Effect of Constituent Core-sizes on Microstructure and Dielectric Properties of BaTiO<Subscript>3</Subscript>@(0.6Ba-TiO<Subscript>3</Subscript>-0.4BiAlO<Subscript>3</Subscript>) Core-Shell Material

The fundamental characteristics of varied initial core-sizes of BaTiO₃(BT) and its influential role on the morphology and dielectric properties of BaTiO₃@0.6BaTiO₃-0.4BiAlO₃(BT@0.6BT-0.4BA) ceramic samples were studied. Alkoxide sol-precipitation method was adopted as revised chemical route to synthesize the constituent “core” BT powders in a dispersed phase, whereas the distinctive initial nano-sized particles were affected by the pre-calcination temperatures (600-900 °C).The microstructure of the uncoated BT ceramics revealed an exaggerated grain growth with an optimized dielectric constant (ε_max >9 000) whilst the coated ceramics behaved otherwise (grain growth inhibited) when sintered at an elevated temperature. Regardless of the previously studied solubility limit (about 0.1%) of BT-BA samples, BT@0.6BT-0.4BA maintained a maximum dielectric constant (ε_max) ranging from 1 592 to 1 708 and tan δ less than 2% under a unit mole ratio at room temperature. In view of all these analyses, the initial nanometer sizes of the as-prepared BT-core powders combined with the increase effect of cation substitutions of Bi³⁺ and Al³⁺ in the shell content, induced the diffuse transition phase of BT@0.6BT-0.4BA composition.     

Living Materials Herald a New Era in Soft Robotics

Living beings have an unsurpassed range of ways to manipulate objects and interact with them. They can make autonomous decisions and can heal themselves. So far, a conventional robot cannot mimic this complexity even remotely. Classical robots are often used to help with lifting and gripping and thus to alleviate the effects of menial tasks. Sensors can render robots responsive, and artificial intelligence aims at enabling autonomous responses. Inanimate soft robots are a step in this direction, but it will only be in combination with living systems that full complexity will be achievable. The field of biohybrid soft robotics provides entirely new concepts to address current challenges, for example the ability to self‐heal, enable a soft touch, or to show situational versatility. Therefore, “living materials” are at the heart of this review. Similarly to biological taxonomy, there is a recent effort for taxonomy of biohybrid soft robotics. Here, an expansion is proposed to take into account not only function and origin of biohybrid soft robotic components, but also the materials. This materials taxonomy key demonstrates visually that materials science will drive the development of the field of soft biohybrid robotics.     

Patterning a π-conjugated polyelectrolyte through sequential polymerization of a bifunctional ionic liquid monomer

An electronically conductive polyelectrolyte is prepared by the sequential polymerization of a bifunctional imidazolium-based ionic liquid (IL) monomer, composed of a thienyl and vinyl containing cation paired with a tetrafluoroborate anion. In the first step, potentiodynamic electropolymerization of the thienyl moiety forms a cationic polyalkylthiophene that is soluble in select organic solvents. Cyclic voltammetry (CV) was used to determine the polymer p-doping potential (0.31 V) and the bipolaronic state (1.49 V). The polymer exhibits electrochromism, converting from red in the neutral state (λ_max = 443 nm) to dark blue in the polaronic state (λ_max = 819 nm). The solution-processable polymer can be cast into a film, masked and patterned by UV-initiated free radical polymerization of the vinyl moiety. Small-angle X-ray scattering (SAXS) revealed that the insoluble crosslinked polyalkylthiophene–polyvinylimidazolium adopts a lamellar structure with a lattice spacing of 3.3 nm. Four-probe d.c. conductivity measurements determined the de-doped electrical conductivity was 1.0 × 10⁻² S/cm. The results underscore the importance of the anion in controlling the polymerization of IL monomers.     

