Tortuosity Factor Research Articles

Digital Twin Reveals the Impact of Carbon Binder Domain Distribution on Performance of Lithium‐Ion Battery Cathodes

The rising demand for high‐performance lithium‐ion batteries (LIBs) emphasizes the need for precise electrode design. The carbon binder domain (CBD) within electrodes, crucial for electron transport and structural integrity, can impede lithium‐ion transport and reduce electrochemically active sites. This study leverages digital twin technology to investigate the impact of CBD distribution along the electrode thickness on LIB cathode performance. Virtual LIB cathodes with varying CBD configurations are generated, and their performance using 3D heterogeneous electrochemical modeling is evaluated. The results reveal that a steep CBD gradient significantly diminishes charge capacity compared to a mild gradient, particularly at higher current rates. This reduction is attributed to an increased tortuosity factor and an uneven distribution of electrochemical activity, which adversely affect mass transport and electrochemical reaction dynamics. These effects are more pronounced at lower porosities and higher loading levels. These findings highlight that optimizing CBD distribution is critical for advancing electrode design and enhancing LIB cathode performance.

3D microstructure reconstruction and characterization of porous materials using a cross-sectional SEM image and deep learning

Accurate assessment of the three-dimensional (3D) pore characteristics within porous materials and devices holds significant importance. Compared to high-cost experimental approaches, this study introduces an alternative method: utilizing a generative adversarial network (GAN) to reconstruct a 3D pore microstructure. Unlike some existing GAN models that require 3D images as training data, the proposed model only requires a single cross-sectional image for 3D reconstruction. Using porous ceramic electrode materials as a case study, a comparison between the GAN-generated microstructures and those reconstructed through focused ion beam-scanning electron microscopy (FIB-SEM) reveals promising consistency. The GAN-based reconstruction technique demonstrates its effectiveness by successfully characterizing pore attributes in porous ceramics, with measurements of porosity, pore size, and tortuosity factor exhibiting notable agreement with the results obtained from mercury intrusion porosimetry.

Carbon-Binder-Domain porosity extraction through lithium-ion battery electrode impedance data

In the field of 3-D resolved computational modelling of Lithium-ion battery electrodes, the arrangement and properties of the Carbon-Binder-Domain (CBD) play a critical role in the ion and electron transport properties through their impact on the electrode tortuosity factor. However, until now, the CBD porosity value -its main descriptor in terms of transport properties and occupied volume- has been determined through educated guesses due to the lack of an experimental approach. Here, a novel methodology is reported for the determination of the CBD internal porosity through the combination of computational modelling and experimental electrochemical impedance spectroscopy (EIS). The methodology is based on the creation of a calibration curve that relates tortuosity factor with CBD porosity through digital stochastic generation of electrode microstructures and diffusivity characterization. The curve is then compared to the EIS experimental results and analyzed through a transmission line model, yielding a good estimation of the parameters. In this work, the usefulness and the identified limitation of this approach are demonstrated using three different formulations of LiNi0.3Mn0.3Co0.3O2 (NMC 111) cathodes. To the best of the authors’ knowledge, this is the first reported method for estimating CBD porosity.

Petrophysical Properties Estimation by Using Well Log and Core Data Interpretation for Tertiary Reservoir in Ajeel Oil Field

