science and engineering should foster interdisciplinary collaboration allowing to overcome the current issues on battery development and properly address one of the global problems of our time in the scope of energy transition and sustainability. Acknowledgements The authors thank the FCT (Fundação para a Ciência e Tecnologia) for financial support under the framework of Strategic Funding grants Figure 13. a) Top-view scanning electronic microscopy images of 3μm diameter of isotropic PE and anisotropic PP (with transverse direction, TD, and machining direction, MD) separators. Resulting separator microstructure of electrolyte-immersed PE (top) and PP (bottom) (i.e., gray polymer space and pale-yellow electrolyte-filled pore space) of 3μm edge length under uniaxial compressive strains of 0%, 5%, 10%, 20%, and 40% in the through plane (TP) direction. The compressive stress in MPa applied to achieve the given percent strain is indicated for each structure. Reproduced under the terms of the creative commons attribution 4.0 license.[150] Copyright 2018, The authors, published by Electrochemical society. b) Predicted regimes of dendrite behavior in a porous separator: the suppression regime, below the blue curve, highlights the loci of pore sizes and recharge rates that are thermodynamically unfavorable for dendrites to grow; the permeable regime, below the black line, where dendrites cannot penetrate more than the very surface of the separator (the first layer of fibers); the penetration regime, between the red and the black line where dendrites rely on electrochemical shielding to find a thermodynamically stable pore to persist inside the separator; and finally, the short-circuit regime, to the right of the red line. Reproduced with permission.[151] Copyright 2015, Elsevier. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.de www.advancedsciencenews.com 2203874 (18 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH UIDB/04650/2020, UID/FIS/04650/2020, UID/QUI/0686/2020, UID/EEA/04436/2020, UIDB/05549/2020, UIDP/05549/2020, and LASI-LA/P/0104/2020, project PTDC/FIS-MAC/28157/2017, MITEXPL/TDI/0033/2021 and project SmartHealth, “NORTE-01-0145FEDER-000045”, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors also thank the FCT for financial support under Investigator FCT Contracts CEECIND/00833/2017 (R.G.) and 2020.04028.CEECIND (C.M.C.) as well POCH and European Union. Financial support from the Basque Government Industry Department under the ELKARTEK program was also acknowledged. This study forms part of the Advanced Materials programme and was supported by MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1) and by Basque Government. Conflict of Interest The authors declare no conflict of interest. Keywords electrochemical models, lithium-ion batteries, modeling, separators, theoretical simulation, thermal models Received: November 14, 2022 Revised: December 9, 2022 Published online: February 15, 2023 [1] a) R. A.Kerr, R. F.Service, Science 2005, 309, 101; b) J. P.Holdren, Science 2007, 315, 737. [2] J. M.Tarascon, M.Armand, Nature 2001, 414, 359. [3] N.Nitta, F.Wu, J. T.Lee, G.Yushin, Mater. Today 2015, 18, 252. [4] G.Pistoia, Lithium-Ion Batteries: Advances and Applications, Elsevier Science, Amsterdam 2013. [5] a) Z.Wang, J.Ni, L.Li, J.Lu, Cell Rep. Phys. Sci. 2020, 1, 