Copper nanoparticles on graphene support: An efficient photocatalyst for coupling of nitroaromatics in visible light

Copper is a low-cost plasmonic metal. Efficient photocatalysts of copper nanoparticles on graphene support are successfully developed for controllably catalyzing the coupling reactions of aromatic nitro compounds to the corresponding azoxy or azo compounds under visible-light irradiation. The coupling of nitrobenzene produces azoxybenzene with a yield of 90 % at 60 °C, but azobenzene with a yield of 96 % at 90 °C. When irradiated with natural sunlight (mean light intensity of 0.044 W cm−2) at about 35 °C, 70 % of the nitrobenzene is converted and 57 % of the product is azobenzene. The electrons of the copper nanoparticles gain the energy of the incident light through a localized surface plasmon resonance effect and photoexcitation of the bound electrons. The excited energetic electrons at the surface of the copper nanoparticles facilitate the cleavage of the NO bonds in the aromatic nitro compounds. Hence, the catalyzed coupling reaction can proceed under light irradiation and moderate conditions. This study provides a green photocatalytic route for the production of azo compounds and highlights a potential application for graphene.

Efficient photocatalytic Suzuki cross-coupling reactions on Au-Pd alloy nanoparticles under visible light irradiation

We report herein highly efficient photocatalysts comprising supported nanoparticles (NPs) of gold (Au) and palladium (Pd) alloys, which utilize visible light to catalyse the Suzuki cross-coupling reactions at ambient temperature. The alloy NPs strongly absorb visible light, energizing the conduction electrons of NPs which produce highly energetic electrons at the surface sites. The surface of the energized NPs activates the substrates and these particles exhibit good activity on a range of typical Suzuki reaction combinations. The photocatalytic efficiencies strongly depend on the Au:Pd ratio of the alloy NPs, irradiation light intensity and wavelength. The results show that the alloy nanoparticles efficiently couple thermal and photonic energy sources to drive Suzuki reactions. Results of the density functional theory (DFT) calculations indicate that transfer of the light-excited electrons from the nanoparticle surface to the reactant molecules adsorbed on the nanoparticle surface activates the reactants. The knowledge acquired in this study may inspire further studies of new efficient photocatalysts and a wide range of organic syntheses driven by sunlight.

Energy band engineering of complex metal oxides

This doctoral studies focused on the development of new materials for efficient use of solar energy for environmental applications. The research investigated the engineering of the band gap of semiconductor materials to design and optimise visible-light-sensitive photocatalysts. Experimental studies have been combined with computational simulation in order to develop predictive tools for a systematic understanding and design on the crystal and energy band structures of multi-component metal oxides.

Solar powered high voltage energization for vehicular exhaust cleaning: A step towards possible retrofitting in vehicles

This paper proposes a novel way of generating high voltage for electric discharge plasma in controlling NOx emission in diesel engine exhaust. A solar powered high frequency electric discharge topology has been suggested that will improve the size and specific energy density required when compared to the traditional repetitive pulse or 50 Hz AC energization. This methodology has been designed, fabricated and experimentally verified by conducting studies on real diesel engine exhaust.

Energy-dependence of nitrate reductase induction in Candida-Utilis

The requirement of a suitable energy source during the induced synthesis of nitrate reductase in Image was investigated. The levels of nitrate reductase induced were shown to be energy-dependent, and to vary in response to the type of carbon source provided. Glycerol, fructose, ethanol, glucose, and sucrose served as efficient energy sources. Growth rate of the yeast and the induced level of nitrate reductase were dependent on the ratio of carbon to nitrogen in the induction medium, and ratio of 2 being optimal. Induction of nitrate reductase was inhibited by uncouplers, 2,4-dinitrophenol (DNP), dicumarol and carbonyl cyanide Candida-Utilis -trifluoromethoxy phenyl hydrazone (CCCP), and by cyanide and azide, indicating an absolute energy-dependency. The facilitation of induction of a high level of nitrate reductase by exogenously added ATP as sole source of energy confirmed the obligate requirement of ATP for the synthesis of nitrate reductase in Candida-Utilis.

Metallo crown porphyrins as ionophores-metal dependent uncoupling of mitochondrial energy potential

Porphyrins appended with crown ether moieties function as efficient uncouplesrs of oxidative phorphorylation in rat liver mitochondria. Permeation of these highly organized porphyrins decrease the respiratory coefficient index (RCI) values. Lowering of the RCI values parallels the number of K+ chelating crown ether groups attached to the porphyrins. The inhibitory effect upon the oxidative phorphorylation reaction depends on the nature of divalent metal ions, VO, Co, Cu and Zn in the porphyrin cavity and related to their relative tendency to complex intracellular K+ ions.

