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

Trends in New South Wales infant hospital admission rates in the first year of life: population-based study

Objective: To examine the trends in hospital admissions in the first year of life and identify whether changes in maternal and infant risk factors explain any changes Design: Population-based study using de-identified linked health data. Participants: All 788,798 liveborn infants delivered in New South Wales from 2001 to 2009 with a linked birth and hospital record. Main outcome measures: The number of infants readmitted to hospital at least once, up to one year of age, per 100 livebirths each year; changes in maternal and infant risk factors were assessed using logistic regression. Results: The number of infants admitted to hospital up to age one decreased 10.5%, from 18.4 per 100 births in 2001 to 16.5 in 2009. Fifty five per cent of this decrease could be explained by changes in factors that are associated with likelihood of hospitalisation; length of stay during the birth admission, maternal age and maternal smoking. The rate of admissions for jaundice and feeding difficulties increased significantly over the study period, while admissions for infections decreased. Conclusions: There has been a decrease in the rate of infants admitted to hospital in the first year of life, which can be partly explained by increasing maternal age, decreasing maternal smoking and a shift to shorter length of hospital stay at birth. Improved maternal and neonatal care in hospital and increased postnatal support at home may have contributed to reduced risk of readmission. The introduction of government policies may explain the rest of the decrease

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

Swelling properties of alkali-metal doped polymeric anion-exchange membranes in alcohol media for application in fuel cells

Swelling properties of four commercial anion-exchange membranes with different structure have been analyzed in several hydro-organic media. With this target, the liquid uptake and the surface expansion of the membranes in contact with different pure liquids, water and alcohols (methanol, ethanol and 1-propanol), and with water alcohol mixtures with different concentrations have been experimentally determined in presence and in absence of an alkaline medium (LiOH, NaOH and KOH of different concentrations). The alkali-metal doping effect on the membrane water uptake has also been investigated, analyzing the influence of the hydroxide concentration and the presence of an alcohol in the doping solution. The results show that the membrane structure plays an essential role in the influence that alcohol nature and alkaline media has on the selective properties of the membrane. The heterogeneous membranes, with lower density, show higher liquid uptakes and dimensional changes than the homogeneous membranes, regardless of the doping conditions. (C) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved