The role of grain boundaries in solid-state Li-metal batteries

Despite the potential advantages promised by solid-state batteries, the success of solid-state electrolytes has not yet been fully realised. This is due in part to the lower ionic conductivity of solid electrolytes. In many solid superionic conductors, grain boundaries are found to be ionically resistive and hence contribute to this lower ionic conductivity. Additionally, in spite of the hope that solid electrolytes would inhibit lithium filaments, in most scenarios their growth is still observed, and in some polycrystalline systems this is suggested to occur along grain boundaries. It is apparent that grain boundaries affect the performance of solid-state electrolytes, however a deeper understanding is lacking. In this perspective, the current theories relating to grain boundaries in solid-state electrolytes are explored, as well as addressing some of the challenges which arise when trying to investigate their role. Glasses are presented as a possible solution to reduce the effect of grain boundaries in electrolytes. Future research directions are suggested which will aid in both understanding the role of grain boundaries, and diminishing their contribution in cases where they are detrimental

Solid electrolyte interphases in lithium metal batteries

Lithium metal batteries (LMBs) have recently received enormous interest as a higher energy density alternative to conventional lithium-ion batteries (LIBs). However, the commercialization of LMBs is currently impeded by poor cycle life due to inhomogeneous lithium deposition and active lithium loss. These are controlled by the solid electrolyte interphase (SEI) that forms on the anode surface, and there have been numerous reported strategies to produce SEIs with desired properties. However, these have not been sufficient to achieve the high cycling stabilities necessary for widespread LMB commercialization, requiring additional understanding of the SEI. In this perspective, we highlight recent progress in characterizing the SEI that forms in LMBs and outline the need to consider SEI nanostructure, transport, and mechanical properties together. We conclude by prescribing several key research fronts necessary for an accurate, systematic study of the SEI that will guide future electrolyte design and enable the development of safe and stable LMBs

Improving damage resistance of solid-state battery cathodes with block copolymers: a non-linear diffusion-mechanics study at the microscale

Minimizing interfacial failure in the composite cathode remains a crucial challenge to unravel the full potential of all solid-state batteries (ASSBs). Polymer-based ASSBs offer promising means of minimizing those damage effects due to their high ductility. However, multicomponent polymers such as block copolymers (BCPs) are needed to meet requirements for both ionic conductivity and mechanical resistance. This study aims to provide a new insight into the combined effects of block copolymer composition (soft-to-hard phase ratio) and interfacial strength on the coupled diffusion-mechanics response of an ASSB cathode, achieved by proposing a non-linear computational micromechanics approach. The approach combines a pressure-dependent diffusion process, interfacial gap-dependent diffusivity, and advanced elasto-viscoplastic constitutive model for a BCP, and it is implemented numerically within a non-linear finite element framework. Two cathode design concepts are explored here, with and without the BCP coating. Results from these case studies suggest that there is a strong interplay between the interface strength (between active particles and the BCP matrix), the BCP material composition, and the interfacial diffusivity. It is found that interfacial damage can be minimized by increasing both the interfacial strength and the amount of the soft component in the BCP system. If the diffusivity across the interface is damage-dependent, the latter is reduced when the BCP is predominantly made of the hard phase. Ultimately, a simple sensitivity analysis reveals that interfacial strength plays a vital role in minimizing interfacial damage, while the coating thickness is the least influential design parameter

Energy-maximising model predictive control for a multi degree-of-freedom pendulum-based wave energy system

Renewable energy sources can be a solution for the recent pollution increasing scenario and the need for diversification of the energy market. Among such alternative sources,wave energy represents a viable solution, due to the its high power density and accessibility.Nonetheless, wave energy is still in phase of development, and a key stepping stone towards commercialisation is strongly linked to the availability of optimal control strategies for maximum energy harvesting. With its ability to handle system constraints and optimise power absorption directly, model predictive control (MPC) has gained popularity within the WEC community as a potential solution for the corresponding energy-maximising problem. In this study, an MPC strategy is developed for real-time control of the so-called PeWEC energy harvesting system,providing also a solution for the wave excitation estimation and forecasting problem, inherently required by the MPC controller to achieve optimal performance. Improved computational requirements are obtained via definition of a reduced control-oriented model, describing the dynamics of the system in a compact form. The performance of the proposed strategy is illustrated via a comprehensive numerical appraisal

