Efficiency enhancement in DSSC using metal nanoparticles: A size dependent study

The photoelectrode of Eosin-Y sensitised DSSC was modified by incorporating Au-nanoparticles to enhance the power conversion efficiency via scattering from surface plasmon polaritons. Size dependence of Au nanoparticle on conversion efficiency was performed in DSSC for the first time by varying the particle size from 20 to 94 nm. It was found that, the conversion efficiency is highly dependent on the size of the Au nanoparticles. For larger particles (>50 nm), the efficiency was found to be increased due to constructive interference between the transmitted and scattered waves from the Au nanoparticle while for smaller particles, the efficiency decreases due to destructive interference. Also a reduction in the V-oc was observed in general, due to the negative shifting of the TiO2 Fermi level on the adsorption of Au nanoparticle. This shift was negligible for larger particles. When 94 nm size particles were employed the conversion efficiency was doubled from 0.74% to 1.52%. This study points towards the application of the scattering effect of metal nanoparticle to enhance the conversion efficiency in DSSCs. (C) 2011 Elsevier Ltd. All rights reserved.

Enhanced efficiency of tri-layered dye solar cells with hydrothermally synthesized titania nanotubes as light scattering outer layer

In the present study dye sensitized solar cells (DSSCs) have been fabricated with a tri-layer photo anode consisting of hydrothermally prepared titania nano tubes (TNT) having a diameter of 9-10 nm and length of several micrometers as outer layer, P25 TiO2 powder as transparent light absorbing middle layer and a compact TiO2 inner layer to improve the adhesion of different layers on a transparent conducting oxide coated substrate. In comparison to cells fabricated using TNTs or P25 alone, the tri-layer DSSCs exhibit an enhanced efficiency of 7.15% with a current density of 17.12 mA cm(-2) under AM 1.5 illumination. The enhancement is attributed to the light scattering generated by TNTs aggregates, reduction in electron transport resistance at the TiO2/dye/electrolyte interface and an improvement in electron life-time. (c) 2012 Elsevier B.V. All rights reserved.

Loading of single-walled carbon nanotubes in cationic cholesterol suspensions significantly improves gene transfection efficiency in serum

We present here a series of cholesterol based cationic lipid suspensions that solubilize single-walled carbon nanotubes (SWCNT) efficiently in water. Each cationic lipid formulation was characterized in terms of their energy minimized molecular structures, bilayer widths of the aggregates based on X-ray diffraction. Then these aggregates were investigated pertaining to their DNA binding and release efficiency, effect of CNT inclusion on the stability of cationic cholesterol lipid-DNA complexes, Zeta potential values and changes in the chiro-optical property of DNA, effect on Raman spectral shift and changes in morphology by SEM and AFM. Each cationic lipid formulation was optimized for the amount of SWCNT solubilized in water, lipid-DNA ratio, amount of the plasmid DNA that can be transfected and the effect on the cellular toxicity. The resulting SWCNT-lipid formulations were then used for in vitro transfection of pEGFP-C3 in A549 (human alveolar basal epithelial) cells and HeLa (human cervical cancer) cells. Advantageously, the CNT-loaded formulations confer an excellent transfection efficiency even in high percentages of blood serum and showed significantly better gene transfer efficiency compared to one of the potent, well-known commercial transfection reagent, Lipofectamine2000.

Improvements in Efficiency of Surface Potential Computation for Independent DG MOSFET

A robust numerical solution of the input voltage equations (IVEs) for the independent-double-gate metal-oxide-semiconductor field-effect transistor requires root bracketing methods (RBMs) instead of the commonly used Newton-Raphson (NR) technique due to the presence of nonremovable discontinuity and singularity. In this brief, we do an exhaustive study of the different RBMs available in the literature and propose a single derivative-free RBM that could be applied to both trigonometric and hyperbolic IVEs and offers faster convergence than the earlier proposed hybrid NR-Ridders algorithm. We also propose some adjustments to the solution space for the trigonometric IVE that leads to a further reduction of the computation time. The improvement of computational efficiency is demonstrated to be about 60% for trigonometric IVE and about 15% for hyperbolic IVE, by implementing the proposed algorithm in a commercial circuit simulator through the Verilog-A interface and simulating a variety of circuit blocks such as ring oscillator, ripple adder, and twisted ring counter.