Quantification of Protein‐Induced Membrane Remodeling Kinetics In Vitro with Lipid Multilayer Gratings

The dynamic self‐organization of lipids in biological systems is a highly regulated process that enables the compartmentalization of living systems at micro‐ and nanoscopic scales. Consequently, quantitative methods for assaying the kinetics of supramolecular remodeling such as vesicle formation from planar lipid bilayers or multilayers are needed to understand cellular self‐organization. Here, a new nanotechnology‐based method for quantitative measurements of lipid–protein interactions is presented and its suitability for quantifying the membrane binding, inflation, and budding activity of the membrane‐remodeling protein Sar1 is demonstrated. Lipid multilayer gratings are printed onto surfaces using nanointaglio and exposed to Sar1, resulting in the inflation of lipid multilayers into unilamellar structures, which can be observed in a label‐free manner by monitoring the diffracted light. Local variations in lipid multilayer volume on the surface is used to vary substrate availability in a microarray format. A quantitative model is developed that allows quantification of binding affinity (K _D) and kinetics (k_on and k_off). Importantly, this assay is uniquely capable of quantifying membrane remodeling. Upon Sar1‐induced inflation of single bilayers from surface supported multilayers, the semicylindrical grating lines are observed to remodel into semispherical buds when a critical radius of curvature is reached.     

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic Multi-Layer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques.

     

HIV risk reduction for incarcerated women: A comparison of brief interventions based on two theoretical models.

Although female inmates are seropositive at rates that exceed those of male inmates, few studies have evaluated HIV risk reduction interventions for incarcerated women. This demonstration project compared an intervention based on social cognitive theory against a comparison condition based on the theory of gender and power. Incarcerated women (N?=?90) were assessed at baseline, postintervention, and again 6 months later. Both interventions produced increased self efficacy, self-esteem, Attitudes Toward Prevention Scale scores, AIDS knowledge, communication skill, and condom application skills that maintained through the 6-month follow-up period. Participants in the intervention based on social cognitive theory showed greater improvement in condom application skills, and women in the program based on the theory of gender and power evidenced greater commitment to change. The results suggests brief interventions in prison settings are feasible and beneficial. However, it is not yet known whether the changes will generalize into the natural environment after the women' s release into the community. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The influence of processing methods on the dielectric properties of BaTi1-xGdxO3-x/2 - Based materials

In this study, the intermediate rare-earth oxide Gd₂O₃ (Gd) was substituted in different amounts (x = 0.2–2 mol%) for the formulation of BaTi_1-xGd_xO_3-x/2 (BTG_x) dielectric materials. The effect of B-site substitution was confirmed by the additional Raman active A_1g octahedral peak at ~835cm^-1 strengthened at x ≥ 0.4 mol%. Additionally, properties of 0.9BTG_0.007-0.1BA dielectric ceramics were analysed based on the influence of various processing methods as a function of sintering temperature. The focal samples were labelled Method-A (direct-mix) and Method-B (indirect-mix). As the sintering temperature (1075–1200 °C) increased, the 1 kHz response of the ε–T curves of Method-A samples transformed from a single peak to broad-narrow double peaks of high dielectric loss tangent (tan δ). Nonetheless, samples of Method-B possessed a clearly defined transmission electron microscopy (TEM) core-shell structure, flattened double-peak ε-T curves, optimised dielectric properties (ε = ~1563–1851 and tan δ < 1.5% at room temperature), and a wide-ranging temperature behaviour that meets the X8R dielectric standards (ΔC/C_25°C < ±15%). The maximum dielectric breakdown strength of Method-B samples reached ~131 kVcm, while the energy storage density was ~0.726 J/cm³ at a maximum efficiency of ~80% at 1100 °C. Thus, exhibiting good potentials for balancing temperature stability with energy storage applications.     

Subsurface Radioactive Contaminant Transport Modeling Using Particle and Kalman Filter Schemes

Contamination of groundwater by radioactive contaminants can be harmful to the environment. Various prediction models have been adopted to simulate the state of contaminants in the subsurface. Conventional numerical models are simplified by approximation and the model parameters are assumed to be constant, thereby introducing error to the prediction results. Particle and Kalman filters are used in this research to simulate the radioactive contaminant cobalt-57 transport in a subsurface environment by using a two-dimensional advection-dispersion model. A radioactive contaminant concentration was predicted spatially and temporally within boundary conditions. The errors in the prediction results were assessed by using the root-mean-square-error (RMSE) equation. The results show that the Kalman filter performs better than the particle filter when the prediction model is linear. Furthermore, the results from filters are closer to the true value in comparison with the numerical solution, and the filters are capable of reducing the RMSE of the numerical solution by approximately 80%.