Temperature dependence of thermal conductivity in 1D nonlinear lattices

We examine the temperature dependence of thermal conductivity of one dimensional nonlinear (anharmonic) lattices with and without on-site potential. It is found from computer simulation that the heat conductivity depends on temperature via the strength of nonlinearity. Based on this correlation, we make a conjecture in the effective phonon theory that the mean-free-path of the effective phonon is inversely proportional to the strength of nonlinearity. We demonstrate analytically and numerically that the temperature behavior of the heat conductivity $\kappa\propto1/T$ is not universal for 1D harmonic lattices with a small nonlinear perturbation. The computer simulations of temperature dependence of heat conductivity in general 1D nonlinear lattices are in good agreements with our theoretic predictions. Possible experimental test is discussed.Comment: 6 pages and 2 figures. Accepted for publication in Europhys. Let

Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms

Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., ±1.5 standard deviation, σ). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta

Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricular assist device implant: A machine learning approach

Background: Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms. Methods: Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute–right ventricular failure (N = 8, 11%) or chronic–right ventricular failure (N = 10, 14%). Logistic regression, penalized logistic regression, linear support vector machines, and naïve Bayes algorithms with leave-one-out validation were used to evaluate the efficiency of any combination of three collected variables in an “all-subsets” approach. Results: Michigan risk score combined with central venous pressure assessed invasively and apical longitudinal systolic strain of the right ventricular–free wall were the most significant predictors of acute–right ventricular failure (maximum receiver operating characteristic–area under the curve = 0.95, 95% confidence interval = 0.91–1.00, by the naïve Bayes), while the right ventricular–free wall systolic strain of the middle segment, right atrial strain (QRS-synced), and tricuspid annular plane systolic excursion were the most significant predictors of Chronic-RVF (receiver operating characteristic–area under the curve = 0.97, 95% confidence interval = 0.91–1.00, according to naïve Bayes). Conclusion: Apical right ventricular strain as well as right atrial strain provides complementary information, both critical to predict acute–right ventricular failure and chronic–right ventricular failure, respectively

Transcatheter heart valve implantation in bicuspid patients with self-expanding device

Bicuspid aortic valve (BAV) patients are conventionally not treated by transcathether aortic valve implantation (TAVI) because of anatomic constraint with unfavorable outcome. Patient-specific numerical simulation of TAVI in BAV may predict important clinical insights to assess the con-formability of the transcathether heart valves (THV) implanted on the aortic root of members of this challenging patient population. We aimed to develop a computational approach and virtually simulate TAVI in a group of n.6 stenotic BAV patients using the self-expanding Evolut Pro THV. Specif-ically, the structural mechanics were evaluated by a finite-element model to estimate the deformed THV configuration in the oval bicuspid anatomy. Then, a fluid–solid interaction analysis based on the smoothed-particle hydrodynamics (SPH) technique was adopted to quantify the blood-flow patterns as well as the regions at high risk of paravalvular leakage (PVL). Simulations demonstrated a slight asymmetric and elliptical expansion of the THV stent frame in the BAV anatomy. The contact pressure between the luminal aortic root surface and the THV stent frame was determined to quantify the device anchoring force at the level of the aortic annulus and mid-ascending aorta. At late diastole, PVL was found in the gap between the aortic wall and THV stent frame. Though the modeling framework was not validated by clinical data, this study could be considered a further step towards the use of numerical simulations for the assessment of TAVI in BAV, aiming at understanding patients not suitable for device implantation on an anatomic basis