In this work, a novel heterofunctional, bimodally-porous carbon morphology, termed the carbon compartment (CC), is utilized as a sulfur host within a lithium-sulfur battery cathode. A multi-scale model explores the physics and chemistry of the lithium-sulfur battery cathode. The CCs are synthesized through a rapid, low cost process to improve electrode-electrolyte interfacial contact and accommodate volumetric expansion associated with sulfide formation. The CCs demonstrate controllable sulfur loading and ca. 700 mAh g⁻¹ (at 47%-wt S) reversible capacity with high coulombic efficiency due to their unique structures. Density functional theory and ab initio molecular dynamics characterize the interface between the C/S composite and electrolyte during the sulfur reduction mechanism. Stochastic realizations of 3D electrode microstructures are reconstructed based on representative SEM micrographs to study the influence of solid sulfur loading and lithium sulfide precipitation on microstructural and electrochemical properties. A macroscale electrochemical performance model is developed to analyze the performance of lithium-sulfur batteries. The combined multi-scale simulation studies explain key fundamentals of sulfur reduction and its relation to the polysulfide shuttle mechanism: how the process is affected due to the presence of carbon substrate, thermodynamics of lithium sulfide formation and deposition on carbon, and microstructural effects on the overall cell performance.

One of the challenges of modern biotechnology is to find new routes to mitigate the resistance to conventional antibiotics. Antimicrobial peptides (AMPs) are an alternative type of biomolecules, naturally present in a wide variety of organisms, with the capacity to overcome the current microorganism resistance threat. Here, we reviewed our recent efforts to develop a new library of non-rationally produced AMPs that relies on bacterial genome inherent diversity and compared it with rationally designed libraries. Our approach is based on a four-stage workflow process that incorporates the interplay of recent developments in four major emerging technologies: artificial intelligence, molecular dynamics, surface-display in microorganisms, and microfluidics. Implementing this framework is challenging because to obtain reliable results, the in silico algorithms to search for candidate AMPs need to overcome issues of the state-of-the-art approaches that limit the possibilities for multi-space data distribution analyses in extremely large databases. We expect to tackle this challenge by using a recently developed classification algorithm based on deep learning models that rely on convolutional layers and gated recurrent units. This will be complemented by carefully tailored molecular dynamics simulations to elucidate specific interactions with lipid bilayers. Candidate AMPs will be recombinantly-expressed on the surface of microorganisms for further screening via different droplet-based microfluidic-based strategies to identify AMPs with the desired lytic abilities. We believe that the proposed approach opens opportunities for searching and screening bioactive peptides for other applications.