Understanding the petrophysical properties of reservoirs is paramount for assessing their characteristics and estimating the potential of oil-bearing formations. This study focuses on the tertiary reservoir in the Ajeel oil field, renowned for its heterogeneous carbonate nature and diverse production formations, including Jeribe, Dhiban, and Euphrates. The formation of the tertiary reservoir is evaluated through a comprehensive analysis of petrophysical properties utilizing well log data from well AJ-25 and core analysis data (routine and special analysis). The study reveals significant insights into the petrophysical properties of the formations. The effective porosity values for the Jeribe, Dhiban, and Euphrates formations are determined as 15.41%, 4.65%, and 9.85%, respectively, with lithology predominantly consisting of dolomite rock interspersed with limestone slices and anhydrite nodules. A high Secondary Porosity Index (SPI) indicates fractures. Archie's Parameters, obtained from a Pickett Plot, yield cement coefficient (m=1.8), saturation exponent (n=1.9), and tortuosity factor (a=1.19). Water saturation levels for the Jeribe, Dhiban, and Euphrates formations are 33%, 86%, and 81%, respectively, with porosity and water saturation cutoff values determined as 10% and 60%, respectively. Net to Gross of Jeribe, Dhiban, and Euphrates formation are 0.706, 0.029, and 0.011, respectively. Jeribe Formation is identified as an oil-bearing zone. In contrast, the Dhiban and Euphrates formations are identified as water-bearing zones. To enhance the accuracy of petrophysical assessments, modern well logging techniques, such as the Formation Micro Scanner log (FMI log), are recommended to investigate fractures and their impact on field performance, as traditional logs may not adequately capture these features. Additionally, our comparative analysis highlights superior petrophysical properties of the Euphrates Formation compared to the Jeribe Formation. Therefore, prioritizing drilling and completion activities in the Euphrates Reservoir within the field, contingent upon its positioning relative to the oil-water contact level, is suggested for optimizing reservoir performance.

Powder bed fusion with electron beam: The interplay of sintering, porosity, and coordination number in modelling the powder thermal conductivity through a novel tortuosity formulation

For the powder bed fusion with electron beam (PBF-EB) additive manufacturing, properties such as the thermal conductivity of the material surrounding the melting area are critical. Thermal conductivity is influenced by the extremely high temperature reached in a short time and distributed in the building area. This fast temperature growth produces sintering phenomena and the creation of a neck between the particles. Because of this sintering, measuring the thermal conductivity at the process conditions is challenging.This paper proposes an analytical formulation for estimating the effective powder bed thermal conductivity at the PBF-EB conditions, introducing a novel modelling strategy for the tortuosity factor. In a changing net of sintered powder particles, the proposed model for the tortuosity factor considers the neck evolution and the complexity of the heat transfer due to the several heat paths possible through the particle net. To show the effectiveness of the proposed model, the thermal conductivity is evaluated for three 3D structures characterised by an increasing number of powder particles and heat path complexity: a simple cubic, a body centred cubic and a portion of a powder bed. It is shown that thermal conductivity strongly depends on the arrangement of the particles in 3D space, the structure density and the complexity of the heat diffusion path (tortuosity). Also, the numerical results from the proposed model show good agreement when compared with finite element analysis and experimental literature data.

Factors influencing hydrogen migration in cap rocks: Establishing new screening criteria for the selection of underground hydrogen storage locations

Safe and efficient underground storage of hydrogen requires cap rocks with effective sealing capabilities. This study investigates the interplay between various factors and their influence on hydrogen leakage from cap rocks. Compared to other commonly stored gases, hydrogen exhibits a significantly faster leakage rate. This study reveals that pore size and tortuosity of cap rocks play a crucial role. Larger pore sizes and lower tortuosity factors lead to increased leakage. Conversely, higher tortuosity (more twisted pores) enhances the sealing capability. Cap rocks with a tortuosity greater than 3 or an effective pore size below 5 nm demonstrated exceptional sealing capabilities. Beyond pore geometry, the study underscores the dependence of hydrogen diffusivity on prevailing underground conditions. Higher reservoir temperatures translate to a faster leakage rate, whereas increased pressure reduces the mean free path of hydrogen molecules, hindering diffusion and promoting retention within the storage formation. Reservoirs of temperature below 350 K and under a pressure exceeding 13.80MPa (2000 psi) exhibited minimal leakage. These findings underscore the crucial role of meticulous cap rock selection for safe and efficient underground hydrogen storage. This research paves the way for future investigations exploring the impact of additional factors such as the presence of other gases, the chemical composition of the cap rock itself, and the long-term effects of hydrogen exposure on cap rock integrity. A comprehensive understanding of these factors is essential for optimizing underground hydrogen storage strategies and ensuring long-term environmental sustainability.

