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

The response of human macrophages to 3D printed titanium antibacterial implants does not affect the osteogenic differentiation of hMSCs

Macrophage responses following the implantation of orthopaedic implants are essential for successful implant integration in the body, partly through intimate crosstalk with human marrow stromal cells (hMSCs) in the process of new bone formation. Additive manufacturing (AM) and plasma electrolytic oxidation (PEO) in the presence of silver nanoparticles (AgNPs) are promising techniques to achieve multifunctional titanium implants. Their osteoimmunomodulatory properties are, however, not yet fully investigated. Here, we studied the effects of implants with AgNPs on human macrophages and the crosstalk between hMSCs and human macrophages when co-cultured in vitro with biofunctionalised AM Ti6Al4V implants. A concentration of 0.3 g/L AgNPs in the PEO electrolyte was found to be optimal for both macrophage viability and inhibition of bacteria growth. These specimens also caused a decrease of the macrophage tissue repair related factor C-C Motif Chemokine Ligand 18 (CCL18). Nevertheless, co-cultured hMSCs could osteogenically differentiate without any adverse effects caused by the presence of macrophages that were previously exposed to the PEO (±AgNPs) surfaces. Further evaluation of these promising implants in a bony in vivo environment with and without infection is highly recommended to prove their potential for clinical use.</p

Tunnelling effects in multiferroic tunnel junctions

The demands from electronic devices have always been to be portable, fast, non-volatile, more intelligent and to consume low energy. One way towards this goal is to introduce multifunctionality of materials in devices. Ferromagnetism and ferroelectricity are two order parameters that can be coupled in a limited number of multiferroics and their coexistence implies the control over magnetisation and polarisation with both electric and magnetic fields. Similar properties were observed at ferromagnetic/ferroelectric thin film interfaces and attracted attention, since high quality thin film devices can be easily obtained nowadays through monitoring in real time of their structural and physical properties. This effect was observed also in tunnel junction configurations, devices which are formed from metallic electrodes separated by a very thin insulating barrier. By combining a barrier with various ferroelectric order parameters (ferroelectric, antiferroelectric, ferrielectric) and ferromagnetic electrodes, multi-field controlled multi-state non-volatile memory devices can be obtained. Tunnelling processes, especially in junctions containing d orbital elements are not completely understood and need deeper investigation. In this thesis, multiferroic tunnel junctions with La0:7Sr0:3MnO3/PbTiO3/Co structure are shown to be functional down to 3 unit cells. Moreover, the domain structure is shown to change with thickness, going through complex patterns including toroidal flux closure structures. The fabrication and working principle of the novel antiferroelectric tunnel junctions are reported for the first time using La0:7Sr0:3MnO3/PbZrO3/Co structures. Both investigated systems exhibit a multiferroic interface characterised by a magnetoelectric coupling which can be tailored by switching the ferroelectric polarisation

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

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.

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

Polarization curling and flux closures in multiferroic tunnel junctions

Formation of domain walls in ferroelectrics is not energetically favourable in low-dimensional systems. Instead, vortex-type structures are formed that are driven by depolarization fields occurring in such systems. Consequently, polarization vortices have only been experimentally found in systems in which these fields are deliberately maximized, that is, in films between insulating layers. As such configurations are devoid of screening charges provided by metal electrodes, commonly used in electronic devices, it is wise to investigate if curling polarization structures are innate to ferroelectricity or induced by the absence of electrodes. Here we show that in unpoled Co/PbTiO3/(La,Sr)MnO3 ferroelectric tunnel junctions, the polarization in active PbTiO3 layers 9 unit cells thick forms Kittel-like domains, while at 6 unit cells there is a complex flux-closure curling behaviour resembling an incommensurate phase. Reducing the thickness to 3 unit cells, there is an almost complete loss of switchable polarization associated with an internal gradient

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

Bi-ferroic memristive properties of multiferroic tunnel junctions

The giant tunnelling electroresistance (TER) and memristive behaviours of ferroelectric tunnel junctions make them promising candidates for future information storage technology. Using conducting ferromagnetic layers as electrodes results in multiferroic tunnel junctions (MFTJs) which show spin dependent transport. The tunnelling magnetoresistance (TMR) of such structures can be reversibly controlled by electric pulsing owing to ferroelectric polarisation-dependent spin polarisation at the ferroelectric/ferromagnetic interface. Here, we show multilevel electric control of both TMR and TER of MFTJs, which indicates the bi-ferroic or magneto-electric memristive properties. This effect is realised by manipulating the ferroelectric domain configuration via non-volatile partial ferroelectric switching obtained by applying low voltage pulses to the junction. Through electrically modulating the ratio between up- and down-polarised ferroelectric domains, a broad range of TMR (between ∼3% and ∼30%) and TER (∼1000%) values can be achieved. The multilevel control of TMR and TER using the electric pulse tunable ferroelectric domain configuration suggests a viable way to obtain multiple state memory

4D printed shape-shifting biomaterials for tissue engineering and regenerative medicine applications

The existing 3D printing methods exhibit certain fabrication-dependent limitations for printing curved constructs that are relevant for many tissues. Four-dimensional (4D) printing is an emerging technology that is expected to revolutionize the field of tissue engineering and regenerative medicine (TERM). 4D printing is based on 3D printing, featuring the introduction of time as the fourth dimension, in which there is a transition from a 3D printed scaffold to a new, distinct, and stable state, upon the application of one or more stimuli. Here, we present an overview of the current developments of the 4D printing technology for TERM, with a focus on approaches to achieve temporal changes of the shape of the printed constructs that would enable biofabrication of highly complex structures. To this aim, the printing methods, types of stimuli, shape-shifting mechanisms, and cell-incorporation strategies are critically reviewed. Furthermore, the challenges of this very recent biofabrication technology as well as the future research directions are discussed. Our findings show that the most common printing methods so far are stereolithography (SLA) and extrusion bioprinting, followed by fused deposition modelling, while the shape-shifting mechanisms used for TERM applications are shape-memory and differential swelling for 4D printing and 4D bioprinting, respectively. For shape-memory mechanism, there is a high prevalence of synthetic materials, such as polylactic acid (PLA), poly(glycerol dodecanoate) acrylate (PGDA), or polyurethanes. On the other hand, different acrylate combinations of alginate, hyaluronan, or gelatin have been used for differential swelling-based 4D transformations. TERM applications include bone, vascular, and cardiac tissues as the main target of the 4D (bio)printing technology. The field has great potential for further development by considering the combination of multiple stimuli, the use of a wider range of 4D techniques, and the implementation of computational-assisted strategies.</p