100078; b) A. Y. S.Eng, C. B.Soni, Y.Lum, E.Khoo, Z.Yao, S. K.Vineeth, V.Kumar, J.Lu, C. S.Johnson, C.Wolverton, Z. W.Seh, Sci. Adv. 2022, 8, eabm2422; c) D. Miranda, C. M. Costa, S. LancerosMendez, J. Electroanal. Chem. 2015, 739, 97. [6] P. M.Gomadam, J. W.Weidner, R. A.Dougal, R. E.White, J. Power Sources 2002, 110, 267. [7] E. Martínez-Rosas, R. Vasquez-Medrano, A. Flores-Tlacuahuac, Comput. Chem. Eng. 2011, 35, 1937. [8] a) L. Cai, R. E. White, J. Power Sources 2011, 196, 5985; b) P. W. C. Northrop, M. Pathak, D. Rife, S. De, S.Santhanagopalan, V. R.Subramanian, J. Electrochem. Soc. 2015, 162, A940; c) S.Bae, H. D.Song, I.Nam, G.-P.Kim, J. M.Lee, J.Yi, Chem. Eng. Sci. 2014, 118, 74; d) R. E.Gerver, J. P.Meyers, J. Electrochem. Soc. 2011, 158, A835. [9] A.Bates, S.Mukherjee, N.Schuppert, B.Son, J. G.Kim, S.Park, Int. J. Energy Res. 2015, 39, 1505. [10] a) A. A.Franco, M. L.Doublet, W. G.Bessler, Physical Multiscale Modeling and Numerical Simulation of Electrochemical Devices for Energy Conversion and Storage: From Theory to Engineering to Practice, Springer, London 2015; b) V.Ramadesigan, P. W. C.Northrop, S. De, S. Santhanagopalan, R. D. Braatz, V. R. Subramanian, J. Electrochem. Soc. 2012, 159, R31. [11] Y. S.Meng, M. E.Arroyo-de Dompablo, Energy Environ. Sci. 2009, 2, 589. [12] A. Muralidharan, M. I. Chaudhari, L. R. Pratt, S. B.Rempe, Sci. Rep. 2018, 8, 10736. [13] E. W. C. Spotte-Smith, S. M.Blau, X. Xie, H. D. Patel, M.Wen, B.Wood, S.Dwaraknath, K. A.Persson, Sci. Data 2021, 8, 203. [14] E. M.Gavilán-Arriazu, M. P.Mercer, D. E.Barraco, H. E.Hoster, E. P. M.Leiva, Prog. Energy 2021, 3, 042001. [15] S.Hu, Y.Li, K. M.Rosso, M. L.Sushko, J. Phys. Chem. C 2013, 117, 28. [16] K.-S.Yun, S. J.Pai, B. C.Yeo, K.-R.Lee, S.-J.Kim, S. S.Han, J. Phys. Chem. Lett. 2017, 8, 2812. [17] a) X.Chen, Q.Zhang, Acc. Chem. Res. 2020, 53, 1992; b) X.Chen, X. Q.Zhang, H. R.Li, Q.Zhang, Batteries Supercaps 2019, 2, 128. [18] O. Borodin, X. Ren, J. Vatamanu, A. von Wald Cresce, J. Knap, K.Xu, Acc. Chem. Res. 2017, 50, 2886. [19] a) Z.-H. Fu, X. Chen, C.-Z. Zhao, H. Yuan, R. Zhang, X. Shen, X.-X.Ma, Y.Lu, Q.-B.Liu, L.-Z.Fan, Q.Zhang, Energy Fuels 2021, 35, 10210; b) A. M.Nolan, Y.Zhu, X.He, Q.Bai, Y.Mo, Joule 2018, 2, 2016. [20] C. R.Groom, I. J.Bruno, M. P.Lightfoot, S. C.Ward, Acta Crystallogr. B 2016, 72, 171. [21] M.Hellenbrandt, Crystallogr. Rev. 2004, 10, 17. [22] J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, C. Wolverton, JOM 2013, 65, 1501. [23] A.Jain, S. P.Ong, G.Hautier, W.Chen, W. D.Richards, S.Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K. A. Persson, APL Mater. 2013, 1, 011002. [24] C.Chen, Y.Zuo, W.Ye, X.Li, Z.Deng, S. P.Ong, Adv. Energy Mater. 2020, 10, 1903242. [25] W. Sha, Y. Guo, Q. Yuan, S. Tang, X. Zhang, S. Lu, X. Guo, Y.-C.Cao, S.Cheng, Adv. Intell. Syst. 2020, 2, 1900143. [26] a) R.Batra, L.Song, R.Ramprasad, Nat. Rev. Mater. 2021, 6, 655; b) M.Meuwly, Chem. Rev. 2021, 121, 10218. [27] G. H.Gu, J.Noh, I.Kim, Y.Jung, J. Mater. Chem. A 2019, 7, 17096. [28] A.Chen, X.Zhang, Z.Zhou, InfoMat 2020, 2, 553. [29] T. Lombardo, M. Duquesnoy, H. El-Bouysidy, F. Årén, A. GalloBueno, P. B. Jørgensen, A. Bhowmik, A. Demortière, E. Ayerbe, F. Alcaide, M. Reynaud, J. Carrasco, A. Grimaud, C. Zhang, T.Vegge, P.Johansson, A. A.Franco, Chem. Rev. 2022, 122, 10899. [30] S.Li, J.Li, H.He, H.Wang, Energy Procedia 2019, 159, 168. [31] X.Chen, X.Liu, X.Shen, Q.Zhang, Angew. Chem., Int. Ed. 2021, 60, 24354. [32] a) A.Bhowmik, I. E.Castelli, J. M.Garcia-Lastra, P. B.Jørgensen, O. Winther, T. Vegge, Energy Storage Mater. 