Tailoring energy absorption capacity of CNT forests through application of electric field

This study examines the effect of electric field on energy absorption capacity of carbon nanotube forests (CNTFs), comprising of vertically aligned multiwalled carbon nanotubes, under both quasistatic (strain rate, (epsilon) over dot = 10(-3) s(-1)) and dynamic ((epsilon) over dot = similar to 10(3) s(-1)) loading conditions. Under quasistatic condition, the CNTFs were cyclically loaded and unloaded while electric field was applied along the length of carbon nanotube (CNT) either throughout the loading cycle or explicitly during either the loading or the unloading segment. The energy absorbed per cycle by CNTF increased monotonically with electric field when the field was applied only during the loading segment: A 7 fold increase in the energy absorption capacity was registered at an electric field of 1 kV/m whereas no significant change in it was noted for other schemes of electro-mechanical loading. The energy absorption capacity of CNTF under dynamic loading condition also increased monotonically with electric field; however, relative to the quasistatic condition, less pronounced effect was observed. This intriguing strain rate dependent effect of electric field on energy absorption capacity of CNTF is explained in terms of electric field induced strengthening of CNTF, originating from the time dependent electric field induced polarization of CNT. (C) 2015 Elsevier Ltd. All rights reserved.

In-plane effective shear modulus of generalized circular honeycomb structures and bundled tubes in a diamond array structure

Use of circular hexagonal honeycomb structures and tube assemblies in energy absorption systems has attracted a large number of literature on their characterization under crushing and impact loads. Notwithstanding these, effective shear moduli (G*) required for complete transverse elastic characterization and in analyses of hierarchical structures have received scant attention. In an attempt to fill this void, the present study undertakes to evaluate G* of a generalized circular honeycomb structures and tube assemblies in a diamond array structure (DAS) with no restriction on their thickness. These structures present a potential to realize a spectrum of moduli with minimal modifications, a point of relevance for manufactures and designers. To evaluate G* in this paper, models based on technical theories - thin ring theory and curved beam theory - and rigorous theory of elasticity are investigated and corroborated with FEA employing contact elements. Technical theories which give a good match for thin HCS offer compact expressions for moduli which can be harvested to study sensitivity of moduli on topology. On the other hand, elasticity model offers a very good match over a large range of thickness along with exact analysis of stresses by employing computationally efficient expressions. (C) 2015 Elsevier Ltd. All rights reserved.

Effect of Surface Energy Anisotropy on the Stability of Growth Fronts in Multiphase Alloys

Eutectic growth offers a variety of examples for pattern formation which are interesting both for theoreticians as well as experimentalists. One such example of patterns is ternary eutectic colonies which arise as a result of instabilities during growth of two solid phases. Here, in addition to the two major components being exchanged between the solid phases during eutectic growth, there is an impurity component which is rejected by both solid phases. During progress of solidification, there develops a boundary layer of the third impurity component ahead of the solidification front of the two solid phases. Similar to Mullins-Sekerka type instabilities, such a boundary layer tends to make the global solidification envelope unstable to morphological perturbations giving rise to two-phase cells. This phenomenon has been studied numerically in two dimensions for the conditions of directional solidification, by Plapp and Karma (Phys Rev E 66:061608, 2002) using phase-field simulations. While, in the work by Plapp and Karma (Phys Rev E 66:061608, 2002) all interfaces are isotropic, in our presentation, we extend the phase-field model by considering interfacial anisotropy in the solid-solid and solid-liquid interfaces and characterize the role of interfacial anisotropy on the stability of the growth front through phase-field simulations in two dimensions.

