Unidirectional suppression of hydrogen oxidation on oxidized platinum clusters

Solar-driven water splitting to produce hydrogen may be an ideal solution for global energy and environment issues. Among the various photocatalytic systems, platinum has been widely used to co-catalyse the reduction of protons in water for hydrogen evolution. However, the undesirable hydrogen oxidation reaction can also be readily catalysed by metallic platinum, which limits the solar energy conversion efficiency in artificial photosynthesis. Here we report that the unidirectional suppression of hydrogen oxidation in photocatalytic water splitting can be fulfilled by controlling the valence state of platinum; this platinum-based cocatalyst in a higher oxidation state can act as an efficient hydrogen evolution site while suppressing the undesirable hydrogen back-oxidation. The findings in this work may pave the way for developing other high-efficientcy platinum-based catalysts for photocatalysis, photoelectrochemistry, fuel cells and water-gas shift reactions.

A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells.

Design of an automated solar concentrator for the pyrolysis of scrap rubber

An automated solar reactor system was designed and built to carry out catalytic pyrolysis of scrap rubber tires at 550°C. To maximize solar energy concentration, a two degrees-of-freedom automated sun tracking system was developed and implemented. Both the azimuth and zenith angles were controlled via feedback from six photo-resistors positioned on a Fresnel lens. The pyrolysis of rubber tires was tested with the presence of two types of acidic catalysts, H-beta and H-USY. Additionally, a photoactive TiO<inf>2</inf> catalyst was used and the products were compared in terms of gas yields and composition. The catalysts were characterized by BET analysis and the pyrolysis gases and liquids were analyzed using GC-MS. The oil and gas yields were relatively high with the highest gas yield reaching 32.8% with H-beta catalyst while TiO<inf>2</inf> gave the same results as thermal pyrolysis without any catalyst. In the presence of zeolites, the dominant gasoline-like components in the gas were propene and cyclobutene. The TiO<inf>2</inf> and non-catalytic experiments produced a gas containing gasoline-like products of mainly isoprene (76.4% and 88.4% respectively). As for the liquids they were composed of numerous components spread over a wide distribution of C<inf>10</inf> to C<inf>29</inf> hydrocarbons of naphthalene and cyclohexane/ene derivatives.

Orange Zinc Germanate with Metallic Ge-Ge Bonds as a Chromophore-Like Center for Visible-Light-Driven Water Splitting

The efficiency of solar-energy-conversion devices depends on the absorption region and intensity of the photon collectors. Organic chromophores, which have been widely stabilized on inorganic semiconductors for light trapping, are limited by the interface between the chromophore and semiconductor. Herein we report a novel orange zinc germanate (Zn-Ge-O) with a chromophore-like structure, by which the absorption region can be dramatically expanded. Structural characterizations and theoretical calculations together reveal that the origin of visible-light response can be attributed to the unusual metallic Ge-Ge bonds which act in a similar way to organic chromophores. Benefiting from the enhanced light harvest, the orange Zn-Ge-O demonstrates superior capacity for solar-driven hydrogen production.

A novel TRNSYS type for short-term borehole heat exchanger simulation: B2G model

Models of ground source heat pump (GSHP) systems are used as an aid for the correct design and optimization of the system. For this purpose, it is necessary to develop models which correctly reproduce the dynamic thermal behavior of each component in a short-term basis. Since the borehole heat exchanger (BHE) is one of the main components, special attention should be paid to ensuring a good accuracy on the prediction of the short-term response of the boreholes. The BHE models found in literature which are suitable for short-term simulations usually present high computational costs. In this work, a novel TRNSYS type implementing a borehole-to-ground (B2G) model, developed for modeling the short-term dynamic performance of a BHE with low computational cost, is presented. The model has been validated against experimental data from a GSHP system located at Universitat Politècnica de València, Spain. Validation results show the ability of the model to reproduce the short-term behavior of the borehole, both for a step-test and under normal operating conditions.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

K alpha yields from Ti foils irradiated with ultrashort laser pulses

We have studied the emission of Kalpha radiation from Ti foils irradiated with ultrashort (45 fs) laser pulses. We utilized the fundamental (800 nm) light from a Ti:sapphire laser on bare foils and foils coated with a thin layer of parylene E (CH). The focusing was varied widely to give a range of intensities from approximately 10(15)-10(19) W cm(-2). Our results show a conversion efficiency of laser to Kalpha energy of similar to 10(-4) at tight focus for both types of targets. In addition, the coated targets exhibited strong secondary peaks of conversion at large defocus, which we believe are due to modification of the extent of preformed plasma due to the dielectric nature of the plastic layer. This in turn affects the level of resonance absorption. A simple model of Kalpha production predicts a much higher conversion than seen experimentally and possible reasons for this are discussed.

Ultrafast laser-driven microlens to focus and energy-select mega-electron volt protons

We present a technique for simultaneous focusing and energy selection of high-current, mega-electron volt proton beams With the use of radial, transient electric fields (107 to 1010 volts per meter) triggered on the inner walls of a hollow microcylinder by an intense subpicosecond laser pulse. Because of the transient nature of the focusing fields, the proposed method allows selection of a desired range out of the spectrum of the polyenergetic proton beam. This technique addresses current drawbacks of laser-accelerated proton beams, such as their broad spectrum and divergence at the source.

Laser-driven proton scaling laws and new paths towards energy increase

The past few years have seen remarkable progress in the development of laser-based particle accelerators. The ability to produce ultrabright beams of multi-megaelectronvolt protons routinely has many potential uses from engineering to medicine, but for this potential to be realized substantial improvements in the performances of these devices must be made. Here we show that in the laser-driven accelerator that has been demonstrated experimentally to produce the highest energy protons, scaling laws derived from fluid models and supported by numerical simulations can be used to accurately describe the acceleration of proton beams for a large range of laser and target parameters. This enables us to evaluate the laser parameters needed to produce high-energy and high-quality proton beams of interest for radiography of dense objects or proton therapy of deep-seated tumours.