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Abstract Tomonaga-Luttinger liquid (TLL) behavior in one-dimensional systems has been predicted and shown to occur at semiconductor-to-metal transitions within two-dimensional materials. Reports of one-dimensional defects hosting a Fermi liquid or a TLL have suggested a dependence on the underlying substrate, however, unveiling the physical details of electronic contributions from the substrate require cross-correlative investigation. Here, we study TLL formation within defectively engineered WS₂atop graphene, where band structure and the atomic environment is visualized with nano angle-resolved photoelectron spectroscopy, scanning tunneling microscopy and spectroscopy, and non-contact atomic force microscopy. Correlations between the local density of states and electronic band dispersion elucidated the electron transfer from graphene into a TLL hosted by one-dimensional metal (1DM) defects. It appears that the vertical heterostructure with graphene and the induced charge transfer from graphene into the 1DM is critical for the formation of a TLL. 

Abstract BackgroundPerivascular spaces (PVSs) carry cerebrospinal fluid (CSF) around the brain, facilitating healthy waste clearance. Measuring those flows in vivo is difficult, and often impossible, because PVSs are small, so accurate modeling is essential for understanding brain clearance. The most important parameter for modeling flow in a PVS is its hydraulic resistance, defined as the ratio of pressure drop to volume flow rate, which depends on its size and shape. In particular, the local resistance per unit length varies along a PVS and depends on variations in the local cross section. MethodsUsing segmented, three-dimensional images of pial PVSs in mice, we performed fluid dynamical simulations to calculate the resistance per unit length. We applied extended lubrication theory to elucidate the difference between the calculated resistance and the expected resistance assuming a uniform flow. We tested four different approximation methods, and a novel correction factor to determine how to accurately estimate resistance per unit length with low computational cost. To assess the impact of assuming unidirectional flow, we also considered a circular duct whose cross-sectional area varied sinusoidally along its length. ResultsWe found that modeling a PVS as a series of short ducts with uniform flow, and numerically solving for the flow in each, yields good resistance estimates at low cost. If the second derivative of area with respect to axial location is less than 2, error is typically less than 15%, and can be reduced further with our correction factor. To make estimates with even lower cost, we found that instead of solving for the resistance numerically, the well-known resistance of a circular duct could be scaled by a shape factor. As long as the aspect ratio of the cross section was less than 0.7, the additional error was less than 10%. ConclusionsNeglecting off-axis velocity components underestimates the average resistance, but the error can be reduced with a simple correction factor. These results could increase the accuracy of future models of brain-wide and local CSF flow, enabling better prediction of clearance, for example, as it varies with age, brain state, and pathological conditions. 

Abstract The largest cells are orders of magnitude bigger than the smallest cells. Organelle content scales to maintain cell function, with different organelles increasing in volume, length, or number as cells increase in size. Scaling may also reflect functional demands placed on organelles by increased cell size. Amphibians exhibit exceptional diversity in cell size. Using transmission electron microscopy, we analyzed 3 species whose enterocyte cell volumes range from 228 to 10,593 μm3. We show that nuclear volume increases by an increase in radius while mitochondrial volume increases by an increase in total network length; the endoplasmic reticulum and Golgi apparatus, with their complex shapes, are intermediate. Notably, all 4 organelle types increase in total volume proportional to cell volume, despite variation in functional (i.e., metabolic, transport) demands. This pattern suggests that organellar building blocks are incorporated into more or larger organelles following the same rules across species that vary ~50-fold in cell sizes, consistent with a “limited precursor” model for organellar scaling that, in turn, assumes equivalent cytoplasmic concentrations of organellar building block proteins. Taken together, our results lead us to hypothesize that salamanders have evolved increased biosynthetic capacity to maintain functional protein concentrations despite huge cell volumes. 

Abstract Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS₂, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co$${}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$ ${}_S^0$) and fabricate it with scanning tunneling microscopy (STM). The Co$${}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$ ${}_S^0$electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects. 

Abstract PurposeAquatic plants, including rice, develop iron (Fe) plaques on their roots due to radial oxygen loss (ROL), and these plaques accumulate both beneficial and toxic elements. Silicon is an important nutrient for rice and both accumulates in Fe plaque and can affect ROL. How these plaques form over time and how Si affects this process remains unclear. MethodsRice was grown in a pot study with 4 levels of added Si. Root Fe plaque formation was monitored weekly using vinyl films placed between the pot and soil. Plants were grown to maturity and then ratooned to also examine the formation of Fe plaque during the ratoon crop. ResultsIron plaque formation increased exponentially during the vegetative phase, peaked at the booting phase, then decreased exponentially – a pattern that repeated in the ratoon crop. While the highest Si treatment led to an earlier onset of Fe plaque formation, increasing Si decreased the amount of Fe plaque at harvest, resulting in a minimal net effect. ConclusionsThe kinetics of Fe plaque formation are dependent on rice growth stage, which may affect whether the Fe plaque is a source or sink of elements such as phosphorous and arsenic. 

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building ‘next generation’ genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness. 

Abstract Measuring the similarity between two arbitrary crystal structures is a common challenge in crystallography and materials science. Although there are an infinite number of ways to mathematically relate two crystal structures, only a few are physically meaningful. Here we introduce both a geometry-based and a symmetry-adapted similarity metric to compare crystal structures. Using crystal symmetry and combinatorial optimization we describe an algorithm to arrive at the structural relationship that minimizes these similarity metrics across all possible maps between any pair of crystal structures. The approach makes it possible to (i) identify pairs of crystal structures that are identical, (ii) quantitatively measure the similarity between crystal structures, and (iii) find and rank structural transformation pathways between any pair of crystal structures. We discuss the advantages of using the symmetry-adapted cost metric over the geometric cost. Finally, we show that all known structural transformation pathways between common crystal structures are recovered with the mapping algorithm. The methodology presented in this study will be of value to efforts that seek to catalogue crystal structures, identify structural transformation pathways or prune large first-principles datasets used to parameterize on-lattice Hamiltonians.