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We develop a Stockmayer fluid model for molecular dynamics simulations of ionic liquids that captures molecular polarization, ionic conductivity, viscosity, and glass transition temperature, using ethylammonium nitrate (EAN) as an example. The ions in EAN are treated as spheres interacting via the Lennard-Jones potential with an embedded point charge and a permanent dipole moment. We show that our simulation results for EAN are consistent with experimental data and then explore the effects of the molecular parameters on the viscosity of ionic liquids. Our results indicate that viscosity monotonically increases with ionic charge and dipole moment but non-monotonically changes with ionic diameter (or molar volume). This non-monotonic trend arises from the competition among the electrostatic interactions, molecular packing, and size asymmetry between the cation and anion. Our model also shows that long-lived ion pairs result in higher viscosities. 

Abstract BackgroundAntarctica and its unique biodiversity are increasingly at risk from the effects of global climate change and other human influences. A significant recent element underpinning strategies for Antarctic conservation has been the development of a system of Antarctic Conservation Biogeographic Regions (ACBRs). The datasets supporting this classification are, however, dominated by eukaryotic taxa, with contributions from the bacterial domain restricted to Actinomycetota and Cyanobacteriota. Nevertheless, the ice-free areas of the Antarctic continent and the sub-Antarctic islands are dominated in terms of diversity by bacteria. Our study aims to generate a comprehensive phylogenetic dataset of Antarctic bacteria with wide geographical coverage on the continent and sub-Antarctic islands, to investigate whether bacterial diversity and distribution is reflected in the current ACBRs. ResultsSoil bacterial diversity and community composition did not fully conform with the ACBR classification. Although 19% of the variability was explained by this classification, the largest differences in bacterial community composition were between the broader continental and maritime Antarctic regions, where a degree of structural overlapping within continental and maritime bacterial communities was apparent, not fully reflecting the division into separate ACBRs. Strong divergence in soil bacterial community composition was also apparent between the Antarctic/sub-Antarctic islands and the Antarctic mainland. Bacterial communities were partially shaped by bioclimatic conditions, with 28% of dominant genera showing habitat preferences connected to at least one of the bioclimatic variables included in our analyses. These genera were also reported as indicator taxa for the ACBRs. ConclusionsOverall, our data indicate that the current ACBR subdivision of the Antarctic continent does not fully reflect bacterial distribution and diversity in Antarctica. We observed considerable overlap in the structure of soil bacterial communities within the maritime Antarctic region and within the continental Antarctic region. Our results also suggest that bacterial communities might be impacted by regional climatic and other environmental changes. The dataset developed in this study provides a comprehensive baseline that will provide a valuable tool for biodiversity conservation efforts on the continent. Further studies are clearly required, and we emphasize the need for more extensive campaigns to systematically sample and characterize Antarctic and sub-Antarctic soil microbial communities. 

We develop a Stockmayer fluid model that accounts for the dielectric responses of polar solvents (water, MeOH, EtOH, acetone, 1-propanol, DMSO, and DMF) and NaCl solutions. These solvent molecules are represented by Lennard-Jones (LJ) spheres with permanent dipole moments and the ions by charged LJ spheres. The simulated dielectric constants of these liquids are comparable to experimental values, including the substantial decrease in the dielectric constant of water upon the addition of NaCl. Moreover, the simulations predict an increase in the dielectric constant when considering the influence of ion translations in addition to the orientation of permanent dipoles. 

Geometric frustration offers a pathway to soft matter self-assembly with controllable finite sizes. While the understanding of frustration in soft matter assembly derives almost exclusively from continuum elastic descriptions, a current challenge is to understand the connection between microscopic physical properties of misfitting “building blocks” and emergent assembly behavior at the mesoscale. We present and analyze a particle-based description of what is arguably the best studied example for frustrated soft matter assembly, negative-curvature ribbon assembly, observed in both assemblies of chiral surfactants and shape-frustrated nanoparticles. Based on our particle model, known as saddle wedge monomers, we numerically test the connection between microscopic shape and interactions of the misfitting subunits and the emergent behavior at the supra-particle scale, specifically focussing on the propagation and relaxation of inter-particle strains, the emergent role of extrinsic shape on frustrated ribbons and the equilibrium regime of finite width selection. Beyond the intuitive role of shape misfit, we show that self-limitation is critically dependent on the finite range of cohesive interactions, with larger size finite assemblies requiring increasing short-range interparticle forces. Additionally, we demonstrate that non-linearities arising from discrete particle interactions alter self-limiting behavior due to both strain-softening in shape-flattened assembly and partial yielding of highly strained bonds, which in turn may give rise to states of hierarchical, multidomain assembly. Tracing the regimes of frustration-limited assembly to the specific microscopic features of misfitting particle shapes and interactions provides necessary guidance for translating the theory of size-programmable assembly into design of intentionally-frustrated colloidal particles. 

Abstract Functional precision medicine offers a promising complement to genomics-based cancer therapy guidance by testing drug efficacy directly on a patient’s tumor cells. Here, we describe a workflow that utilizes single-cell mass measurements with inline brightfield imaging and machine-learning based image classification to broaden the clinical utility of such functional testing for cancer. Using these image-curated mass measurements, we characterize mass response signals for 60 different drugs with various mechanisms of action across twelve different cell types, demonstrating an improved ability to detect response for several slow acting drugs as compared with standard cell viability assays. Furthermore, we use this workflow to assess drug responses for various primary tumor specimen formats including blood, bone marrow, fine needle aspirates (FNA), and malignant fluids, all with reports generated within two days and with results consistent with patient clinical responses. The combination of high-resolution measurement, broad drug and malignancy applicability, and rapid return of results offered by this workflow suggests that it is well-suited to performing clinically relevant functional assessment of cancer drug response.