2019, 21, 446; b) A.Jain, G.Hautier, S. P.Ong, K.Persson, J. Mater. Res. 2016, 31, 977; c) D. H.Barrett, A.Haruna, Curr. Opin. Electrochem. 2020, 21, 160. [33] S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, New Jersey 2002. [34] R. P.Joshi, J.Eickholt, L.Li, M.Fornari, V.Barone, J. E. Peralta, ACS Appl. Mater. Interfaces 2019, 11, 18494. [35] O.Allam, B. W.Cho, K. C.Kim, S. S.Jang, RSC Adv. 2018, 8, 39414. [36] X.Wang, R.Xiao, H.Li, L.Chen, J. Materiomics 2017, 3, 178. [37] M.Attarian Shandiz, R.Gauvin, Comput. Mater. Sci. 2016, 117, 270. [38] R. A. Eremin, P. N. Zolotarev, O. Y. Ivanshina, I. A. Bobrikov, J. Phys. Chem. C 2017, 121, 28293. [39] R. P. Cunha, T. Lombardo, E. N. Primo, A. A. Franco, Batteries Supercaps 2020, 3, 60. [40] Y.Takagishi, T.Yamanaka, T.Yamaue, Batteries 2019, 5, 54. [41] S. Müller, C. Sauter, R. Shunmugasundaram, N. Wenzler, V.De Andrade, F.De Carlo, E.Konukoglu, V.Wood, Nat. Commun. 2021, 12, 6205. [42] H.Xu, M.Zhu, J.Marcicki, X. G.Yang, J. Power Sources 2017, 345, 137. [43] J. Huber, C. Tammer, S.Krotil, S. Waidmann, X.Hao, C.Seidel, G.Reinhart, Proc. CIRP 2016, 57, 585. [44] K. Sodeyama, Y. Igarashi, T.Nakayama, Y. Tateyama, M. Okada, Phys. Chem. Chem. Phys. 2018, 20, 22585. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.dewww.advancedsciencenews.com 2203874 (19 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH [45] R.Jalem, M.Nakayama, T.Kasuga, J. Mater. Chem. A 2014, 2, 720. [46] S.Ibrahim, M. R.Johan, Int. J. Electrochem. Sci. 2011, 6, 5565. [47] N.Kireeva, V. S.Pervov, Phys. Chem. Chem. Phys. 2017, 19, 20904. [48] B. Liu, J.Yang, H.Yang, C.Ye, Y.Mao, J.Wang, S.Shi, J.Yang, W.Zhang, J. Mater. Chem. A 2019, 7, 19961. [49] K. Fujimura, A. Seko, Y. Koyama, A. Kuwabara, I. Kishida, K. Shitara, C. A. J. Fisher, H. Moriwake, I. Tanaka, Adv. Energy Mater. 2013, 3, 980. [50] A.Gayon-Lombardo, L.Mosser, N. P.Brandon, S. J.Cooper, npj Comput. Mater. 2020, 6, 82. [51] T.Gao, W.Lu, J. Electrochem. Soc. 2020, 167, 110519. [52] S.Zhu, N.Zhao, J.Sha, Energy Storage 2019, 1, e98. [53] W. Li, J. Zhu, Y. Xia, M. B. Gorji, T. Wierzbicki, Joule 2019, 3, 2703. [54] X.Tang, K.Yao, B.Liu, W.Hu, F.Gao, Energies 2018, 11, 86. [55] K. A.Severson, P. M.Attia, N.Jin, N.Perkins, B.Jiang, Z.Yang, M. H.Chen, M.Aykol, P. K.Herring, D.Fraggedakis, M. Z.Bazant, S. J.Harris, W. C.Chueh, R. D.Braatz, Nat. Energy 2019, 4, 383. [56] D.Gao, M.Huang, J. Power Electron. 2017, 17, 756. [57] a) J. Wei, G.Dong, Z.Chen, IEEE Trans. Ind. Electron. 2018, 65, 5634; b) H.Dong, X.Jin, Y.Lou, C.Wang, J. Power Sources 2014, 271, 114; c) Y. Cheng, C.Lu, T. Li, L. Tao, Energy 2015, 90, 1983; d) Y. Zhang, Q. Tang, Y. Zhang, J. Wang, U. Stimming, A. A.Lee, Nat. Commun. 2020, 11, 1706; e) M. A.Patil, P.Tagade, K. S.Hariharan, S. M.Kolake, T.Song, T.Yeo, S.Doo, Appl. Energy 2015, 159, 285; f) D.Yang, X.Zhang, R.Pan, Y.Wang, Z.Chen, J. Power Sources 2018, 384, 387; g) T. Zahid, K.Xu, W. Li, C. Li, H.Li, Energy 2018, 162, 871. [58] a) T. F.Fuller, M.Doyle, J.Newman, J. Electrochem. Soc. 1994, 141, 1; b) M.Doyle, T. F.Fuller, J.Newman, J. Electrochem. Soc. 1993, 140, 1526; c) M.Doyle, J.Newman, A. S.Gozdz, C. N.Schmutz, J. M.Tarascon, J. Electrochem. Soc. 1996, 143, 1890. [59] G. F.Kennell, R. W.Evitts, Adv. Chem. Eng. Sci. 2012, 2, 12. [60] a) M.Tang, P.Albertus, J.Newman, J. Electrochem. Soc. 2009, 156, A390; b) K.Eberman, P.Gomadam, G.Jain, E.Scott, ECS Trans. 2010, 25, 47. [61] D. Miranda, C. M. Costa, A. M. Almeida, S. Lanceros-Méndez, Solid State Ionics 2015, 278, 78. [62] a) G. Inoue, H. Mashioka, Y. Tsuge, ECS Meet. Abstr. 2018, MA2018-02, 206; b) F.Röder, S.Sonntag, D.Schröder, U.Krewer, Energy Technol. 2016, 4, 1588. [63] G.Sikha, B. N.Popov, R. E.White, J. Electrochem. Soc. 2004, 151, A1104. [64] a) S. J.Cooper, D. S.Eastwood, J.Gelb, G.Damblanc, D. J. L.Brett, R. S.Bradley, P. J.Withers, P. D.Lee, A. J.Marquis, N. P.Brandon, P. R. Shearing, J. Power Sources 2014, 247, 1033; b) M. Ebner, D.-W. Chung, R. E. García, V. Wood, Adv. Energy Mater. 2014, 4, 1301278. [65] K. K.Patel, J. M.Paulsen, J.Desilvestro, J. Power Sources 2003, 122, 144. [66] a) M. Park, X. Zhang, M. Chung, G. B. Less, A. M. Sastry, J. Power Sources 2010, 195, 7904; b) H.Wolf, Z.Pajkic, T.Gerdes, M.Willert-Porada, J. Power Sources 2009, 190, 157; c) Y. G.Chirkov, V. I.Rostokin, A. M.Skundin, Russ. J. Electrochem. 2011, 47, 1239; d) S. Yu, Y. Chung, M. S. Song, J. H. Nam, W. I. Cho, J. Appl. Electrochem. 2012, 42, 443; e) Y. H.Chen, C. W.Wang, X.Zhang, A. M. Sastry, J. Power Sources 2010, 195, 2851; f) G. Sikha, R. E.White, J. Electrochem. Soc. 2008, 155, A893. [67] C. M.Costa, M. M.Silva, S.Lanceros-Méndez, RSC Adv. 2013, 3, 11404. [68] U.Sahapatsombut, H.Cheng, K.Scott, J. Power Sources 2013, 243, 409. [69] C.Cai, D.Hensley, G. M.Koenig, J. Energy Storage 2022, 54, 105218. [70] G. J.Nelson, B. N.Cassenti, A. A.Peracchio, W. K. S.Chiu, J. Electrochem. Soc. 2012, 159, A598. [71] J. Association for Chemical Innovation, Computer Simulation of Polymeric Materials: Applications of the OCTA System, Springer, Berlin/Heidelberg, Germany 2016. [72] D. V. Horváth, R. Tian, C. Gabbett, V. Nicolosi, J. N. Coleman, J. Electrochem. Soc. 2022, 169, 030503. [73] D. Miranda, C. M. Costa, A. M. Almeida, S. Lanceros-Méndez, Appl. Energy 2016, 165, 318. [74] D. Miranda, C. M. Costa, A. M. Almeida, S. Lanceros-Méndez, J. Electroanal. Chem. 2016, 780, 1. [75] R.Zhao, S.Zhang, J.Liu, J.Gu, J. Power Sources 2015, 299, 557. [76] D. Miranda, C. M. Costa, A. M. Almeida, S. Lanceros-Méndez, Energy 2018, 149, 262. [77] K.Shah, D.Chalise, A.Jain, J. Power Sources 2016, 330, 167. [78] S.Golmon, K.Maute, M. L.Dunn, Comput. Struct. 2009, 87, 1567. [79] B.Suthar, V.Ramadesigan, S.De, R. D.Braatz, V. R.Subramanian, Phys. Chem. Chem. Phys. 2014, 16, 277. [80] P.Singh, N.Khare, P. K.Chaturvedi, Int. J. Eng. Sci. Technol. 2018, 21, 35. [81] a) L. Sheng, X. Xie, C. Arbizzani, L. Bargnesi, Y. Bai, G. Liu, H. Dong, T. Wang, J. He, J. Membr. Sci. 2022, 657, 120644; b) X.Dai, X.Zhang, J.Wen, C.Wang, X.Ma, Y.Yang, G.Huang, H.-M.Ye, S.Xu, Energy Storage Mater. 2022, 51, 638. [82] a) P. Arora, Z. Zhang, Chem. Rev. 2004, 104, 4419; b) C. M.Costa, Y.-H.Lee, J.-H.Kim, S.-Y.Lee, S.Lanceros-Méndez, Energy Storage Mater. 2019, 22, 346; c) J. Nunes-Pereira, C. M.Costa, S.Lanceros-Méndez, J. Power Sources 2015, 281, 378; d) C. M. Costa, E. Lizundia, S. Lanceros-Méndez, Prog. Energy Combust. Sci. 2020, 79, 100846; e) J.Xing, J.Li, W.Fan, T.Zhao, X.Chen, H.Li, Y.Cui, Z.Wei, Y.Zhao, Composites, Part B 2022, 243, 110105. [83] K.Xu, Chem. Rev. 2004, 104, 4303. [84] a) L.Ding, D.Li, F.Du, D.Zhang, S.Zhang, R.Xu, T.Wu, J. Power Sources 2022, 543, 231838; b) J.Im, J. Ahn, J. Y. Kim, E. J. Park, S.Yoon, Y.