RESUMEN
Catalytic additives able to accelerate the lithium-sulfur redox reaction are a key component of sulfur cathodes in lithium-sulfur batteries (LSBs). Their design focuses on optimizing the charge distribution within the energy spectra, which involves refinement of the distribution and occupancy of the electronic density of states. Herein, beyond charge distribution, we explore the role of the electronic spin configuration on the polysulfide adsorption properties and catalytic activity of the additive. We showcase the importance of this electronic parameter by generating spin polarization through a defect engineering approach based on the introduction of Co vacancies on the surface of CoSe nanosheets. We show vacancies change the electron spin state distribution, increasing the number of unpaired electrons with aligned spins. This local electronic rearrangement enhances the polysulfide adsorption, reducing the activation energy of the Li-S redox reactions. As a result, more uniform nucleation and growth of Li2S and an accelerated liquid-solid conversion in LSB cathodes are obtained. These translate into LSB cathodes exhibiting capacities up to 1089 mA h g-1 at 1 C with 0.017% average capacity loss after 1500 cycles, and up to 5.2 mA h cm-2, with 0.16% decay per cycle after 200 cycles in high sulfur loading cells.
RESUMEN
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.