Who pollutes in Scotland? A prelude to an analysis of sustainability policies in a devolved context

The notion of sustainable development has begun to figure prominently in the regional, as well as the national, policy concerns of many industrialized countries. Indicators have typically been used to monitor changes in economic, environmental and social variables to show whether economic development is on a sustainable path. This paper focuses on pollution in Scotland and analyses the sustainability policies in a devolved political context

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

Polynomial scaling approximations and dynamic correlation corrections to doubly occupied configuration interaction wave functions

A class of polynomial scaling methods that approximate Doubly Occupied Configuration Interaction (DOCI) wave functions and improve the description of dynamic correlation is introduced. The accuracy of the resulting wave functions is analysed by comparing energies and studying the overlap between the newly developed methods and full configuration interaction wave functions, showing that a low energy does not necessarily entail a good approximation of the exact wave function. Due to the dependence of DOCI wave functions on the single-particle basis chosen, several orbital optimisation algorithms are introduced. An energy-based algorithm using the simulated annealing method is used as a benchmark. As a computationally more affordable alternative, a seniority number minimising algorithm is developed and compared to the energy based one revealing that the seniority minimising orbital set performs well. Given a well-chosen orbital basis, it is shown that the newly developed DOCI based wave functions are especially suitable for the computationally efficient description of static correlation and to lesser extent dynamic correlation.Fil: Van Raemdonck, Mario. Ghent University; BélgicaFil: Alcoba, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Poelmans, Ward. Ghent University; BélgicaFil: De Baerdemacker, Stijn. Ghent University; BélgicaFil: Torre, Alicia. Universidad del País Vasco; EspañaFil: Lain, Luis. Universidad del País Vasco; EspañaFil: Massaccesi, Gustavo Ernesto. Universidad de Barcelona. Facultad de Física. Departamento de Física Fomental; EspañaFil: Van Neck, D.. Ghent University; BélgicaFil: Bultinck, P.. Ghent University; Bélgic

Natural and laboratory mutations in kuzbanian are associated with zinc stress phenotypes in Drosophila melanogaster

Organisms must cope with altered environmental conditions such as high concentrations of heavy metals. Stress response to heavy metals is mediated by the metal-responsive transcription factor 1 (MTF-1), which is conserved from Drosophila to humans. MTF-1 binds to metal response elements (MREs) and changes the expression of target genes. kuzbanian (kuz), a metalloendopeptidase that activates the evolutionary conserved Notch signaling pathway, has been identified as an MTF-1 target gene. We have previously identified a putatively adaptive transposable element in the Drosophila melanogaster genome, named FBti0019170, inserted in a kuz intron. In this work, we investigated whether a laboratory mutant stock overexpressing kuz is associated with zinc stress phenotypes. We found that both embryos and adult flies overexpressing kuz are more tolerant to zinc compared with wild-type flies. On the other hand, we found that the effect of FBti0019170 on zinc stress tolerance depends on developmental stage and genetic background. Moreover, in the majority of the genetic backgrounds analyzed, FBti0019170 has a deleterious effect in unpolluted environments in pre-adult stages. These results highlight the complexity of natural mutations and suggest that besides laboratory mutations, natural mutations should be studied in order to accurately characterize gene function and evolution.H.L.M. was a VAST-CSIC fellow, L.G. was a FI/DGR fellow (2012FI-B-00676) and J.G. is a Ramón y Cajal fellow (RYC-2010-07306). This work was supported by grants from the European Community’s Seven Framework Programme (FP7-PEOPLE-2011-CIG-293860), from the Spanish Government (BFU2011-24397 and BFU2014-57779-P), and from the Generalitat de Catalunya (2014 SGR 201).EUR 1,305 APC fee funded by the EC FP7 Post-Grant Open Access PilotPeer reviewe