-G.Lee, K. Y.Cho, Chem. Eng. J. 2022, 450, 138159. [85] a) X. Huang, J. Solid State Electrochem. 2011, 15, 649; b) V.Deimede, C.Elmasides, Energy Technol. 2015, 3, 453; c) Y.Wu, F.Yang, Y.Cao, M.Xiang, J.Kang, T.Wu, Q.Fu, Polymer 2021, 230, 124081; d) T.Wu, K.Wang, M.Xiang, Q.Fu, Chin. J. Chem. 2019, 37, 1207. [86] a) K.Bicy, A. B.Gueye, D.Rouxel, N.Kalarikkal, S.Thomas, Surf. Interfaces 2022, 31, 101977; b) Y.Li, Q.Li, Z.Tan, J. Power Sources 2019, 443, 227262. [87] a) T.-H. Cho, M. Tanaka, H. Ohnishi, Y. Kondo, M. Yoshikazu, T.Nakamura, T.Sakai, J. Power Sources 2010, 195, 4272; b) J.Hao, G.Lei, Z.Li, L.Wu, Q.Xiao, L.Wang, J. Membr. Sci. 2013, 428, 11. [88] a) J.-W.Lee, A. M.Soomro, M.Waqas, M. A. U.Khalid, K. H.Choi, Int. J. Energy Res. 2020, 44, 7035; b) C.-H. Chao, C.-T. Hsieh, W.-J.Ke, L.-W.Lee, Y.-F.Lin, H.-W.Liu, S.Gu, C.-C.Fu, R.-S.Juang, B. C.Mallick, Y. A.Gandomi, C.-Y.Su, J. Power Sources 2021, 482, 228896. [89] a) C.Cheng, H.Liu, C.Ouyang, N.Hu, G.Zha, H.Hou, Compos. Commun. 2022, 33, 101217; b) A. J.Manly, W. E.Tenhaeff, J. Mater. Chem. A 2022, 10, 10557. [90] a) R. R. Jacquemond, C. T.-C.Wan, Y.-M. Chiang, Z. Borneman, F. R.Brushett, K.Nijmeijer, A.Forner-Cuenca, Cell Rep. Phys. Sci. 2022, 3, 100943; b) X. Yao, X. Song, F. Zhang, J. Ma, H. Jiang, L. Wang, Y. Liu, E. Huixiang Ang, H. Xiang, ChemElectroChem 2022, 9, 202200390. [91] X. Xie, L. Sheng, R. Xu, X.Gao, L. Yang, Y. Gao, Y. Bai, G.Liu, H.Dong, X.Fan, T.Wang, X.Huang, J.He, J. Electroanal. Chem. 2022, 920, 116570. [92] a) Z.Wang, J.Chen, S.Sun, Z.Huang, X.Zhang, X.Li, H.Dong, Energy Storage Mater. 2022, 50, 161; b) J. Y.Kim, D. Y.Lim, Energies 2010, 3, 866. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.de www.advancedsciencenews.com 2203874 (20 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH [93] Y.Min, L.Guo, G.Wei, D.Xian, B.Zhang, L.Wang, Chem. Eng. J. 2022, 443, 136480. [94] W.Chen, X.Wang, J.Liang, Y.Chen, W.Ma, S.Zhang, Membranes 2022, 12, 124. [95] S. H.Kang, H. Y.Jeong, T. H.Kim, J. Y.Lee, S. K.Hong, Y. T.Hong, J.Choi, S.So, S. J.Yoon, D. M.Yu, Polymers 2022, 14, 1649. [96] J.Gou, W.Liu, A.Tang, L.Wu, J. Membr. Sci. 2022, 659, 120807. [97] M.Li, K.Wang, J.Liu, F.Shen, C.Xu, X.Han, J. Colloid Interface Sci. 2022, 622, 1029. [98] X.Li, K.Liu, Y.Yan, J.Yu, N.Dong, B.Liu, G.Tian, S.Qi, D.Wu, J. Colloid Interface Sci. 2022, 625, 936. [99] Y. D.Lee, J.Yuenyongsuwan, P.Nanthananon, Y. K.Kwon, Polymer 2022, 255, 125110. [100] J.Liu, D.Cao, H.Yao, D.Liu, X.Zhang, Q.Zhang, L.Chen, S.Wu, Y.Sun, D.He, J.Liu, ACS Appl. Energy Mater. 2022, 5, 8639. [101] X.Gao, L.Sheng, M.Li, X.Xie, L.Yang, Y.Gong, M.Cao, Y.Bai, H. Dong, G. Liu, T. Wang, X. Huang, J. He, ACS Appl. Polym. Mater. 2022, 4, 5125. [102] M.Qiao, G.Zhang, J.Deng, J.Guo, J.Zhang, J. Mater. Sci. 2022, 57, 11796. [103] W. Tang, Q. Liu, N. Luo, F. Chen, Q. Fu, Compos. Sci. Technol. 2022, 225, 109479. [104] H. Jeong, J. Hwang, J. Kim, W.-J. Song, K. J. Lee, Mater. Chem. Phys. 2022, 288, 126354. [105] A. J.Manly, W. E.Tenhaeff, Electrochim. Acta 2022, 425, 140705. [106] W.Wang, A. C. Y.Yuen, Y.Yuan, C.Liao, A.Li, I. I.Kabir, Y.Kan, Y.Hu, G. H.Yeoh, Chem. Eng. J. 2023, 451, 138496. [107] S.Nag, A.Pramanik, S.Roy, S.Mahanty, J. Solid State Chem. 2022, 312, 123214. [108] Y.Pan, L.-Y.He, X.-Y.Qiu, X.Li, J. Electron. Mater. 