ABSTRACT
We demonstrate a universal approach to extract one- and two-dimensional nanomaterials from contaminated water, which is based on a microscopic oil-water interface trapping mechanism. Results indicate that carbon nanotubes, graphene, boron nitride nanotubes, boron nitride nanosheets, and zinc oxide nanowires can be successfully extracted from contaminated water at a successful rate of nearly 100%. The effects of surfactants, particle shape, and type of organic extraction fluids are evaluated. The proposed extraction mechanism is also supported by in situ monitoring of the extraction process. We believe that this extraction approach will prove important for the purification of water contaminated by nanoparticles and will support the widespread adoption of nanomaterial applications.
ABSTRACT
Most seafloor hydrothermal circulation occurs far from the magmatic influence of mid-ocean ridges, driving large flows of water, heat and solutes through volcanic rock outcrops on ridge flanks. Here we create three-dimensional simulations of ridge-flank hydrothermal circulation, flowing between and through seamounts, to determine what controls hydrogeological sustainability, flow rate and preferred flow direction in these systems. We find that sustaining flow between outcrops that penetrate less-permeable sediment depends on a contrast in transmittance (the product of outcrop permeability and the area of outcrop exposure) between recharging and discharging sites, with discharge favoured through less-transmissive outcrops. Many simulations include local discharge through outcrops at the recharge end of an outcrop-to-outcrop system. Both of these characteristics are observed in the field. In addition, smaller discharging outcrops sustain higher flow rates than larger outcrops, which may help to explain how so much lithospheric heat is extracted globally by this process.
ABSTRACT
Image-based screening has become a mature field over the past decade, largely due to the detailed information that can be obtained about compound mode of action by considering the phenotypic effects of test compounds on cellular morphology. However, very few examples exist of extensions of this approach to bacterial targets. We now report the first high-throughput, high-content platform for the prediction of antibiotic modes of action using image-based screening. This approach employs a unique feature segmentation and extraction protocol to quantify key size and shape metrics of bacterial cells over a range of compound concentrations, and matches the trajectories of these metrics to those of training set compounds of known molecular target to predict the test compound's mode of action. This approach has been used to successfully predict the modes of action of a panel of known antibiotics, and has been extended to the evaluation of natural products libraries for the de novo prediction of compound function directly from primary screening data.
