Investigating the effect of silver nanorods embedded in polydimethylsiloxane matrix using nanoindentation and its use for flexible electronics

The stretchable electrodes with excellent flexibility, electrical conductivity, and mechanical durability are the most fundamental components in the emerging and exciting field of flexible electronics. This article proposes a method for fabrication of such a stretchable electrode by embedding silver nanorods (AgNRs) into a polydimethylsiloxane (PDMS) matrix that is grown by a unique glancing angle deposition technique. The surface, mechanical, and electrical properties of PDMS are significantly changed after embedding the AgNRs in it. The results show that surface roughness and polarity increase after AgNRs are embedded in the PDMS matrix. Elastic modulus (E) and hardness (H) decrease with an increase in the indentation load as a result of the indentation depth effect. Due to strong interfacial adhesion of AgNRs embedded in the PDMS matrix, the E and H of nanocomposite are increased by 167.6 and 93.3% compared with PDMS film, respectively. Furthermore, the AgNRs-PDMS film has an electrical resistivity value in the order of 10⁻⁷ Ωm. It remains conductive during various mechanical strains such as bending, twisting, and stretching, which is demonstrated using a light-emitting diode circuit. Simultaneously, the antimicrobial activity of silver could make it a promising candidate for wearable electronics.     

Fracture characteristics of a burst tested maraging steel rocket motor case

A 0.3 m diameter, 2 m long and 0.0015 m thick, 18 nickel 1800 MNm^–2 grade maraging steel motor case was designed, fabricated and burst tested to gain experience for using the steel as booster case material in satellite launch vehicles. The bursting occurred at 15.2 MPa for which the effective hoop stress worked out to be 1754 MNm^–2 almost equal to the ultimate tensile strength (1764 MNm^–2) of the material in the solution treated and aged condition. The failure analysis revealed that the material failed due to normal tensile overload fracture. The burst test data was used to arrive at fracture mechanics parameters like crack size, gross section area stress and the stress for leak before bursting.     

Sequence,Chromatin and Evolution of Satellite DNA

Satellite DNA consists of abundant tandem repeats that play important roles in cellular processes, including chromosome segregation, genome organization and chromosome end protection. Most satellite DNA repeat units are either of nucleosomal length or 5–10 bp long and occupy centromeric, pericentromeric or telomeric regions. Due to high repetitiveness, satellite DNA sequences have largely been absent from genome assemblies. Although few conserved satellite-specific sequence motifs have been identified, DNA curvature, dyad symmetries and inverted repeats are features of various satellite DNAs in several organisms. Satellite DNA sequences are either embedded in highly compact gene-poor heterochromatin or specialized chromatin that is distinct from euchromatin. Nevertheless, some satellite DNAs are transcribed into non-coding RNAs that may play important roles in satellite DNA function. Intriguingly, satellite DNAs are among the most rapidly evolving genomic elements, such that a large fraction is species-specific in most organisms. Here we describe the different classes of satellite DNA sequences, their satellite-specific chromatin features, and how these features may contribute to satellite DNA biology and evolution. We also discuss how the evolution of functional satellite DNA classes may contribute to speciation in plants and animals.     

Bulk free radical polymerizations of methyl methacrylateunder non-isothermal conditions and with intermediate additionof initiator: Experiments and modeling

Experimental data on the monomer conversion, x_m, and the weight average molecular weight, M_w, have been generated under several isothermal and non-isothermal conditions for the polymerization of methyl methacrylate in a rheometer-reactor assembly. The non-isothermal results, in particular, can be used to provide more stringent tests of kinetic models than isothermal data alone. A simple empirical model has been used to describe this system that accounts for the gel (Trommsdorff) and glass effects. The model involves only x_m and the temperature, and is quite general. The model parameters are tuned using only three sets of isothermal data. Good agreement is found between the experimental results and model predictions for a whole variety of experimental conditions, including non-isothermal operation and with intermediate addition of initiator. Because of its generality, this model is quite suitable for use for on-line optimizing control as well as for describing industrial reactors.     

Viscosity of bulk free radical polymerizing systems under near-isothermal and non-isothermal conditions

A viscometer-reactor assembly is used to generate data on the viscosity, η(t), of an example polymerizing system exhibiting the Trommsdorff effect, namely, the bulk free radical polymerization of methyl methacrylate (MMA), at different temperature conditions [near-isothermal and non-isothermal (near-step increase and near-step decrease in temperature)] and at two different initiator, 2,2′-azoisobutyronitrile (AIBN), concentrations. Two types of cup and bob assemblies, viz., the Haake^® SV-2 and the Haake^® HV-DIN, have been used to measure η(t) of the reaction mass, until reasonably high values of viscosity, well into the gel effect region. Only three sets of experimental data on x_m(t), M_w(t) and η(t) under near-isothermal conditions, are used to develop general correlations for the viscosity. These tuned correlations predict the values of the viscosity for a whole variety of other experimental conditions, including non-isothermal cases, reflecting that the physics of the system is well represented by them. Hence, these correlations can be used for other systems after tuning their parameters. The feasibility of on-line soft sensing is demonstrated for a few cases.     

Low frequency ac conduction and dielectric relaxation behavior of solution grown and uniaxially stretched poly(vinylidene fluoride) films

The measurements of ac conductivity [σ_m(ω)], dielectric constant [?′(ω)] and loss [?″(ω)] have been performed on solution grown (thickness ∼85 μm) and uniaxially stretched (thickness ∼25, 45 and 80 μm) films of poly(vinylidene fluoride) (PVDF) in the frequency range 0.1 kHz-10 MHz and in the temperature range 77-400 K. The σ_m(ω) can be described by the relation σ(ω) = Aω^s, where s is close to unity and decreases with increase in temperature. Three relaxations, observed in the present investigation, have been designated as the α_c-, the α_a- and the β-relaxations appearing from high temperature side to the low temperature side. The α_c-relaxation could not be observed in the case of uniaxially stretched poly(vinylidene fluoride) films. The α_c- and α_a-relaxations are associated with the molecular motions in the crystalline regions and micro-Brownian motion in the amorphous regions of the main polymer chain, respectively, whereas the β-relaxation is attributed to the rotation of side group dipoles or to the local oscillations of the frozen main polymer chain.     

Diffusion coefficient in polymer solutions

Molecular diffusivity of a solute in a solvent may be determined by measuring the extent of dispersion of solute in solvent flowing in a straight circular tube under the conditions of laminar flow. This simple and rapid method for determination of molecular diffusivity in aquous polymer solutions is discussed. Experimental results show a substantial reduction in the solute diffusivity with increase in polymer concentration.     

Performance models of storage contention in cloud environments

We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found our models to be effective in predicting such quantities across a range of synthetic and emulated application workloads.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.