Cross-sectional analysis of lithium ion electrodes using spatial autocorrelation techniques.

Join counting, a standard technique in spatial autocorrelation analysis, has been used to quantify the clustering of carbon, fluorine and sodium in cross-sectioned anode and cathode samples. The sample preparation and EDS mapping steps are sufficiently fast for every coating from two Design of Experiment (DoE) test matrices to be characterised. The results show two types of heterogeneity in material distribution; gradients across the coating from the current collector to the surface, and clustering. In the cathode samples, the carbon is more clustered than the fluorine, implying that the conductive carbon component is less well distributed than the binder. The results are correlated with input parameters systematically varied in the DoE coating blade gap, coating speed, and other output parameters coat weight, and electrochemical resistance

Experimental data of cathodes manufactured in a convective dryer at the pilot-plant scale, and charge and discharge capacities of half-coin lithium-ion cells.

Megtec Systems pilot-plant scale continuous convective coater. The data was generated as part of an experimental design involving the following coating-drying process variables and ranges: comma bar gap, 80-140 µm; web speed, 0.5-1.5 m/min; coating ratio, 110-150%; drying temperature, 85-110 °C and drying air speed, 5-15 m/s. The manufacturing data include pre-calendered coating thickness, mass loading dry and wet, pre-calendered porosity, spatial autocorrelation and join counting (SAJC) -score for carbon and for fluorine, cell thickness, coating weight and porosity of 15 different electrode coatings and 45 half-coin cells. The electrochemical data was obtained at 25 °C in a Maccor 4000 series battery cycler and consists of charge and discharge capacities at C/20, C/5, C/2, 1C, 2C, 5C and 10C C-rates. Discharge gravimetric and volumetric capacities, rate performance (at 5C:0.2C) and first cycle loss data is also reported. Details of the experimental design and a comprehensive analysis of the data can be found in the co-submitted manuscript (Román-Ramírez et al., 2021). Additional collected data not used in Román-Ramírez et al. (2021) is reported in the present manuscript and include visual observations of coating defects, rheological properties of the electrode slurries (solid content, viscosity, coating shear rate and viscosity at coating shear rate), room temperature and room humidity during the coatings and first cycle loss of the coin cells. Raw and analyzed data is made available. The reported data can be used to extend the analysis reported in Román-Ramírez et al. (2021), and for the comparison of relevant data obtained at different manufacturing scales. [Abstract copyright: © 2021 The Author(s). Published by Elsevier Inc.

Understanding the effect of coating-drying operating variables on electrode physical and electrochemical properties of lithium-ion batteries

The effect of coating and drying process variables (comma bar gap, web speed, coating ratio, drying temperature and drying air speed) on NMC622 cathode physical properties (thickness, mass loading and porosity) and electrochemical properties (gravimetric capacity, volumetric capacity and rate performance) is studied by a design of experiments approach. Electrochemical performance is assessed on half coin cells at C-rates from C/20 up to 10C. The statistical analysis of the data reveals that the cathode physical properties are mainly affected by comma bar gap and coating ratio. The electrochemical properties also show high correlations between comma bar gap and coating ratio for some C-rates. As a second evaluation, the relationship between the cathode half-cell physical characteristics with the electrochemical performance is studied through multiple linear regression analysis. A correlation mainly between coating weight and the electrochemical properties is found. Empirical linear models representing the relationship between the output and input variables are provided, showing correlation coefficients ( ) as high as 0.99

Machine learning for optimised and clean Li-ion battery manufacturing: Revealing the dependency between electrode and cell characteristics

The large number of parameters involved in each step of Li-ion electrode manufacturing process as well as the complex electrochemical interactions in those affect the properties of the final product. Optimization of the manufacturing process, although very challenging, is critical for reducing the production time, cost, and carbon footprint. Data-driven models offer a solution for manufacturing optimization problems and underpin future aspirations for manufacturing volumes. This study combines machine-learning approaches with the experimental data to build data-driven models for predicting final battery performance. The models capture the interdependencies between the key parameters of electrode manufacturing, its structural features, and the electrical performance characteristics of the associated Li-ion cells. The methodology here is based on a set of designed experiments conducted in a controlled environment, altering electrode coating control parameters of comma bar gap, line speed and coating ratio, obtaining the electrode structural properties of active material mass loading, thickness, and porosity, extracting the manufactured half-cell characteristics at various cycling conditions, and finally building models for interconnectivity studies and predictions. Investigating and quantifying performance predictability through a systems' view of the manufacturing process is the main novelty of this paper. Comparisons between different machine-learning models, analysis of models’ performance with a limited number of inputs, analysis of robustness to measurement noise and data-size are other contributions of this study. The results suggest that, given manufacturing parameters, the coated electrode properties and cell characteristics can be predicted with about 5% and 3% errors respectively. The presented concepts are believed to link the manufacturing at lab-scale to the pilot-line scale and support smart, optimised, and clean production of electrodes for high-quality Li-ion batteries

Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence

Li-ion battery is one of the key players in energy storage technology empowering electrified and clean transportation systems. However, it is still associated with high costs due to the expensive material as well as high fluctuations of the manufacturing process. Complicated production processes involving mechanical, chemical, and electrical operations makes the predictability of the manufacturing process a challenge, hence the process is optimised through trial and error rather systematic simulation. To establish an in-depth understanding of the interconnected processes and manufacturing parameters, this paper combines data-mining techniques and real production to offer a method for the systematic analysis, understanding and improving the Li-ion battery electrode manufacturing chain. The novelty of this research is that unlike most of the existing research that are focused on cathode manufacturing only, it covers both of the cathode and anode case studies. Furthermore, it is based on real manufacturing data, proposes a systematic design of experiment method for generating high quality and representative data, and leverages the artificial intelligence techniques to identify the dependencies in between the manufacturing parameters and the key quality factors of the electrode. Through this study, machine learning models are developed to quantify the predictability of electrode and cell properties given the coating process control parameters. Moreover, the manufacturing parameters are ranked and their contribution to the electrode and cell characteristics are quantified by models. The systematic data acquisition approach as well as the quantified interdependencies are expected to assist the manufacturer when moving towards an improved battery production chain

Measurement of anisotropic volumetric resistivity in lithium ion electrodes

Measurements of the electronic conductivity of lithium ion coatings are an important part of electrode development, particularly for thicker electrodes and in high power applications. A resistance measurement system with 46 probes has been used to characterise lithium ion electrodes, with different formulations and coat weights. The results show that the total through plane resistance is dominated by the interface resistance between the coating and the metal foil, rather than the volumetric resistivity of the coating. For coatings containing carbon nano-tubes, the in plane resistivities in the coating and perpendicular directions are different. A finite volume model was developed to help analyse and interpret the resistivity data

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Low availability of carnitine precursors as a possible reason for the diminished plasma carnitine concentrations in pregnant women

<p>Abstract</p> <p>Background</p> <p>It has been shown that plasma carnitine concentrations decrease markedly during gestation. A recent study performed with a low number of subjects suggested that this effect could be due to a low iron status which leads to an impairment of carnitine synthesis. The present study aimed to confirm this finding in a greater number of subjects. It was moreover intended to find out whether low carnitine concentrations during pregnancy could be due to a reduced availability of precursors of carnitine synthesis, namely trimethyllysine (TML) and γ-butyrobetaine (BB).</p> <p>Methods</p> <p>Blood samples of 79 healthy pregnant women collected at delivery were used for this study.</p> <p>Results</p> <p>There was only a weak, non-significant (P > 0.05), correlation between plasma concentration of ferritin and those of free and total carnitine. There was no correlation between other parameters of iron status (plasma iron concentration, hemoglobin, MCV, MCH) and plasma concentration of free and total carnitine. There were, however, significant (P < 0.05) positive correlations between concentrations of TML and BB and those of free and total carnitine in plasma.</p> <p>Conclusions</p> <p>The results of this study suggest that an insufficient iron status is not the reason for low plasma carnitine concentrations observed in pregnant women. It is rather indicated that low plasma carnitine concentrations are caused by a low availability of precursors for carnitine synthesis during gestation.</p

Structure and inhibitory effects on angiogenesis and tumor development of a new vascular endothelial growth inhibitor

Blocking angiogenesis is an attractive strategy to inhibit tumor growth, invasion, and metastasis. We describe here the structure and the biological action of a new cyclic peptide derived from vascular endothelial growth factor (VEGF). This 17-amino acid molecule designated cyclopeptidic vascular endothelial growth inhibitor (cyclo-VEGI, CBO-P11) encompasses residues 79-93 of VEGF which are involved in the interaction with VEGF receptor-2. In aqueous solution, cyclo-VEGI presents a propensity to adopt a helix conformation that was largely unexpected because only \u3b2-sheet structures or random coil conformations have been observed for macrocyclic peptides. Cyclo-VEGI inhibits binding of iodinated VEGF165 to endothelial cells, endothelial cells proliferation, migration, and signaling induced by VEGF165. This peptide also exhibits anti-angiogenic activity in vivo on the differentiated chicken chorioallantoic membrane. Furthermore, cyclo-VEGI significantly blocks the growth of established intracranial glioma in nude and syngeneic mice and improves survival without side effects. Taken together, these results suggest that cyclo-VEGI is an attractive candidate for the development of novel angiogenesis inhibitor molecules useful for the treatment of cancer and other angiogenesis-related diseases