Structural and Electrochemical Investigation of Anode‐Supported Proton‐Conducting Solid Oxide Fuel Cell Fabricated by the Freeze Casting Process

ABSTRACTHierarchically oriented macroporous NiO–BaZr0.1Ce0.7Y0.2O3−δ (BZCY7) anode‐supporting layer (ASL) was developed using the freeze casting technique. The resulting freeze‐cast structure was analyzed through scanning electron microscopy and X‐ray computed tomography. A thin layer of BZCY7 was utilized as a proton‐conducting electrolyte, whereas La1.9Sr0.1Ni0.7Cu0.3O3−δ –gadolinium‐doped ceria 10% Gd (LSNC–GDC10) was employed and evaluated as cathode layer. The performance of the cell was assessed by means of electrochemical impedance spectroscopy and I–V–P curves at various temperatures. Furthermore, as a point of comparison, a cell with an ASL was prepared using the dry pressing method, incorporating 20 wt.% graphite as a pore‐forming agent. The freeze‐cast anode‐supported cell demonstrated a polarization resistance of 1.45 Ω cm2 at 550°C and 0.29 Ω cm2 at 750°C. Maximum achieved power densities were 0.189 and 0.429 W cm−2 at 550 and 750°C, respectively. For the cell fabricated by the dry pressing method, the maximum power densities were 0.158 and 0.397 W cm−2 at 550 and 750°C, respectively. Additionally, the tortuosity factor of the anode layer and the gas diffusion streamline in the direction of solidification were determined by using 3D X‐ray tomography imaging and subsequent image processing.

Influence of semi-crystalline microstructure on gas permeability of Poly(Ether-Ketone-Ketone)

In a context of ecological transition, aeronautics is moving towards the development of hydrogen-powered aircraft for which lightweight, long-lasting and tough storage tanks must be developed. Among other properties, the tightness of tanks made of polymer and composite materials is of prime importance. This study thus aims at understanding the permeation phenomena through Poly-Ether-Ketone-Ketone (PEKK), which is one of the thermoplastic polymers considered for tank manufacture. In order to better understand the phenomena governing gas permeability, tests were carried out on different PEKK grades of different T/I ratios and degree of crystallinity. The results show that the crystallinity ratio is not the only factor that governs gas diffusion and solubility, but that permeability also depends on PEKK semi-crystalline microstructure. Even if considering crystallites organization through a tortuosity factor improves permeability predictions, permeability can be better understood using a 3 phases description of PEKK microstructure considering distinct contributions of the rigid and mobile amorphous fractions (RAF and MAF). It appears that the MAF permeation does not depend on PEKK T/I ratio, but that gas diffusion and solubility increase for RAF when increasing T/I ratio, thus counterbalancing the blocking effect of crystallites.

Editors’ Choice—Visualizing the Impact of the Composite Cathode Microstructure and Porosity on Solid-State Battery Performance

The kinetics of composite cathodes for solid-state batteries (SSBs) relies heavily on their microstructure. Spatial distribution of the different phases, porosity, interface areas, and tortuosity factors are important descriptors that need accurate quantification for models to predict the electrochemistry and mechanics of SSBs. In this study, high-resolution focused ion beam-scanning electron microscopy tomography was used to investigate the microstructure of cathodes composed of a nickel-rich cathode active material (NCM) and a thiophosphate-based inorganic solid electrolyte (ISE). The influence of the ISE particle size on the microstructure of the cathode was visualized by 3D reconstruction and charge transport simulation. By comparison of experimentally determined and simulated conductivities of composite cathodes with different ISE particle sizes, the electrode charge transport kinetics is evaluated. Porosity is shown to have a major influence on the cell kinetics and the evaluation of the active mass of electrochemically active particles reveals a higher fraction of connected NCM particles in electrode composites utilizing smaller ISE particles. The results highlight the importance of homogeneous and optimized microstructures for high performance SSBs, securing fast ion and electron transport.

Adsorption of Diffusing Tracers, Apparent Tortuosity, and Application to Mesoporous Silica.

