Gyroid-Forming Diblock Copolymers Confined in Cylindrical Geometry: A Case of Extreme Makeover for Domain Morphology

The self-assembly of gyroid-forming diblock copolymers confined in cylindrical geometry is studied using a combination of computer simulations and experiments. The simulations, based on a system qualitatively representative of poly(styrene-b-isoprene), are performed with cylindrical nanopores of different diameter (D) and surface selectivity. The effects of the pore size and surface selectivity on morphology are systematically investigated. Different morphological sequences are predicted for two gyroid-forming diblock copolymers. The experiments are carried out on two gyroid-forming poly(styrene-b-dimethylsiloxane) block copolymer samples confined in the core of continuous core−shell nanofibers of different diameters, which are obtained by a coaxial two-fluid electrospinning technique. The internal microphase-separated morphologies of these fibers are investigated by transmission electron microscopy (TEM). Both simulations and experiments demonstrate that a rich variety of structures spontaneously form for the gyroid-forming diblock copolymers, depending on the conditions of cylindrical confinement. Many of these confinement-induced structures are quite different from those of cylinder-forming or lamella-forming block copolymers. Simulations further show that these structures depend sensitively on the block copolymer composition, surface selectivity, and the ratio D/L0 where L0 is the period of the equilibrium gyroid phase. While the simulation and experimental systems are representative of different chemistries, the morphological predictions of simulations are qualitatively consistent with the experimental observations.Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract DAAD-19-02-D-0002)United States. Army Research Offic

Mechanical properties, microstructure and crystallographic texture of magnesium AZ91-D alloy welded by Friction Stir Welding (FSW)

The objective of the study was to characterize the properties of a magnesium alloy welded by friction stir welding (FSW). The results led to a better understanding of the relationship between this process and the microstructure and anisotropic properties of alloy materials. Welding principally leads to a large reduction in grain size in welded zones due to the phenomenon of dynamic recrystallization. The most remarkable observation was that crystallographic textures appeared from a base metal without texture in two zones: the thermo-mechanically affected and stir welded zones. The latter zone has the peculiarity of possessing a marked texture with two components on the basal plane and the pyramidal plane. These characteristics disappeared in the TMAZ, which had only one component following the basal plane. These modifications have been explained by the nature of the plastic deformation in these zones, which occurs at a moderate temperature in the TMAZ and high temperature in the SWZ

Correlation of some environmental variables with adaptive net-spinning strategies in stream larval Hydropsychids (Trichoptera) in Garhwal Himalaya (Short Communication)

Introduction The Hydropsychidae (Trichoptera) being the master spinners of freshwater rapids spin the net s of various sizes required during different seasons, and for different instars. Also they adopt strategies for coexistence between similar and different species by selecting various mesh sizes, current regimes, and habitat modes. The paper highlights the results of the study on the various net spinning strategies and associated environmental variables. In two larval hydropsychids from a small stream ‘Malethagaad in the Garhwal, Central Himalaya (India). Among net – spinning Hydropsychidae, the effect of certain seasonal environmental variables was studied and the most influencing ones recognised were water temperature changes, current speed, and increase in resource particulate concentration downstream. It was observed that in response to increase in all the above parameters (except temperature) net – spinning activity increases, reducing the size of mesh and thus successfully enabling the animal to adjust to the drag of the water current and particle filtration requirements with relation to the body size and microclimate of the dwelling mode. Certain anomalies were observed in hydropsychid capture nets from lotic sidewater pools and an increase in temperature probably also triggered an increase in net – spinning activity during summer, but the mesh shapes and sizes vary. Also probably the behavioural and morphological differences of various instars considered, facilitated in adaptive strategies for coexistence between similar and different species. Summary 1. The Hydropsyche k1 and Hydropsyche k2 divide up net spinning sites by partial differences in water velocity preferences, use of different dwelling crevices and modes of habitat. 2. The catch net mesh was found to be related to current speeds, resource particle requirements and the morphological stages of various instars and the combination of these variables may be taken as deciding factor as to how the mesh size is determined in a particular species to facilitate coexistence among different and similar instars of the same and different putative species. 3. Also the correlation of body size and net dimensions along with the distribution on the stream, with the increased resource particle concentration downstream and larger instars downstream point towards a natural distributional strategy enabling a successful species propagation for instars associated. Tropical Freshwater Biology VOL. 8 1999, pp. 27-3

Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches-random forest, boosted regression trees, and Cubist-were examined for the downscaling of AMSR-E soil moisture (25 9 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [r = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through cross-validation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moistureclos