A design approach to innovation in the Australian energy industry

Electricity businesses across Australia are facing many market disruptions, such as the increasing demand from the rapid uptake of domestic air conditioners and the contrasting problematic generation from solar power connections to the grid. In this context, the opportunity to proactively leverage forthcoming technological advances in battery storage and electric vehicles to address the steeply rising cost of electricity supply has emerged. This research explores a design approach to support a business to navigate such disruptions in the current market.This study examines a design-led approach to innovation conducted over a ten month action research study within a large, risk-averse firm in the Australian energy sector. This article presents results describing a current foresight gap within the business; the response of the business to using design-led innovation to address this issue; and the tools, approaches and processes used. The business responses indicate their perception of the value of qualitative customer engagement as a path to addressing, and potentially benefiting from, disruptive innovation. It is anticipated that these results will further business model development within the company, and assist in leveraging disruptive innovations for this industry participant, thus limiting future increases in the cost of electricity supply for customers in Australia.

Metallo crown porphyrins as ionophores-metal dependent uncoupling of mitochondrial energy potential

Porphyrins appended with crown ether moieties function as efficient uncouplesrs of oxidative phorphorylation in rat liver mitochondria. Permeation of these highly organized porphyrins decrease the respiratory coefficient index (RCI) values. Lowering of the RCI values parallels the number of K+ chelating crown ether groups attached to the porphyrins. The inhibitory effect upon the oxidative phorphorylation reaction depends on the nature of divalent metal ions, VO, Co, Cu and Zn in the porphyrin cavity and related to their relative tendency to complex intracellular K+ ions.

Efficient eucalypt cell wall deconstruction and conversion for sustainable lignocellulosic biofuels

In order to meet the world’s growing energy demand and reduce the impact of greenhouse gas emissions resulting from fossil fuel combustion, renewable plant-based feedstocks for biofuel production must be considered. The first-generation biofuels, derived from starches of edible feedstocks, such as corn, create competition between food and fuel resources, both for the crop itself and the land on which it is grown. As such, biofuel synthesized from non-edible plant biomass (lignocellulose) generated on marginal agricultural land will help to alleviate this competition. Eucalypts, the broadly defined taxa encompassing over 900 species of Eucalyptus, Corymbia, and Angophora are the most widely planted hardwood tree in the world, harvested mainly for timber, pulp and paper, and biomaterial products. More recently, due to their exceptional growth rate and amenability to grow under a wide range of environmental conditions, eucalypts are a leading option for the development of a sustainable lignocellulosic biofuels. However, efficient conversion of woody biomass into fermentable monomeric sugars is largely dependent on pretreatment of the cell wall, whose formation and complexity lend itself toward natural recalcitrance against its efficient deconstruction. A greater understanding of this complexity within the context of various pretreatments will allow the design of new and effective deconstruction processes for bioenergy production. In this review, we present the various pretreatment options for eucalypts, including research into understanding structure and formation of the eucalypt cell wall.

Efficient probabilistic contingency analysis through a stability measure considering wind perturbation

The integration of stochastic wind power has accentuated a challenge for power system stability assessment. Since the power system is a time-variant system under wind generation fluctuations, pure time-domain simulations are difficult to provide real-time stability assessment. As a result, the worst-case scenario is simulated to give a very conservative assessment of system transient stability. In this study, a probabilistic contingency analysis through a stability measure method is proposed to provide a less conservative contingency analysis which covers 5-min wind fluctuations and a successive fault. This probabilistic approach would estimate the transfer limit of a critical line for a given fault with stochastic wind generation and active control devices in a multi-machine system. This approach achieves a lower computation cost and improved accuracy using a new stability measure and polynomial interpolation, and is feasible for online contingency analysis.