The apparent tortuosity due to adsorption of diffusing tracers in a porous material is determined by a scaling approach and is used to analyze recent data on LiCl and alkane diffusion in mesoporous silica. The slope of the adsorption isotherm at small loadings is written as β = qA/qG, where qA is the adsorption-desorption ratio and qG = ϵ/(as) - 1 is a geometrical factor depending on the range a of the tracer-wall interaction, the porosity ϵ, and the specific surface area s. The adsorption leads to a decrease of effective diffusion coefficient, which is quantified by multiplying the geometrical tortuosity factor τgeom by an apparent tortuosity factor τapp. In wide pores or when the adsorption barrier is high, τapp = β + 1, as obtained in previous works, but in narrow pores there is an additional contribution from frequent adsorption-desorption transitions. These results are obtained in media with parallel pores of constant cross sections, where the ratio between the effective pore width ϵ/s and the actual width is ≈0.25. Applications to mesoporous silica samples are justified by the small deviations from this ideal ratio. In the analysis of alkane self-diffusion data, the fractions of adsorbed molecules predicted in a recent theoretical work are used to estimate τgeom of the silica samples, which is ≫1 only in the sample with the narrowest pores (nominal 3 nm). The application of the model to Li+ ion diffusion leads to similar values of τgeom and to a difference of energy barriers of desorption and adsorption for those ions of ∼0.06 eV. Comparatively, alkane self-diffusion provides the correct order of magnitude of τgeom, with adsorption playing a less important role, whereas adsorption effects on Li+ diffusion are much more important.

SEM Image Processing Assisted by Deep Learning to Quantify Mesoporous γ-Alumina Spatial Heterogeneity and Its Predicted Impact on Mass Transfer.

The pore network architecture of porous heterogeneous catalyst supports has a significant effect on the kinetics of mass transfer occurring within them. Therefore, characterizing and understanding structure-transport relationships is essential to guide new designs of heterogeneous catalysts with higher activity and selectivity and superior resistance to deactivation. This study combines classical characterization via N2 adsorption and desorption and mercury porosimetry with advanced scanning electron microscopy (SEM) imaging and processing approaches to quantify the spatial heterogeneity of γ-alumina (γ-Al2O3), a catalyst support of great industrial relevance. Based on this, a model is proposed for the spatial organization of γ-Al2O3, containing alumina inclusions of different porosities with respect to the alumina matrix. Using original, advanced SEM image analysis techniques, including deep learning semantic segmentation and porosity measurement under gray-level calibration, the inclusion volume fraction and interphase porosity difference were identified and quantified as the key parameters that served as input for effective tortuosity factor predictions using effective medium theory (EMT)-based models. For the studied aluminas, spatial porosity heterogeneity impact on the effective tortuosity factor was found to be negligible, yet it was proven to become significant for an inclusion content of at least 30% and an interphase porosity difference of over 20%. The proposed methodology based on machine-learning-supported image analysis, in conjunction with other analytical techniques, is a general platform that should have a broader impact on porous materials characterization.

3D porosity, flow, and transport characteristics of two L chondrites reveal wet accretion-related cosmic web-like porosity

Porosity, with its structure-dependent flow properties (permeability and tortuosity) and transport properties (thermal conductivity and thermal diffusivity), is closely related to the accretion, thermal metamorphism, and associated hydrothermal alteration of ordinary chondrite (OC) parent bodies. Using synchrotron radiation microtomography (SRμCT), we reveal the varying porosity structures in two L chondrite falls of low (Mezö-Madaras L3.7) and high (Bath Furnace L6) petrologic types and quantify porosity properties, such as shape and connectivity, and related effective permeability and tortuosity factor. Although the two specimens demonstrate similar effective permeabilities, they exhibit significantly different tortuosity factors and textures of porosity, which include notable differences in void throat diameters, complexity and density of the interconnected void network, heterogeneity in void distribution, and the extent of primary and secondary porosity. The complex relationships among porosity, permeability, tortuosity, and thermal conductivity can be explained by the varying void arrangements related to varying grain sizes among the petrologic types of OCs, which in turn reflect their varying evolutionary paths.Electron microprobe and attached energy-dispersive X-ray spectrometer reveal signs of hydrothermal alteration in both petrologic types. High-energy SRμCT imaging (0.65 μm voxel size) reveals the presence of a new microporosity substructure resembling a microscopic cosmic web, which may be linked to fluid-assisted metamorphism and hydrothermal alteration during wet accretion of the parent body. Furthermore, the proportion of this continuous porosity may be related to the temperatures associated with different petrologic types, and the wet accretion model may resolve the lack of correlation between petrologic types and porosity of OCs. Finally, the uncovered cosmic web-like microporosity structure may explain the observed concurrent high thermal conductivity, low permeability, and high porosity of the high-petrologic-type OCs.

