Search for: All records

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn$$_2$$ ${}_2$O$$_4$$ ${}_4$chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97$$R^2$$ $R^2$score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries. 

During the survey phase of the Kepler mission, several thousand stars were observed in short cadence, allowing for the detection of solar-like oscillations in more than 500 main-sequence and subgiant stars. These detections showed the power of asteroseismology in determining fundamental stellar parameters. However, the Kepler Science Office discovered an issue in the calibration that affected half of the store of short-cadence data, leading to a new data release (DR25) with corrections on the light curves. In this work, we re-analyzed the one-month time series of the Kepler survey phase to search for solar-like oscillations that might have been missed when using the previous data release. We studied the seismic parameters of 99 stars, among which there are 46 targets with new reported solar-like oscillations, increasing, by around 8%, the known sample of solar-like stars with an asteroseismic analysis of the short-cadence data from this mission. The majority of these stars have mid- to high-resolution spectroscopy publicly available with the LAMOST and APOGEE surveys, respectively, as well as precise Gaia parallaxes. We computed the masses and radii using seismic scaling relations and we find that this new sample features massive stars (above 1.2  M ⊙ and up to 2  M ⊙ ) and subgiants. We determined the granulation parameters and amplitude of the modes, which agree with the scaling relations derived for dwarfs and subgiants. The stars studied here are slightly fainter than the previously known sample of main-sequence and subgiants with asteroseismic detections. We also studied the surface rotation and magnetic activity levels of those stars. Our sample of 99 stars has similar levels of activity compared to the previously known sample and is in the same range as the Sun between the minimum and maximum of its activity cycle. We find that for seven stars, a possible blend could be the reason for the non-detection with the early data release. Finally, we compared the radii obtained from the scaling relations with the Gaia ones and we find that the Gaia radii are overestimated by 4.4%, on average, compared to the seismic radii, with a scatter of 12.3% and a decreasing trend according to the evolutionary stage. In addition, for homogeneity purposes, we re-analyzed the DR25 of the main-sequence and subgiant stars with solar-like oscillations that were previously detected and, as a result, we provide the global seismic parameters for a total of 525 stars. 

The microstructural optimization of porous lithium ion battery electrodes has traditionally been driven by experimental trial and error efforts, based on anecdotal understanding and intuition, leading to the development of useful but qualitative rules of thumb to guide the design of porous energy storage technology. In this paper, an advanced data-driven framework is presented wherein the effect of experimentally accessible microstructural parameters such as active particle morphology and spacial arrangement, underlying porosity, cell thickness, etc. , on the corresponding macroscopic power and energy density is systematically assessed. For the Li x C 6 | LMO chemistry, an analysis performed on 53 356 battery architectures reported in the literature revealed that for commercial microstructures based on oblate-shaped particles, lightly textured samples deliver higher power and energy density responses as compared to highly textured samples, which suffer from large polarization losses. In contrast, high aspect ratio prolate-shaped particles deliver the highest energy and power density, particularly in the limit of wire-like morphologies. Polyhedra-based colloidal microstructures demonstrate high area densities, and low tortuosities, but provide no appreciable power and energy density benefit over currently manufactured particle morphologies. The developed framework enables to establish general microstructure design guidelines and propose optimal electrode microstructures based on the intended application, given an anode and cathode chemistry. 

Abstract: Introduced species can have strong ecological, social and economic effects on their non-native environment. Introductions of megafaunal species are rare and may contribute to rewilding efforts, but they may also have pronounced socio-ecological effects because of their scale of influence. A recent introduction of the hippopotamus (Hippopotamus amphibius) into Colombia is a novel introduction of a megaherbivore onto a new continent, and raises questions about the future dynamics of the socio-ecological system into which it has been introduced. Here we synthesize current knowledge about the Colombian hippopotamus population, review the literature on the species to predict potential ecological and socio-economic effects of this introduction, and make recommendations for future study. Hippopotamuses can have high population growth rates (7–11%) and, on the current trajectory, we predict there could be 400–800 individuals in Colombia by 2050. The hippopotamus is an ecosystem engineer that can have profound effects on terrestrial and aquatic environments and could therefore affect the native biodiversity of the Magdalena River basin. Hippopotamuses are also aggressive and may pose a threat to the many inhabitants of the region who rely upon the Magdalena River for their livelihoods, although the species could provide economic benefits through tourism. Further research is needed to quantify the current and future size and distribution of this hippopotamus population and to predict the likely ecological, social and economic effects. This knowledge must be balanced with consideration of social and cultural concerns to develop appropriate management strategies for this novel introduction.