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Cubic CsH2PO4, which crystallizes in the CsCl structure-type, can support a large concentration of excess Cs via phosphate vacancies. Dissolution of Cs3PO4 into the cubic structure lowers the temperature at which superprotonic conductivity occurs. 

We introduce a technique for extracting microstructural geometry from NMR line shape analysis in porous materials at angstrom-scale resolution with the use of weak magnetic field gradients. Diverging from the generally held view of FID signals undergoing simple exponential decay, we show that a detailed analysis of the line shape can unravel structural geometry on much smaller scales than previously thought. While the original q-space PFG NMR relies on strong magnetic field gradients in order to achieve high spatial resolution, our current approach reaches comparable or higher resolution using much weaker gradients. As a model system, we simulated gas diffusion for xenon confined within carbon nanotubes over a range of temperatures and nanotube diameters in order to unveil manifestations of confinement in the diffusion behavior. We report a multiscale scheme that couples the above-mentioned MD simulations with the generalized Langevin equation to estimate the transport properties of interest for this problem, such as diffusivity coefficients and NMR line shapes, using the Green–Kubo correlation function to correctly evaluate time-dependent diffusion. Our results highlight how NMR methodologies can be adapted as effective means toward structural investigation at very small scales when dealing with complicated geometries. This method is expected to find applications in materials science, catalysis, biomedicine, and other areas. 

Developmental biology-inspired strategies for tissue-building have extraordinary promise for regenerative medicine, spurring interest in the relationship between cell biophysical properties and morphological transitions. However, mapping gene or protein expression data to cell biophysical properties to physical morphogenesis remains challenging with current techniques. Here, we presentmultiplexedadhesion andtraction ofcells athighyield (MATCHY). MATCHY advances the multiplexing and throughput capabilities of existing traction force and cell–cell adhesion assays using microfabrication and a semiautomated computation scheme with machine learning–driven cell segmentation. Both biophysical assays are coupled with serial downstream immunofluorescence to extract cell type/signaling state information. MATCHY is especially suited to complex primary tissue-, organoid-, or biopsy-derived cell mixtures since it does not rely on a priori knowledge of cell surface markers, cell sorting, or use of lineage-specific reporter animals. We first validate MATCHY on canine kidney epithelial cells engineered for rearranged during transfection (RET) tyrosine kinase expression and quantify a relationship between downstream signaling and cell traction. We then use MATCHY to create a biophysical atlas of mouse embryonic kidney primary cells and identify distinct biophysical states along the nephron differentiation trajectory. Our data complement expression-level knowledge of adhesion molecule changes that accompany nephron differentiation with quantitative biophysical information. These data reveal an “energetic ratchet” that accounts for spatial trends in nephron progenitor cell condensation as they differentiate into early nephron structures, which we validate through agent-based computational simulation. MATCHY offers semiautomated cell biophysical characterization at >10,000-cell throughput, an advance benefiting fundamental studies and new synthetic tissue strategies for regenerative medicine. 

Cation lattice flexibility and covalent bond lengths serve as good physical descriptors of proton conduction in solid acids and enable the discovery of promising proton conductors beyond traditional chemistries. 

The dynamics of viscoelastic fluids are governed by a memory function, essential yet challenging to compute, especially when diffusion faces boundary restrictions. We propose a computational method that captures memory effects by analyzing the time-correlation function of the pressure tensor, a viscosity indicator, through the Stokes–Einstein equation’s analytic continuation into the Laplace domain. We integrate this equation with molecular dynamics simulations to derive necessary parameters. Our approach computes nuclear magnetic resonance (NMR) line shapes using a generalized diffusion coefficient, accounting for temperature and confinement geometry. This method directly links the memory function with thermal transport parameters, facilitating accurate NMR signal computation for non-Markovian fluids in confined geometries. 

The compounds RbH2PO4 and Rb5H7(PO4)4 display eutectoid behavior, with superprotonic α-RbH2−3y(PO4)1−y forming at a eutectoid temperature of 244 °C in a cubic, CsCl-type structure with a high concentration of phosphate vacancies.