Experimental study on impedance dispersion characteristics and electrical resistivity of high temperature granite

Electromagnetic methods are widely used in geothermal exploration. The electrical resistivity and impedance dispersion characteristics of high temperature rocks are the basis for the inversion interpretation of electromagnetic responses from geothermal reservoirs. Alternating current impedance spectroscopy experiments on granite at different temperatures and water contents were carried out. The impedance spectrum from these tests show that the resistivity of both dry and wet granite is mainly caused by grain boundaries or microfractures, rather than the mineral grains. The resistivity of granite decreases dramatically with the appearance of water, and decreases with increasing temperature. When temperature increases from 25 °C to 150 °C, the resistivity of dry granite decreases from 5690–5812 Ω·m to 5519–5638 Ω·m, while that of wet granite decreases from 3123–3278 Ω·m to 2618–2736 Ω·m. The relationship between temperature and resistivity can be described by an empirical equation, in which the temperature coefficient of dry granite is about 2.8 × 10−4, and that of wet granite is about 1.7 × 10−3. It shows the resistivity of wet granite is more significantly affected by temperature than that of dry granite. A mixing model describing resistivity of wet granite was established based on the resistivity of dry granite and water. This mixing model can also take temperature into consideration. Even though the equivalent porosity of granite is quite small, the resistivity of granite can still be described by Archie's equation, which was initially developed for high porosity sandstone. The coefficients in Archie's equation for granite were calculated from experimental results, with a tortuosity factor a = 0.005 and a cementation exponent m ranging from 1.50 to 1.75.

Time‐Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction

AbstractThe manufacturing process of Lithium‐ion battery electrodes directly affects the practical properties of the cells, such as their performance, durability, and safety. While computational physics‐based modeling has been proven as a very useful method to produce insights on the manufacturing properties interdependencies as well as the formation of electrode microstructures, their high computational costs prevent their direct utilization in electrode optimization loops. In this work, a novel time‐dependent deep learning (DL) model of the battery electrodes manufacturing process is reported, demonstrated for calendering of nickel manganese cobalt (NMC111) electrodes, and trained with time‐series data arising from physics‐based Discrete Element Method (DEM) simulations. The DL model predictions are validated by comparing evaluation metrics (e.g., mean square error (MSE) and R2 score) and electrode functional metrics (contact surface area, porosity, diffusivity, and tortuosity factor), showing very good accuracy with respect to the DEM simulations. The DL model can remarkably capture the elastic recovery of the electrode upon compression (spring‐back phenomenon) and the main 3D electrode microstructure features without using the functional descriptors for its training. Furthermore, the DL model has a significantly lower computational cost than the DEM simulations, paving the way toward quasi‐real‐time optimization loops of the 3D electrode architecture predicting the calendering conditions to adopt in order to obtain the desired electrode performance.

Mesoscopic Model of Extrusion during Solvent‐Free Lithium‐ion Battery Electrode Manufacturing**

AbstractSolvent‐free (SF) manufacturing of lithium‐ion battery (LIB) electrodes is safer and more environmentally friendly than the traditional slurry casting approach. However, as a young technique, SF manufacturing is under development of its pathways and operation conditions. In different SF processes reported in literature, extrusion is a common step. A detailed model of this process would be extremely computationally demanding. This work proposes a novel simplified discrete element model at the mesoscopic scale for the extrusion during SF manufacturing of LIB electrodes. In addition to active material particles, we consider fluid‐like solid particles to approximate the molten polymer and the carbon additive phases. The formulation and other process parameters are taken from our experimental facility that uses extrusion to fabricate filaments for 3D printing of LIB cells. The extrusion is carried out in a conical twin screw extruder. Our approach allows to obtain representative electrode microstructures after extrusion, where electrical conductivity, ionic effective diffusivity, tortuosity factor and porosity are calculated. The model is a proof of concept that is employed to investigate the influence of the extruder speed and the cohesion level on the resulting electrode properties.