Room temperature ferromagnetism and white light emissive CdS:Cr nanoparticles synthesized by chemical co-precipitation method

Undoped and Cr (3% and 5%) doped CdS nanoparticles were synthesized by chemical co-precipitation method. The synthesized nanocrystalline particles are characterized by energy dispersive X-ray analysis (EDAX), scanning electron microscope (SEM), X-ray Diffraction (XRD), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS), photoluminescence (PL), Electron paramagnetic resonance (EPR), vibrating sample magnetometer (VSM) and Raman spectroscopy. XRD studies indicate that Cr doping in host CdS result a structural change from Cubic phase to mixed (cubic + hexagonal) phase. Due to quantum confinement effect, widening of the band gap is observed for undoped and Cr doped CdS nanoparticles compared to bulk CdS. The average particle size calculated from band gap values is in good agreement with the TEM study calculation and it is around 4-5 nm. A strong violet emission band consisting of two emission peaks is observed for undoped CdS nanoparticles, whereas for CdS:Cr nanoparticles, a broad emission band ranging from 420 nm to 730 nm with a maximum at similar to 587 nm is observed. The broad emission band is due to the overlapped emissions from variety of defects. EPR spectra of CdS:Cr samples reveal resonance signal at g = 2.143 corresponding to interacting Cr3+ ions. VSM studies indicate that the diamagnetic CdS nanoparticles are transform to ferromagnetic for 3% Cr3+ doping and the ferromagnetic nature is diminished with increasing the doping concentration to 5%. (C) 2015 Elsevier B.V. All rights reserved.

Near-Optimal Detection Thresholds for Bayesian Spectrum Sensing Under Fading

This paper considers the problem of energy-based, Bayesian spectrum sensing in cognitive radios under various fading environments. Under the well-known central limit theorem based model for energy detection, we derive analytically tractable expressions for near-optimal detection thresholds that minimize the probability of error under lognormal, Nakagami-m, and Weibull fading. For the Suzuki fading case, a generalized gamma approximation is provided, which saves on the computation of an integral. In each case, the accuracy of the theoretical expressions as compared to the optimal thresholds are illustrated through simulations.

Many-Body Localization in the Presence of a Single-Particle Mobility Edge

In one dimension, noninteracting particles can undergo a localization-delocalization transition in a quasiperiodic potential. Recent studies have suggested that this transition transforms into a many-body localization (MBL) transition upon the introduction of interactions. It has also been shown that mobility edges can appear in the single particle spectrum for certain types of quasiperiodic potentials. Here, we investigate the effect of interactions in two models with such mobility edges. Employing the technique of exact diagonalization for finite-sized systems, we calculate the level spacing distribution, time evolution of entanglement entropy, optical conductivity, and return probability to detect MBL. We find that MBL does indeed occur in one of the two models we study, but the entanglement appears to grow faster than logarithmically with time unlike in other MBL systems.

Chemical Shifts to Metabolic Pathways: Identifying Metabolic Pathways Directly from a Single 2D NMR Spectrum

Identifying cellular processes in terms of metabolic pathways is one of the avowed goals of metabolomics studies. Currently, this is done after relevant metabolites are identified to allow their mapping onto specific pathways. This task is daunting due to the complex nature of cellular processes and the difficulty in establishing the identity of individual metabolites. We propose here a new method: ChemSMP (Chemical Shifts to Metabolic Pathways), which facilitates rapid analysis by identifying the active metabolic pathways directly from chemical shifts obtained from a single two-dimensional (2D) C-13-H-1] correlation NMR spectrum without the need for identification and assignment of individual metabolites. ChemSMP uses a novel indexing and scoring system comprised of a ``uniqueness score'' and a ``coverage score''. Our method is demonstrated on metabolic pathways data from the Small Molecule Pathway Database (SMPDB) and chemical shifts from the Human Metabolome Database (HMDB). Benchmarks show that ChemSMP has a positive prediction rate of >90% in the presence of deduttered data and can sustain the same at 60-70% even in the presence of noise, such as deletions of peaks and chemical shift deviations. The method tested on NMR data acquired for a mixture of 20 amino acids shows a success rate of 93% in correct recovery of pathways. When used on data obtained from the cell lysate of an unexplored oncogenic cell line, it revealed active metabolic pathways responsible for regulating energy homeostasis of cancer cells. Our unique tool is thus expected to significantly enhance analysis of NMIR-based metabolomics data by reducing existing impediments.