Mesh Simplification Based on Edge Collapsing Could Improve Computational Efficiency in Near Infrared Optical Tomographic Imaging

The diffusion equation-based modeling of near infrared light propagation in tissue is achieved by using finite-element mesh for imaging real-tissue types, such as breast and brain. The finite-element mesh size (number of nodes) dictates the parameter space in the optical tomographic imaging. Most commonly used finite-element meshing algorithms do not provide the flexibility of distinct nodal spacing in different regions of imaging domain to take the sensitivity of the problem into consideration. This study aims to present a computationally efficient mesh simplification method that can be used as a preprocessing step to iterative image reconstruction, where the finite-element mesh is simplified by using an edge collapsing algorithm to reduce the parameter space at regions where the sensitivity of the problem is relatively low. It is shown, using simulations and experimental phantom data for simple meshes/domains, that a significant reduction in parameter space could be achieved without compromising on the reconstructed image quality. The maximum errors observed by using the simplified meshes were less than 0.27% in the forward problem and 5% for inverse problem.

Quantum heat engines: A thermodynamic analysis of power and efficiency

We show that the operation and the output power of a quantum heat engine that converts incoherent thermal energy into coherent cavity photons can be optimized by manipulating quantum coherences. The gain or loss in the efficiency at maximum power depends on the details of the output power optimization. Quantum effects tend to enhance the output power and the efficiency as the photon occupation in the cavity is decreased.

Low temperature growth of SnO2 nanowires by electron beam evaporation and their application in UV light detection

For the first time, high quality tin oxide (SnO2) nanowires have been synthesized at a low substrate temperature of 450 degrees C via vapor-liquid-solid mechanism using an electron beam evaporation technique. The grown nanowires have shown length of 2-4 mu m and diameter of 20-60 nm. High resolution transmission electron microscope studies on the grown nanowires have shown the single crystalline nature of the SnO2 nanowires. We investigated the effect of growth temperature and oxygen partial pressure on SnO2 nanowires growth. Variation of substrate temperature at a constant oxygen partial pressure of 4 x 10(-4) mbar suggested that a temperature equal to or greater than 450 degrees C was the best condition for phase pure SnO2 nanowires growth. The SnO2 nanowires grown on a SiO2 substrate were subjected to UV photo detection. The responsivity and quantum efficiency of SnO2 NWs photo detector (at 10V applied bias) was 12 A/W and 45, respectively, for 12 mu W/cm(2) UV lamp (330 nm) intensity on the photo detector.

Dependence of electron recombination time and light to electricity conversion efficiency on shape of the nanocrystal light sensitizer

Tunability of electron recombination time and light to electricity conversion efficiency to superior values in semiconductor sensitized solar cells via optimized design of nanocrystal light sensitizer shape is discussed here.

FIELD EMISSION EFFICIENCY OF A CARBON NANOTUBE ARRAY UNDER PARASITIC NONLINEARITIES

Carbon Nanotubes (CNTs) grown on substrates are potential electron sources in field emission applications. Several studies have reported the use of CNTs in field emission devices, including field emission displays, X-ray tube, electron microscopes, cathode-ray lamps, etc. Also, in recent years, conventional cold field emission cathodes have been realized in micro-fabricated arrays for medical X-ray imaging. CNTbased field emission cathode devices have potential applications in a variety of industrial and medical applications, including cancer treatment. Field emission performance of a single isolated CNT is found to be remarkable, but the situation becomes complex when an array of CNTs is used. At the same time, use of arrays of CNTs is practical and economical. Indeed, such arrays on cathode substrates can be grown easily and their collective dynamics can be utilized in a statistical sense such that the average emission intensity is high enough and the collective dynamics lead to longer emission life. The authors in their previous publications had proposed a novel approach to obtain stabilized field emission current from a stacked CNT array of pointed height distribution. A mesoscopic modeling technique was employed, which took into account electro-mechanical forces in the CNTs, as well as transport of conduction electron coupled with electron phonon induced heat generation from the CNT tips. The reported analysis of pointed arrangements of the array showed that the current density distribution was greatly localized in the middle of the array, the scatter due to electrodynamic force field was minimized, and the temperature transients were much smaller compared to those in an array with random height distribution. In the present paper we develop a method to compute the emission efficiency of the CNT array in terms of the amount of electrons hitting the anode surface using trajectory calculations. Effects of secondary electron emission and parasitic capacitive nonlinearity on the current-voltage signals are accounted. Field emission efficiency of a stacked CNT array with various pointed height distributions are compared to that of arrays with random and uniform height distributions. Effect of this parasitic nonlinearity on the emission switch-on voltage is estimated by model based simulation and Monte Carlo method.