Study of restricted diffusion of lithium salts in diglyme confined in mesoporous carbons as a model for cathodes in lithium-air batteries.

The Li+ ion mobility through the porous cathode is a critical aspect in the development of commercial Li-air batteries. The bulk transport properties of lithium salts in organic solvents are not reliable parameters for the design of this type of battery since confinement could significantly modify the transport properties, especially when pore diameters are below 10 nm. In this work, we studied the effect of the carbon mesostructure and surface charge on the diffusion of LiTf and LiTFSI salts dissolved in diglyme, typical electrolytes for lithium-air batteries. Interdiffusion coefficients of the salts were determined using a conductimetric method. NMR spectroscopy and relaxometry were used to explore the effect of the carbon structure and the surface charge density on the interaction between the electrolytes and the pore wall. We showed that carbon micro/mesoporous structure plays a critical role in the transport properties of the electrolyte, producing a decrease of up to 2-3 orders of magnitude in the salt interdiffusion coefficients when going from bulk solutions to pores below 4 nm in diameter. It was observed that for pores 25 nm in diameter, the reduction in the diffusion coefficient can be mainly ascribed to the porosity of the sample, giving tortuosity factors around 1. However, for smaller pore sizes (1-10 nm diameter) bigger tortuosity coefficients were observed and were related to strong ion-pore wall interactions. Moreover, it was noticed that the ratio between the diffusion coefficients of the two studied salts dissolved in diglyme, is different in bulk and under confinement, demonstrating that the interactions of the ions with the charged pore wall probably compete with the cation-anion interactions, affecting salt association under confinement.

Water transport through mesoporous amorphous-carbon dust

The diffusion of water molecules through mesoporous dust of amorphous carbon (a-C) is a key process in the evolution of prestellar, protostellar, and protoplanetary dust, as well as in that of comets. It also plays a role in the formation of planets. Given the absence of data on this process, we experimentally studied the isothermal diffusion of water molecules desorbing from water ice buried at the bottom of a mesoporous layer of aggregated a-C nanoparticles, a material analogous to protostellar and cometary dust. We used infrared spectroscopy to monitor diffusion in low temperature (160 to 170 K) and pressure (6 × 10−5 to 8 × 10−4 Pa) conditions. Fick’s first law of diffusion allowed us to derive diffusivity values on the order of 10−2 cm2 s−1, which we linked to Knudsen diffusion. Water vapor molecular fluxes ranged from 5 × 1012 to 3 × 1014 cm−2 s−1 for thicknesses of the ice-free porous layer ranging from 60 to 1900 nm. Assimilating the layers of nanoparticles to assemblies of spheres, we attributed to this cosmic dust analog of porosity 0.80−0.90 a geometry correction factor, similar to the tortuosity factor of tubular pore systems, between 0.94 and 2.85. Applying the method to ices and refractory particles of other compositions will provides us with other useful data.

(Invited) Optimizing the Binder Distribution in Battery Electrodes with Manufacturing Process Simulations and Machine Learning