Relationship between entropy and diffusion: A statistical mechanical derivation of Rosenfeld expression for a rugged energy landscape

Diffusion-a measure of dynamics, and entropy-a measure of disorder in the system are found to be intimately correlated in many systems, and the correlation is often strongly non-linear. We explore the origin of this complex dependence by studying diffusion of a point Brownian particle on a model potential energy surface characterized by ruggedness. If we assume that the ruggedness has a Gaussian distribution, then for this model, one can obtain the excess entropy exactly for any dimension. By using the expression for the mean first passage time, we present a statistical mechanical derivation of the well-known and well-tested scaling relation proposed by Rosenfeld between diffusion and excess entropy. In anticipation that Rosenfeld diffusion-entropy scaling (RDES) relation may continue to be valid in higher dimensions (where the mean first passage time approach is not available), we carry out an effective medium approximation (EMA) based analysis of the effective transition rate and hence of the effective diffusion coefficient. We show that the EMA expression can be used to derive the RDES scaling relation for any dimension higher than unity. However, RDES is shown to break down in the presence of spatial correlation among the energy landscape values. (C) 2015 AIP Publishing LLC.

Design and implementation of spectrum sensing for cognitive radios with a frequency-hopping primary system

In this work, spectrum sensing for cognitive radios is considered in the presence of multiple Primary Users (PU) using frequency-hopping communication over a set of frequency bands. The detection performance of the Fast Fourier Transform (FFT) Average Ratio (FAR) algorithm is obtained in closed-form, for a given FFT size and number of PUs. The effective throughput of the Secondary Users (SU) is formulated as an optimization problem with a constraint on the maximum allowable interference on the primary network. Given the hopping period of the PUs, the sensing duration that maximizes the SU throughput is derived. The results are validated using Monte Carlo simulations. Further, an implementation of the FAR algorithm on the Lyrtech (now, Nutaq) small form factor software defined radio development platform is presented, and the performance recorded through the hardware is observed to corroborate well with that obtained through simulations, allowing for implementation losses. (C) 2015 Elsevier B.V. All rights reserved.

Embodied and operational energy of urban residential buildings in India

Globally, buildings consume nearly half of the total energy produced, and consequently responsible for a large share of CO2 emissions. A building's life cycle energy (LCE) comprises its embodied energy (EE) and operational energy (OE). The building design, prevalent climatic conditions and occupant behaviour primarily determines its LCE. Thus, for the identification of appropriate emission-reduction strategies, studies into building LCE are crucial. While OE reflects the energy utilized in operating a, EE comprises the initial capital energy involved in its construction (material and burden associated with material consumption in buildings. Assessment of EE and OE in buildings is crucial towards identifying appropriate design and operational strategies for reduction of the building's life cycle energy. This paper discusses EE and OE assessment of a few residential buildings in different climatic locations in India. The study shows that share of OE and EE in LCE greatly depends upon the types of materials used in construction and extent of space conditioning adopted. In some cases EE can exceed life cycle OE. Buildings with reinforced concrete frame and monolithic reinforced concrete walls have very high EE. (C) 2015 Elsevier B.V. All rights reserved.