2022, 51, 4307. [109] L.Yu, J.Gu, C.Pan, J.Zhang, Z.Wei, Y.Zhao, Composites, Part A 2022, 162, 107132. [110] W.Zhai, H.Yu, H.Chen, L.Li, D.Li, Y.Zhang, T.He, Sep. Purif. Technol. 2022, 293, 121091. [111] Y. Yang, N. Li, T. Lv, Z. Chen, Y. Liu, K. Dong, S. Cao, T. Chen, Nanoscale Adv. 2022, 4, 1718. [112] a) E.Lizundia, C. M.Costa, R.Alves, S.Lanceros-Méndez, Carbohydr. Polym. Technol. Appl. 2020, 1, 100001; b) H.Zhang, S.Wang, A.Wang, Y. Li, F.Yu, Y. Chen, Appl. Surf. Sci. 2022, 593, 153411; c) Y.Xie, H.Zhu, R.Zeng, B.Na, S.Zou, C.Chen, J. Power Sources 2022, 538, 231562; d) X.Zheng, J.Wu, X.Wang, Z.Yang, Chem. Eng. J. 2022, 446, 137194. [113] a) R. F. P. Pereira, R. Brito-Pereira, R. Gonçalves, M. P. Silva, C. M.Costa, M. M.Silva, V.deZea Bermudez, S.Lanceros-Méndez, ACS Appl. Mater. Interfaces 2018, 10, 5385; b) A. Reizabal, R. Gonçalves, A. Fidalgo-Marijuan, C. M. Costa, L. Pérez, J.-L.Vilas, S.Lanceros-Mendez, J. Membr. Sci. 2020, 598, 117678. [114] A.Reizabal, A.Fidalgo-Marijuan, R.Gonçalves, A.Gutiérrez-Pardo, F. Aguesse, L. Pérez-Álvarez, J. L. Vilas-Vilela, C. M. Costa, S.Lanceros-Mendez, J. Colloid Interface Sci. 2022, 611, 366. [115] a) W.Xiao, L.Zhao, Y.Gong, J.Liu, C.Yan, J. Membr. Sci. 2015, 487, 221; b) L.Ding, N.Yan, S.Zhang, R.Xu, T.Wu, F.Yang, Y.Cao, M.Xiang, Electrochim. Acta 2022, 403, 139568; c) N. Kassenova, S. Kalybekkyzy, M. V. Kahraman, A. Mentbayeva, Z. Bakenov, J. Power Sources 2022, 520, 230896. [116] A. Liu, Z. Jiang, S. Li, J. Du, Y. Tao, J. Lu, Y. Cheng, H. Wang, Int. J. Biol. Macromol. 2022, 213, 690. [117] J. P.Serra, A.Fidalgo-Marijuan, J.Teixeira, L.Hilliou, R.Gonçalves, K. Urtiaga, A. Gutiérrez-Pardo, F. Aguesse, S.Lanceros-Mendez, C. M.Costa, Adv. Sustainable Syst. 2022, 6, 2200279. [118] Y.Chen, J.Li, Y.Ju, R.Cheng, Y.Zhai, J.Sheng, H.Liu, L.Li, Appl. Surf. Sci. 2022, 592, 153222. [119] a) Y. Feng, M. Wang, L. Gao, Z. He, K. Chen, Z. Li, H. He, Y. Lin, J. Materiomics 2022, 8, 1184; b) S. Ahankari, D. Lasrado, R.Subramaniam, Mater. Adv. 2022, 3, 1472. [120] M.Kim, J. K.Kim, J. H.Park, Adv. Funct. Mater. 2015, 25, 3399. [121] Y.Kang, C.Deng, Z.Wang, Y.Chen, X.Liu, Z.Liang, T.Li, Q.Hu, Y.Zhao, Nanoscale Res. Lett. 2020, 15, 107. [122] a) E. Shekarian, M. R. Jafari Nasr, T.Mohammadi, O. Bakhtiari, M.Javanbakht, J. Appl. Polym. Sci. 2019, 136, 47841; b) W.Jiang, Y.Han, Y.Ding, Nanotechnology 2022, 33, 425601. [123] a) J. Li, L. Chen, F. Wang, Z. Qin, Y. Zhang, N. Zhang, X. Liu, G. Chen, Chem. Eng. J. 2023, 451, 138536; b) X. Wu, C. Zhou, C.Dong, C.Shen, B.Shuai, C.Li, Y.Li, Q.An, X.Xu, L.Mai, Nano Res. 2022, 15, 8048; c) A. Valverde, R. Gonçalves, M. M. Silva, S. Wuttke, A. Fidalgo-Marijuan, C. M. Costa, J. L. Vilas-Vilela, J. M. Laza, M. I. Arriortua, S. Lanceros-Méndez, R. Fernández de Luis, ACS Appl. Energy Mater. 2020, 3, 11907; d) R. Freund, O. Zaremba, G. Arnauts, R. Ameloot, G. Skorupskii, M. Dincă, A.Bavykina, J.Gascon, A.Ejsmont, J.Goscianska, M.Kalmutzki, U.Lächelt, E.Ploetz, C. S.Diercks, S.Wuttke, Angew. Chem., Int. Ed. 2021, 60, 23975. [124] a) S. Aadheeshwaran, K. Sankaranarayanan, V. Ganesh, Ionics 2021, 27, 607; b) Y.Luo, H.Bai, B.Li, X.Song, J.Zhao, Y.Xiao, S.Lei, B.Cheng, J. Alloys Compd. 2021, 879, 160368. [125] D.Cao, J.Deng, L.Jiang, X.Li, G.Zhang, J. Energy Storage 2022, 55, 105496. [126] Y.Min, X.Liu, L.Guo, A.Wu, D.Xian, B.Zhang, L.Wang, ACS Appl. Energy Mater. 