Structure of nanoparticles embedded in micellar polycrystals

We investigate by scattering techniques the structure of water-based soft composite materials comprising a crystal made of Pluronic block-copolymer micelles arranged in a face-centered cubic lattice and a small amount (at most 2% by volume) of silica nanoparticles, of size comparable to that of the micelles. The copolymer is thermosensitive: it is hydrophilic and fully dissolved in water at low temperature (T ~ 0{\deg}C), and self-assembles into micelles at room temperature, where the block-copolymer is amphiphilic. We use contrast matching small-angle neuron scattering experiments to probe independently the structure of the nanoparticles and that of the polymer. We find that the nanoparticles do not perturb the crystalline order. In addition, a structure peak is measured for the silica nanoparticles dispersed in the polycrystalline samples. This implies that the samples are spatially heterogeneous and comprise, without macroscopic phase separation, silica-poor and silica-rich regions. We show that the nanoparticle concentration in the silica-rich regions is about tenfold the average concentration. These regions are grain boundaries between crystallites, where nanoparticles concentrate, as shown by static light scattering and by light microscopy imaging of the samples. We show that the temperature rate at which the sample is prepared strongly influence the segregation of the nanoparticles in the grain-boundaries.Comment: accepted for publication in Langmui

Towards a generalized methodology for smart antenna measurements

The huge expansion of mobile communications and the need for high data rate services require more efficient use of the spectrum to increase the capacity of networks and enhance the quality of services. Within that frame, the adoption of Smart Antenna techniques in future wireless systems is expected to have a significant impact on the aforementioned needs. Following the proliferation of the use of Smart Antennas systems there is a growing need for characterization of such systems which is still an open issue. In this work, a generalized methodology for Smart Antenna characterization measurements is introduced. Simulation results from the application of the proposed measurement procedure using a reference array to characterise the smart antenna algorithm subsystem are presented

Quantitative description of temperature induced self-aggregation thermograms determined by differential scanning calorimetry

A novel thermodynamic approach for the description of differential scanning calorimetry (DSC) experiments on self-aggregating systems is derived and presented. The method is based on a mass action model where temperature dependence of aggregation numbers is considered. The validity of the model was confirmed by describing the aggregation behavior of poly(ethylene oxide)-poly(propylene oxide) block copolymers, which are well-known to exhibit a strong temperature dependence. The quantitative description of the thermograms could be performed without any discrepancy between calorimetric and van 't Hoff enthalpies, and moreover, the aggregation numbers obtained from the best fit of the DSC experiments are in good agreement with those obtained by light scattering experiments corroborating the assumptions done in the derivation of the new model

A statewide evaluation of seven strategies to reduce opioid overdose in North Carolina

Background In response to increasing opioid overdoses, US prevention efforts have focused on prescriber education and supply, demand and harm reduction strategies. Limited evidence informs which interventions are effective. We evaluated Project Lazarus, a centralised statewide intervention designed to prevent opioid overdose. Methods Observational intervention study of seven strategies. 74 of 100 North Carolina counties implemented the intervention. Dichotomous variables were constructed for each strategy by county-month. Exposure data were: Process logs, surveys, addiction treatment interviews, prescription drug monitoring data. Outcomes were: Unintentional and undetermined opioid overdose deaths, overdose-related emergency department (ED) visits. Interrupted time-series Poisson regression was used to estimate rates during preintervention (2009-2012) and intervention periods (2013-2014). Adjusted IRR controlled for prescriptions, county health status and time trends. Time-lagged regression models considered delayed impact (0-6 months). Results In adjusted immediate-impact models, provider education was associated with lower overdose mortality (IRR 0.91; 95% CI 0.81 to 1.02) but little change in overdose-related ED visits. Policies to limit ED opioid dispensing were associated with lower mortality (IRR 0.97; 95% CI 0.87 to 1.07), but higher ED visits (IRR 1.06; 95% CI 1.01 to 1.12). Expansions of medication-assisted treatment (MAT) were associated with increased mortality (IRR 1.22; 95% CI 1.08 to 1.37) but lower ED visits in time-lagged models. Conclusions Provider education related to pain management and addiction treatment, and ED policies limiting opioid dispensing showed modest immediate reductions in mortality. MAT expansions showed beneficial effects in reducing ED-related overdose visits in time-lagged models, despite an unexpected adverse association with mortality

Nanoparticle Network Formation in Nanostructured and Disordered Block Copolymer Matrices