The performance of lithium-ion battery cells strongly depends on the microstructure of the electrodes, which is determined by the spatial distribution of the active material, carbon additive, binder, and pores within the electrode volume. The manufacturing process determines this spatial distribution, which, in turn, determines the interfaces between these materials and the pores, thereby impacting the practical properties of the electrodes, such as their electrical conductivity, energy density, and wettability. The binder plays a crucial role in this process, as its spatial distribution controls the degree of percolation between the particles and the adhesion of the electrode with the current collector.Since 2017, we have been developing the ARTISTIC computational platform,1 which constitutes a digital twin of the battery electrode and cell manufacturing process. Supported by a combination of physics-based models and machine learning, this platform has been demonstrated by us for lithium-ion, sodium-ion, and solid-state batteries with electrodes produced by wet processing.2 The platform simulates each step of the manufacturing process, including mixing, coating/drying, calendering, electrolyte infiltration, and resulting cell performance. It allows for predicting 3D-resolved electrode and cell sandwich microstructures with their associated electrochemical performance as a function of the manufacturing parameters.In this talk, I will discuss how the ARTISTIC computational platform can optimize the spatial distribution of active material, carbon additive, and binder within the electrode volume. Using Bayesian Optimization, the platform predicts the manufacturing parameters required to obtain optimal electrodes in terms of several properties, such as electric conductivity, tortuosity factor, electroactive surface area, and energy density. I will particularly discuss the platform's capabilities to capture the adhesion of the electrode with the current collector through the binder. Additionally, I will present an extension of this digital twin for the 3D-resolved simulation of the dry processing of active material, carbon additive, and binder based on extrusion and compare the results (in the form of 3D-resolved electrode microstructures) with experimental data. Results from the wet and dry processing simulations will be discussed for electrodes made with different active material chemistries, such as NMC and LFP, and the crucial role of the binder in the heterogeneity of lithiation/delithiation of the electrodes upon their electrochemical cycling will be explored.Finally, building on our previously reported virtual reality digital tools for battery manufacturing,3 I will present a novel virtual reality interface that allows for the interactive and immersive use of the ARTISTIC computational platform to track the influence of manufacturing parameters on the active material, carbon additive, and binder's spatial location, as well as the resulting electrode properties. I will conclude by discussing why this virtual interface is also a powerful tool to assist in the electrode manufacturing optimization.

NMC811 Electrodes with High Mass Loadings Enabled By Non-Solvent Induced Phase Inversion

Higher energy density Li-ion batteries that can enable longer driving ranges are currently subject to intensive research interest. Increasing the thickness of electrodes and reducing the proportion of inactive components per cell is one way to achieve this. In thick electrodes, a higher electronic and ionic overpotential and mechanical failure (cracking, delamination etc.) induced by binder migration during the drying process leads to sluggish (dis)charge performance or even cell failure respectively. Here we report non-solvent induced phase inversion as a scalable, effective method to arrive at thick NMC811 electrodes.By tuning the non-solvent properties and other processing conditions to be compatible with Ni-rich electrodes, it was possible to obtain NMC811 electrodes with mass loadings up to 11 mAh/cm2 (single sided) which outperform their conventional processed counterparts in Li/NMC half cells. This improvement could be attributed to the altered carbon-binder structure, which improves the pore connectivity and lowers the electrode tortuosity factor. The rapid solvent removal also reduces the long-range binder migration during drying. With imaging and porosimetry techniques we show the structural change which leads to the observed improved electrochemical performance. The method also shows good compatibility with double sided slot-die electrode coating procedure, making this a technique with potentially high industrial feasibility towards making thicker NMC811 and other electrodes.

Empirical and Numerical Modelling of Gas–Gas Diffusion for Binary Hydrogen–Methane Systems at Underground Gas Storage Conditions

The physical process in which a substance moves from a location with a higher concentration to a location with a lower concentration is known as molecular diffusion. It plays a crucial role during the mixing process between different gases in porous media. Due to the petrophysical properties of the porous medium, the diffusion process occurs slower than in bulk, and the overall process is also affected by thermodynamic conditions. The complexity of measuring gas–gas diffusion in porous media at increased pressure and temperature resulted in significant gaps in data availability for modelling this process. Therefore, correlations for ambient conditions and simplified diffusivity models have been used for modelling purposes. In this study, correlations in dependency of petrophysical and thermodynamic properties were developed based on more than 30 measurements of the molecular diffusion of the binary system hydrogen–methane in gas storage rock samples at typical subsurface conditions. It allows reproducing the laboratory observations by evaluating the bulk diffusion coefficient and the tortuosity factor with relative errors of less than 50 % with minor exceptions, leading to a strong improvement compared to existing correlations. The developed correlation was implemented in the open-source simulator DuMux and the implementation was validated by reproducing the measurement results. The validated implementation in DuMux allows to model scenarios such as Underground Hydrogen Storage (UHS) on a field-scale and, as a result, can be used to estimate the temporary loss of hydrogen into the cushion gas and the purity of withdrawn gas due to the gas–gas mixing process.