2022, 5, 9131. [127] J. C. Barbosa, R. Gonçalves, A. Valverde, P. M. Martins, V. I.Petrenko, M.Márton, A.Fidalgo-Marijuan, R.Fernández de Luis, C. M.Costa, S.Lanceros-Méndez, Chem. Eng. J. 2022, 443, 136329. [128] A. A. Franco, A. Rucci, D. Brandell, C. Frayret, M. Gaberscek, P.Jankowski, P.Johansson, Chem. Rev. 2019, 119, 4569. [129] K. Shah, V.Vishwakarma, A.Jain, J. Electrochem. Energy Convers. Storage 2016, 13, 030801. [130] W.Xie, Y.Dang, L.Wu, W.Liu, A.Tang, Y.Luo, Polym. Test. 2020, 90, 106773. [131] Y.Kawagoe, D.Surblys, G.Kikugawa, T.Ohara, AIP Adv. 2019, 9, 025302. [132] S.Yan, X.Xiao, X.Huang, X.Li, Y.Qi, Polymer 2014, 55, 6282. [133] Y.Saito, W.Morimura, R.Kuratani, S.Nishikawa, J. Phys. Chem. C 2016, 120, 3619. [134] R.Zahn, M. F.Lagadec, M.Hess, V.Wood, ACS Appl. Mater. Interfaces 2016, 8, 32637. [135] A.Ehrl, J.Landesfeind, W. A.Wall, H. A.Gasteiger, J. Electrochem. Soc. 2017, 164, A2716. [136] K. Yoo, A. Deshpande, S. Banerjee, P. Dutta, Electrochim. Acta 2015, 176, 301. [137] V.Zadin, D.Danilov, D.Brandell, P. H. L.Notten, A.Aabloo, Electrochim. Acta 2012, 65, 165. [138] M. F.Lagadec, R. Zahn, S. Müller, V.Wood, Energy Environ. Sci. 2018, 11, 3194. [139] C. Sauter, R. Zahn, V. Wood, J. Electrochem. Soc. 2020, 167, 100546. [140] a) M. Gilaki, I. Avdeev, J. Power Sources 2016, 328, 443; b) S.Kalnaus, A.Kumar, Y.Wang, J.Li, S.Simunovic, J. A.Turner, P.Gorney, J. Power Sources 2018, 378, 139. [141] R. Gonçalves, T. Marques-Almeida, D. Miranda, M. M. Silva, V. F. Cardoso, C. M. Costa, S. Lanceros-Méndez, Energy Storage Mater. 2019, 21, 124. [142] R. Gonçalves, D. Miranda, T. Marques-Almeida, M. M. Silva, V. F.Cardoso, A. M.Almeida, C. M.Costa, S.Lanceros-Méndez, J. Colloid Interface Sci. 2021, 596, 158. [143] P.Priimägi, H.Kasemägi, A.Aabloo, D.Brandell, V.Zadin, Electrochim. Acta 2017, 244, 129. [144] Y.-k. Ahn, J. Park, D. Shin, S. Cho, S. Y. Park, H. Kim, Y. Piao, J.Yoo, Y. S.Kim, J. Mater. Chem. A 2015, 3, 10715. [145] D.Shi, X.Xiao, X.Huang, H.Kia, J. Power Sources 2011, 196, 8129. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.dewww.advancedsciencenews.com 2203874 (21 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH [146] M. Zhuo, D. Grazioli, A. Simone, Electrochim. Acta 2021, 393, 139045. [147] a) X. Xiao, W. Wu, X. Huang, J. Power Sources 2010, 195, 7649; b) W.Wu, X.Xiao, X.Huang, S.Yan, Comput. Mater. Sci. 2014, 83, 127. [148] X.Zhang, E.Sahraei, K.Wang, J. Power Sources 2016, 327, 693. [149] D. Grazioli, V. Zadin, D. Brandell, A. Simone, Electrochim. Acta 2018, 296, 1142. [150] M. F.Lagadec, R.Zahn, V.Wood, J. Electrochem. Soc. 2018, 165, A1829. [151] A.Jana, D. R.Ely, R. E.García, J. Power Sources 2015, 275, 912. [152] H.Xu, C.Bae, J. Power Sources 2019, 430, 67. [153] D. Sauerteig, N. Hanselmann, A. Arzberger, H. Reinshagen, S.Ivanov, A.Bund, J. Power Sources 2018, 378, 235. [154] Y.Li, G.Zhang, B.Chen, W.Zhao, L.Sha, D.Wang, J.Yu, S.Shi, Chin. Chem. Lett. 2022, 33, 3287. [155] A.Cannon, E. M.Ryan, ACS Appl. Energy Mater. 