Incorporation of nanoparticles composed of surface-functionalized fumed silica (FS) or native colloidal silica (CS) into a nanostructured block copolymer yields hybrid nanocomposites whose mechanical properties can be tuned by nanoparticle concentration and surface chemistry. In this work, dynamic rheology is used to probe the frequency and thermal responses of nanocomposites composed of a symmetric poly(styrene-b-methyl methacrylate) (SM) diblock copolymer and varying in nanoparticle concentration and surface functionality. At sufficiently high loading levels, FS nanoparticle aggregates establish a load-bearing colloidal network within the copolymer matrix. Transmission electron microscopy images reveal the morphological characteristics of the nanocomposites under these conditions

Degradation versus self-assembly of block copolymer micelles

The stability of micelles self-assembled from block copolymers can be altered by the degradation of the blocks. Slow degradation shifts the equilibrium size distribution of block copolymer micelles and change their properties. Quasi-equilibrium scaling theory shows that the degradation of hydrophobic blocks in the core of micelles destabilize the micelles reducing their size, while the degradation of hydrophilic blocks forming coronas of micelles favors larger micelles and may, at certain conditions, induce the formation of micelles from individual chains.Comment: Published in Langmuir http://pubs.acs.org/doi/pdf/10.1021/la204625

Wavelet Neural Network Methodology for Ground Resistance Forecasting

Motivated by the need of engineers for a flexible and reliable tool for estimating and predicting grounding systems behavior, this study developed a model that accurately describes and forecasts the dynamics of ground resistance variation. It is well-known that grounding systems are a key of high importance for the safe operation of electrical facilities, substations, transmission lines and, generally, electric power systems. Yet, in most cases, during the design stage, electrical engineers and researchers have limited information regarding the terrain’s soil resistivity variation. Moreover, the periodic measurement of ground resistance is hindered very often by the residence and building infrastructure. The model, developed in the present study, consists of a nonlinear, nonparametric Wavelet Neural Network (WNN), trained in field measurements of soil resistivity and rainfall height, observed the past four years. The proposed framework is tested in five different grounding systems with different ground enhancing compounds, so that can be used for the evaluation of the behavior of several ground enhancing compounds, frequently used in grounding practice. The research results indicate that the WNN can constitute an accurate model for ground resistance forecasting and can be a useful tool in the disposal of electrical engineers. Therefore, this paper introduces the wavelet analysis in the field of ground resistance evaluation and endeavors to take advantage of the benefits of computational intelligence

Designing AfriCultuReS services to support food security in Africa

ABSTRACT: Earth observation (EO) data are increasingly being used to monitor vegetation and detect plant growth anomalies due to water stress, drought, or pests, as well as to monitor water availability, weather conditions, disaster risks, land use/land cover changes and to evaluate soil degradation. Satellite data are provided regularly by worldwide organizations, covering a wide variety of spatial, temporal and spectral characteristics. In addition, weather, climate and crop growth models provide early estimates of the expected weather and climatic patterns and yield, which can be improved by fusion with EO data. The AfriCultuReS project is capitalizing on the above to contribute towards an integrated agricultural monitoring and early warning system for Africa, supporting decision making in the field of food security. The aim of this article is to present the design of EO services within the project, and how they will support food security in Africa. The services designed cover the users' requirements related to climate, drought, land, livestock, crops, water, and weather. For each category of services, results from one case study are presented. The services will be distributed to the stakeholders and are expected to provide a continuous monitoring framework for early and accurate assessment of factors affecting food security in Africa.This paper is part of the AfriCultuReS project "Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing", which received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreement No. 77465

Capital structure revisited. Do crisis and competition matter in a Keiretsu corporate structure?

The file attached to this record is the author's final peer reviewed version.open accessWe investigate firm-level determinants of capital structure using a large sample of 4,284 Japanese firms over a nineteen-year period (i.e., over 61,000 firm-year observations), a hitherto less examined sample for this purpose. We conduct our analysis and interpret our findings predominantly within the pecking order, the trade-off and the agency theoretical frameworks. We uncover three new findings. First, our evidence indicates that insights derived from the extant literature on capital structure are cross-national and are applicable in the context of Japan, despite the unique characteristics of Japanese firms. Second, financial crisis significantly impacts the relationship between leverage and firm-level determinants, particularly accentuating the effect of asset tangibility and growth. Third, product market competition significantly impacts the observed relationship between firm-level determinants and leverage. Our results are robust, controlling for the joint effects of competition and crisis