Calendering of Li(Ni0.33Mn0.33Co0.33)O2‐based cathodes: analyzing the link between process parameters and electrode properties by advanced statistics

International audienceThe optimization of the calendering process represents one of the key tasks for tuning the lithium‐ion battery performance. In this study we present a systematic statistical‐based study of the three main calendering parameters (namely, the applied pressure, roll temperature and line speed) effect on the porosity, electrode mechanical properties and electronic conductivity. Our work main goal is to understand how by changing the calendering parameters, the electrode properties can be tuned and up to which degree they determine the electrode capacity of Li(Ni0.33Mn0.33Co0.33)O2‐based cathodes. The statistical tools used for the analysis were the analysis of the covariance (ANCOVA), the principal components analysis (PCA) and the unsupervised machine learning k‐means clustering algorithm. Our results showed that while porosity and the mechanical properties depend mainly on the applied pressure, the electrode’s conductivity correlates mainly with the temperature. All of them were found to influence the cathode’s capacity (at a rate equal to C), being the best condition applied pressures between 60 and 120 MPa and roll temperatures between 60 and 75 °C

Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies

International audienceThe number of parameters involved in lithium-ion battery electrode manufacturing and the complexity of the physicochemical interactions throughout the associated processes make highly complex to find interdependencies between the final electrode characteristics and the fabrication parameters. In this work, we have analyzed three different machine-learning algorithms (decision tree, support vector machine, and deep neural network) in order to find the best one to uncover the interdependencies between the slurry manufacturing parameters and the final properties of NMC-based cathodes. The results revealed that the support vector machine method shows high accuracy and the possibility to predict the influence of manufacturing parameters on themass loading and porosity of the electrodes in a straightforward graphical way. Furthermore, we report for the first time this new approach and a case study that, by comparing the trends observed experimentally and from the model, demonstrates the validity and the quality of the proposed approach

Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning

International audienceBoth society and market calls for safer, high-performing and cheap Li-ion batteries (LIBs) in order to speed up the transition from oil-based to electric-based economy. One critical aspect to be taken into account in this modern challenge is LIBs manufacturing process, whose optimization is time and resources consuming due to the several interdependent physicochemical mechanisms involved. In order to tackle rapidly this challenge, digital tools able to optimize LIBs manufacturing parameters are crucially needed for both well-known and recently discovered chemistries. The methodology presented here encompasses experimental characterizations, in silico generation of electrode mesostructures and machine learning algorithms to track the effect of the calendering process over a wide array of mesoscale electrode properties critically linked to the electrochemical performance. Particularly, features as the interconnectivity of the particles network, the electrolyte tortuosity and effective ionic conductivity, the percentage of current collector surface covered by either active material or carbon-binder domain particles and the active material surface in contact with electrolyte were analysed and discussed in detail. This approach was tested and validated for the case of LiNi1/3Mn1/3Co1/3O2-based cathodes calendering, proving its capability to ease the process parameters-electrode properties interdependencies analysis, paving the way to deeper understanding of LIBs manufacturing

Immobilization of graphene-derived materials at gold surfaces: Towards a rational design of protein-based platforms for electrochemical and plasmonic applications

This work is focused on the critical analysis of the non-covalent modification of a thiolated-gold surface with different grapheneous materials and the covalent attachment of bovine serum albumin (BSA) as a model protein. The main goal was to find a relationship between the nature and amount of the grapheneous nanomaterial, the amount of immobilized protein, and the electrochemical and plasmonic properties of the resulting platforms. The characterization of the grapheneous nanomaterials (graphene oxide (GO), GO modified with chitosan (CHIT), (GO-CHIT), and chemically reduced GO-CHIT (RGO-CHIT)) was performed by using FTIR, Raman, TGA, Dynamic light scattering (DLS), UV-Vis spectroscopy and zeta-potential measurements. The characterization of the thiolated-gold surfaces modified with the different nanomaterials and BSA was performed using surface plasmon resonance (SPR), cyclic voltammetry, electrochemical impedance spectroscopy (EIS) and scanning electrochemical microscopy (SECM). The pH of the grapheneous materials dispersions demonstrated to be a critical parameter to control the assembly of the nanomaterials and the model protein at the gold surfaces and, consequently, the electroactivity and plasmonics of the resulting platforms. When using GO, the optimum pH is 8.00 while in the case of GO-CHIT and RGO-CHIT, pHs << pKa,(CHIT) are the most adequate. We demonstrated that in the case of our model system, if the detection method depends on the direct quantification of the amount of BSA immobilized at the platform (like SPR), the use of GO is the best option; while if the detection mode depends on the changes in the electrochemical response of a redox marker (like EIS), the selected grapheneous material should be RGO. (c) 2017 Elsevier Ltd. All rights reserved.CONICET SECyT-UNC ANPCyT (Argentina) National Fund for Scientific and Technological Development-CHILE FONDECYT 1161225 National Fund for Scientific and Technological Development-CHILE FONDAP 15130011 Argentina-Chile International Collaboration Grant CONICYT/MINCYT CH/13/03//PCCI13005

Investigating electrode calendering and its impact on electrochemical performance by means of a new discrete element method model: Towards a digital twin of Li-Ion battery manufacturing

International audienceLithium-ion battery (LIB) manufacturing optimization is crucial to reduce its CO2 fingerprint and cost, while improving their electrochemical performance. In this article, we present an experimentally validated calendering Discrete Element Method model for LiNi0.33Mn0.33Co0.33O2-based cathodes by considering explicitly both active material (AM) an

4D-resolved physical model for Electrochemical Impedance Spectroscopy of Li(Ni1-x-yMnxCoy)O2-based cathodes in symmetric cells: Consequences in tortuosity calculations

International audienc