A Search for Energy Minimized Sequences of Proteins

In this paper, we present numerical evidence that supports the notion of minimization in the sequence space of proteins for a target conformation. We use the conformations of the real proteins in the Protein Data Bank (PDB) and present computationally efficient methods to identify the sequences with minimum energy. We use edge-weighted connectivity graph for ranking the residue sites with reduced amino acid alphabet and then use continuous optimization to obtain the energy-minimizing sequences. Our methods enable the computation of a lower bound as well as a tight upper bound for the energy of a given conformation. We validate our results by using three different inter-residue energy matrices for five proteins from protein data bank (PDB), and by comparing our energy-minimizing sequences with 80 million diverse sequences that are generated based on different considerations in each case. When we submitted some of our chosen energy-minimizing sequences to Basic Local Alignment Search Tool (BLAST), we obtained some sequences from non-redundant protein sequence database that are similar to ours with an E-value of the order of 10(-7). In summary, we conclude that proteins show a trend towards minimizing energy in the sequence space but do not seem to adopt the global energy-minimizing sequence. The reason for this could be either that the existing energy matrices are not able to accurately represent the inter-residue interactions in the context of the protein environment or that Nature does not push the optimization in the sequence space, once it is able to perform the function.

A Nonlinear Approach for Re-entry Guidance of Reusable Launch Vehicles Using Model Predictive Static Programming

A nonlinear suboptimal guidance scheme is developed for the reentry phase of the reusable launch vehicles. A recently developed methodology, named as model predictive static programming (MPSP), is implemented which combines the philosophies of nonlinear model predictive control theory and approximate dynamic programming. This technique provides a finite time nonlinear suboptimal guidance law which leads to a rapid solution of the guidance history update. It does not have to suffer from computational difficulties and can be implemented online. The system dynamics is propagated through the flight corridor to the end of the reentry phase considering energy as independent variable and angle of attack as the active control variable. All the terminal constraints are satisfied. Among the path constraints, the normal load is found to be very constrictive. Hence, an extra effort has been made to keep the normal load within a specified limit and monitoring its sensitivity to the perturbation.

Single atom (Pd/Pt) supported on graphitic carbon nitride as efficient photocatalyst for visible-light reduction of carbon dioxide

Reducing carbon dioxide (CO2) to hydrocarbon fuel with solar energy is significant for high-density solar energy storage and carbon balance. In this work, single palladium/platinum (Pd/Pt) atoms supported on graphitic carbon nitride (g-C3N4), i.e. Pd/g-C3N4 and Pt/g-C3N4, acting as photocatalysts for CO2 reduction were investigated by density function theory (DFT) calcu-lations for the first time. During CO2 reduction, the individual metal atoms function as the active sites, while g-C3N4 provides the source of hydrogen (H*) from hydrogen evolution reaction. The complete, as-designed photocatalysts exhibit excellent activity in CO2 reduction. HCOOH is the preferred product of CO2 reduction on the Pd/g-C3N4 catalyst with a rate-determining barrier of 0.66 eV, while the Pt/g-C3N4 catalyst prefers to reduce CO2 to CH4 with a rate-determining barrier of 1.16 eV. In addition, depositing atom catalysts on g-C3N4 significantly enhances the visible light absorption, rendering them ideal for visible light reduction of CO2. Our findings open a new avenue of CO2 reduction for renewable energy supply.

Two-Phase Thermodynamic Model for Efficient and Accurate Absolute Entropy of Water from Molecular Dynamics Simulations

Presented here is the two-phase thermodynamic (2PT) model for the calculation of energy and entropy of molecular fluids from the trajectory of molecular dynamics (MD) simulations. In this method, the density of state (DoS) functions (including the normal modes of translation, rotation, and intramolecular vibration motions) are determined from the Fourier transform of the corresponding velocity autocorrelation functions. A fluidicity parameter (f), extracted from the thermodynamic state of the system derived from the same MD, is used to partition the translation and rotation modes into a diffusive, gas-like component (with 3Nf degrees of freedom) and a nondiffusive, solid-like component. The thermodynamic properties, including the absolute value of entropy, are then obtained by applying quantum statistics to the solid component and applying hard sphere/rigid rotor thermodynamics to the gas component. The 2PT method produces exact thermodynamic properties of the system in two limiting states: the nondiffusive solid state (where the fluidicity is zero) and the ideal gas state (where the fluidicity becomes unity). We examine the 2PT entropy for various water models (F3C, SPC, SPC/E, TIP3P, and TIP4P-Ew) at ambient conditions and find good agreement with literature results obtained based on other simulation techniques. We also validate the entropy of water in the liquid and vapor phases along the vapor-liquid equilibrium curve from the triple point to the critical point. We show that this method produces converged liquid phase entropy in tens of picoseconds, making it an efficient means for extracting thermodynamic properties from MD simulations.

A Wavelet based Teager Energy Operator for Spike Detection in Microelectrode Array Recordings

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the `local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike. In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90%and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.