2021, 4, 7848. [156] J.Liang, Q.Chen, X.Liao, P.Yao, B.Zhu, G.Lv, X.Wang, X.Chen, J.Zhu, Angew. Chem., Int. Ed. 2020, 59, 6561. Daniel Miranda graduated in Physics and Chemistry in 2005 and obtained his Master’s Degree in Physics in 2008. In 2017, he received a Ph.D. in Physics from the Science School of the University of Minho. Currently, he is an Adjunct Professor invited at School of Technology, Polytechnic Institute of Cávado and Ave (IPCA), Portugal, and integrated member/researcher of 2Ai Laboratory at IPCA. His work focuses on the development of theoretical models of lithium-ion batteries through computational simulation (Finite Elements Method) and computational simulation and modeling applied in energy, energy systems, and storage energy applications. Renato Gonçalves, Ph.D., has a background in Chemistry and Chemistry Analysis Characterization Techniques and obtained his Ph.D. degree in Materials Engineering (2017) at the University of Minho, Portugal. Since 2019, he is working at Chemistry Centre of the University of Minho, Portugal as a researcher being involved in various national and international research projects and scientific international collaborations (Cambridge, Jodhpur, Bilbao). His current research interest involves the development of materials, synthesis, and printing techniques for advanced applications and new energy storage materials, including Lithium-ion batteries. He has published more than 80 papers and book chapters as author and co-author. Stefan Wuttke created the research group “WuttkeGroup for Science”, initially hosted at the Institute of Physical Chemistry at the University of Munich (LMU, Germany). Currently, he is an Ikerbasque Professor at the Basque Center for Materials, Applications, and Nanostructures (BCMaterials, Spain). His principal focus is the design, synthesis, and functionalization of MOFs and their nanometric counterparts to target diverse applications. At the same time, he aims to establish a basic understanding of the chemical and physical elementary processes involved in the synthesis, functionalization, and application of these hybrid materials. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License www.advenergymat.de www.advancedsciencenews.com 2203874 (22 of 22) © 2023 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH Carlos M. Costa graduated in Physics in 2005 and obtained his Master’s Degree in Materials Engineering in 2007. In 2014, he received a Ph.D. in Physics from the Science School of the University of Minho. Currently, He is an Assistant researcher at the Center of Physics of the same university. His work focuses on the development of sustainable materials for sensors and actuators as well as on the development of electroactive polymer-based porous membranes, anode and cathode material for energy storage applications: lithium-ion batteries, printed batteries. Senentxu Lanceros-Mendez is Ikerbasque Professor at BCMaterials, Basque Center for Materials, Applications and Nanostructures, Leioa, Spain, where he is the Scientific Director. He is an Associate Professor at the Physics Department of the University of Minho, Portugal (on leave), which also belongs to the Center of Physics. He graduated in Physics at the University of the Basque Country, Leioa, Spain and obtained his Ph.D. degree at the Institute of Physics of the Julius-Maximilians-Universität Würzburg, Germany. His work is focused on the development of smart and multifunctional materials for sensors and actuators, energy, environmental, and biomedical applications. Adv. Energy Mater. 2023, 13, 2203874 16146840, 2023, 13, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/aenm.202203874 by Universidade Do Minho, Wiley Online Library on [09/11/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License