A dendrimer facilitates resonance energy transfer between hydrophobic aromatic guest molecules in water

A water soluble third generation poly(alkyl aryl ether) dendrimer was examined for its ability to solubilize hydrophobic polyaromatic molecules in water and facilitate non-radiative resonance energy transfer between them. One to two orders of magnitude higher aqueous solubilities of pyrene (PY), perylene (PE), acridine yellow (AY) and acridine orange (AO) were observed in presence of a defined concentration of the dendrimer. A reduction in the quantum yield of the donor PY* emission and a partial decrease in lifetime of the donor excited state revealed the occurrence of energy transfer from dendrimer solubilized excited PY to ground state PE molecules, both present within a dendrimer. The energy transfer efficiency was estimated to be similar to 61%. A cascade resonance energy transfer in a three component system, PY*-to-PE-to-AY and PY*-to-PE-to-AO, was demonstrated through incorporation of AY or AO in the two component PY-PE system. In the three-component system, excitation of PY resulted in emission from AY or AO via a cascade energy transfer process. Careful choice of dye molecules with good spectral overlap and the employment of dendrimer as the medium enabled us to expand absorption-emission wavelengths, from similar to 330 nm to similar to 600 nm in aqueous solution. (C) 2015 Elsevier B.V. All rights reserved.

Life-cycle analysis of energy and greenhouse gas emissions of automotive fuels in India: Part 2-Well-to-wheels analysis

In this second of the two-part study, the results of the Tank-to-Wheels study reported in the first part are combined with Well-to-Tank results in this paper to provide a comprehensive Well-to-Wheels energy consumption and greenhouse gas emissions evaluation of automotive fuels in India. The results indicate that liquid fuels derived from petroleum have Well-to-Tank efficiencies in the range of 75-85% with liquefied petroleum gas being the most efficient fuel in the Well-to-Tank stage with 85% efficiency. Electricity has the lowest efficiency of 20% which is mainly attributed due to its dependence on coal and 25.4% losses during transmission and distribution. The complete Well-to-Wheels results show diesel vehicles to be the most efficient among all configurations, specifically the diesel-powered split hybrid electric vehicle. Hydrogen engine configurations are the least efficient due to low efficiency of production of hydrogen from natural gas. Hybridizing electric vehicles reduces the Well-to-Wheels greenhouse gas emissions substantially with split hybrid configuration being the most efficient. Electric vehicles do not offer any significant improvement over gasoline-powered configurations; however a shift towards renewable sources for power generation and reduction in losses during transmission and distribution can make it a feasible option in the future. (C) 2015 Elsevier Ltd. All rights reserved.

Time-Instant Sampling Based Encoding of Time-Varying Acoustic Spectrum

The inner ear has been shown to characterize an acoustic stimuli by transducing fluid motion in the inner ear to mechanical bending of stereocilia on the inner hair cells (IHCs). The excitation motion/energy transferred to an IHC is dependent on the frequency spectrum of the acoustic stimuli, and the spatial location of the IHC along the length of the basilar membrane (BM). Subsequently, the afferent auditory nerve fiber (ANF) bundle samples the encoded waveform in the IHCs by synapsing with them. In this work we focus on sampling of information by afferent ANFs from the IHCs, and show computationally that sampling at specific time instants is sufficient for decoding of time-varying acoustic spectrum embedded in the acoustic stimuli. The approach is based on sampling the signal at its zero-crossings and higher-order derivative zero-crossings. We show results of the approach on time-varying acoustic spectrum estimation from cricket call signal recording. The framework gives a time-domain and non-spatial processing perspective to auditory signal processing. The approach works on the full band signal, and is devoid of modeling any bandpass filtering mimicking the BM action. Instead, we motivate the approach from the perspective of event-triggered sampling by afferent ANFs on the stimuli encoded in the IHCs. Though the approach gives acoustic spectrum estimation but it is shallow on its complete understanding for plausible bio-mechanical replication with current mammalian auditory mechanics insights.

Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals

Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.

Distributed Nonparametric Sequential Spectrum Sensing under Electromagnetic Interference

We propose a distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain and the EMI is not known. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the Fe. Modification of a recently developed random walk test is selected for the local nodes for energy detection as well as at the fusion centre for mean detection. We show via simulations and analysis that the nonparametric distributed algorithm developed performs well in the presence of fading, EMI and outliers. The algorithm is iterative in nature making the computation and storage requirements minimal.