Training for Antenna Selection in Time-Varying Channels: Optimal Selection, Energy Allocation, and Energy Efficiency Evaluation

Single receive antenna selection (AS) is a popular method for obtaining diversity benefits without the additional costs of multiple radio receiver chains. Since only one antenna receives at any time, the transmitter sends a pilot multiple times to enable the receiver to estimate the channel gains of its N antennas to the transmitter and select an antenna. In time-varying channels, the channel estimates of different antennas are outdated to different extents. We analyze the symbol error probability (SEP) in time-varying channels of the N-pilot and (N+1)-pilot AS training schemes. In the former, the transmitter sends one pilot for each receive antenna. In the latter, the transmitter sends one additional pilot that helps sample the channel fading process of the selected antenna twice. We present several new results about the SEP, optimal energy allocation across pilots and data, and optimal selection rule in time-varying channels for the two schemes. We show that due to the unique nature of AS, the (N+1)-pilot scheme, despite its longer training duration, is much more energy-efficient than the conventional N-pilot scheme. An extension to a practical scenario where all data symbols of a packet are received by the same antenna is also investigated.

Information and Efficiency in the Nervous System-A Synthesis

In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components-like genetic circuits, biochemical cascades, and ion channels, among others-enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode-with almost 20-60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.

Application of the random eigenvalue problem in forced response analysis of a linear stochastic structure

The random eigenvalue problem arises in frequency and mode shape determination for a linear system with uncertainties in structural properties. Among several methods of characterizing this random eigenvalue problem, one computationally fast method that gives good accuracy is a weak formulation using polynomial chaos expansion (PCE). In this method, the eigenvalues and eigenvectors are expanded in PCE, and the residual is minimized by a Galerkin projection. The goals of the current work are (i) to implement this PCE-characterized random eigenvalue problem in the dynamic response calculation under random loading and (ii) to explore the computational advantages and challenges. In the proposed method, the response quantities are also expressed in PCE followed by a Galerkin projection. A numerical comparison with a perturbation method and the Monte Carlo simulation shows that when the loading has a random amplitude but deterministic frequency content, the proposed method gives more accurate results than a first-order perturbation method and a comparable accuracy as the Monte Carlo simulation in a lower computational time. However, as the frequency content of the loading becomes random, or for general random process loadings, the method loses its accuracy and computational efficiency. Issues in implementation, limitations, and further challenges are also addressed.

Enhanced photoabsorption efficiency of incomplete nanoshells

The rather low scattering or extinction efficiency of small nanoparticles, metallic and otherwise, is significantly enhanced when they are adsorbed on a larger core particle. But the photoabsorption by particles with varying surface area fractions on a larger core particle is found to be limited by saturation. It is found that the core-shell particle can have a lower absorption efficiency than a dielectric core with its surface partially nucleated with absorbing particles-an ``incomplete nanoshell'' particle. We have both numerically and experimentally studied the optical efficiencies of titania (TiO2) nucleated in various degrees on silica (SiO2) nanospheres. We show that optimal surface nucleation over cores of appropriate sizes and optical properties will have a direct impact on the applications exploiting the absorption and scattering properties of such composite particles.

The effect of cell size and channel density on neuronal information encoding and energy efficiency

Identifying the determinants of neuronal energy consumption and their relationship to information coding is critical to understanding neuronal function and evolution. Three of the main determinants are cell size, ion channel density, and stimulus statistics. Here we investigate their impact on neuronal energy consumption and information coding by comparing single-compartment spiking neuron models of different sizes with different densities of stochastic voltage-gated Na+ and K+ channels and different statistics of synaptic inputs. The largest compartments have the highest information rates but the lowest energy efficiency for a given voltage-gated ion channel density, and the highest signaling efficiency (bits spike(-1)) for a given firing rate. For a given cell size, our models revealed that the ion channel density that maximizes energy efficiency is lower than that maximizing information rate. Low rates of small synaptic inputs improve energy efficiency but the highest information rates occur with higher rates and larger inputs. These relationships produce a Law of Diminishing Returns that penalizes costly excess information coding capacity, promoting the reduction of cell size, channel density, and input stimuli to the minimum possible, suggesting that the trade-off between energy and information has influenced all aspects of neuronal anatomy and physiology.