It is now well accepted that the gut microbiota contributes to our health. However, what determines the microbiota composition is still unclear. Whereas it might be expected that the intestinal niche would be dominant in shaping the microbiota, studies in vertebrates have repeatedly demonstrated dominant effects of external factors such as host diet and environmental microbial diversity. Hypothesizing that genetic variation may interfere with discerning contributions of host factors, we turned to Caenorhabditis elegans as a new model, offering the ability to work with genetically homogenous populations. Deep sequencing of 16S rDNA was used to characterize the (previously unknown) worm gut microbiota as assembled from diverse produce-enriched soil environments under laboratory conditions. Comparisons of worm microbiotas with those in their soil environment revealed that worm microbiotas resembled each other even when assembled from different microbial environments, and enabled defining a shared core gut microbiota. Community analyses indicated that species assortment in the worm gut was non-random and that assembly rules differed from those in their soil habitat, pointing at the importance of competitive interactions between gut-residing taxa. The data presented fills a gap in C. elegans biology. Furthermore, our results demonstrate a dominant contribution of the host niche in shaping the gut microbiota.

This article introduces a lumped electrochemical model for lithium-ion batteries. The governing equations of the standard ‘pseudo 2-dimensional’ (p2D) model are volume-averaged over each region in a cathode-separator-anode representation. This gives a set of equations in which the evolution of each averaged variable is expressed as an overall balance containing internal source terms and interfacial fluxes. These quantities are approximated to ensure mass and charge conservation. The averaged porous domains may thus be regarded as three ‘tanks-in-series’. Predictions from the resulting equation system are compared against the p2D model and simpler Single Particle Model (SPM). The Tanks-in-Series model achieves substantial agreement with the p2D model for cell voltage, with error metrics of <15 mV even at rates beyond the predictive capability of SPM. Predictions of electrochemical variables are examined to study the effect of approximations on cell-level predictions. The Tanks-in-Series model is a substantially smaller equation system, enabling solution times of a few milliseconds and indicating potential for deployment in real-time applications. The methodology discussed herein is generalizable to any model based on conservation laws, enabling the generation of reduced-order models for different battery types. This can potentially facilitate Battery Management Systems for various current and next-generation batteries.

This article applies and efficiently implements the Tanks-in-Series methodology (J. Electrochem. Soc., 167, 013534 (2020)) to generate a computationally efficient electrochemical model for Lithium-Sulfur batteries. The original Tank model approach for Lithium-ion batteries is modified to account for porosity changes with time. In addition, an exponential scaling method is introduced that enables efficient simulation of the model equations to address the wide range of time constants present for different reactions in the Lithium-Sulfur system. The Tank Model achieves acceptable voltage error even for transport-limited discharged conditions. Predictions of internal electrochemical variables are examined, and electrochemical implications of the approximations discussed. This suggests significant potential for real-time applications such as optimal charging, cell-balancing, and estimation, and represents a step forward in efforts to incorporate detailed electrochemical models in advanced Battery Management Systems for Lithium-Sulfur batteries.

Experimental insights into lithium-sulfur (LiS) battery chemistry have resulted in practical improvements in cell coulombic efficiency, sulfur utilization, and cycle life. However, optimization of this complex battery chemistry requires experimentally aligned modeling tools. A porous electrode theory-based model incorporating key electrolyte dissociation chemistry is developed for the LiS cell. The proposed chemistry produces a radical anion species that is widely observed spectroscopically in LiS electrolytes. We explore the implications of radical anion formation on the current-voltage behavior within the context of a state-of-art high energy density LiS cell with low electrolyte: sulfur (E/S) ratio and ideally-protected anode. Parameters describing the dissociation reaction equilibrium and kinetics are shown to alter the electrolyte speciation in ways that can be linked to observations from LiS electrolyte engineering experiments.

A survey of physical phenomena in the modeling literature and challenges for accelerating development of LiS batteries using continuum models.