Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.

Demixing in atmospheric-pressure free-burning arcs is investigated using a two-dimensional numerical model that incorporates the combined diffusion coefficient treatment of diffusion. Arcs in mixtures of argon with helium, nitrogen, oxygen, and hydrogen are modeled. It is found that demixing almost always has a large influence on arc composition, with the greatest changes occurring in the argon-helium and argon-hydrogen arcs. The influence of three different demixing processes is assessed. Demixing due to frictional forces is found to dominate in the high-temperature regions of the arc, while demixing due to mole fraction gradients is important in the regions where dissociation of the molecular gases occurs. Demixing due to thermal diffusion has a smaller effect. The effects of demixing on arc temperature and flow velocity are usually small. The heat flux to the anode is significantly increased near the axis by demixing in argon-nitrogen and argon-hydrogen arcs. The predictions of the model are validated by comparison with spectroscopic measurements of arc composition in argon-nitrogen and argon-helium arcs.

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Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging - and to assess their dynamics during learning - remain limited. Here, we describe an approach based on a distinct application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.

Sustainable ammonia synthesis at ambient conditions that relies on renewable sources of energy and feedstocks is globally sought to replace the Haber–Bosch process. Here, using nitrogen and water as raw materials, a nonthermal plasma catalysis approach is demonstrated as an effective power‐to‐chemicals conversion strategy for ammonia production. By sustaining a highly reactive environment, successful plasma‐catalytic production of NH3 was achieved from the dissociation of N2 and H2O under mild conditions. Plasma‐induced vibrational excitation is found to decrease the N2 and H2O dissociation barriers, with the presence of matched catalysts in the nonthermal plasma discharge reactor contributing significantly to molecular dissociation on the catalyst surface. Density functional theory calculations for the activation energy barrier for the dissociation suggest that ruthenium catalysts supported on magnesium oxide exhibit superior performance over other catalysts in NH3 production by lowering the activation energy for the dissociative adsorption of N2 down to 1.07 eV. The highest production rate, 2.67 mmol gcat.−1 h−1, was obtained using ruthenium catalyst supported on magnesium oxide. This work highlights the potential of nonthermal plasma catalysis for the activation of renewable sources to serve as a new platform for sustainable ammonia production.