Self-assembled biomimetic [2Fe2S]-hydrogenase-based photocatalyst for molecular hydrogen evolution

The large-scale production of clean energy is one of the major challenges society is currently facing. Molecular hydrogen is envisaged as a key green fuel for the future, but it becomes a sustainable alternative for classical fuels only if it is also produced in a clean fashion. Here, we report a supramolecular biomimetic approach to form a catalyst that produces molecular hydrogen using light as the energy source. It is composed of an assembly of chromophores to a bis(thiolate)-bridged diiron ([2Fe2S]) based hydrogenase catalyst. The supramolecular building block approach introduced in this article enabled the easy formation of a series of complexes, which are all thoroughly characterized, revealing that the photoactivity of the catalyst assembly strongly depends on its nature. The active species, formed from different complexes, appears to be the [Fe-2(mu-pdt)(CO)(4){PPh2(4-py)}(2)] (3) with 2 different types of porphyrins (5a and 5b) coordinated to it. The modular supramolecular approach was important in this study as with a limited number of building blocks several different complexes were generated.

A microblock ionomer in proton exchange membrane electrolysis for the production of high purity hydrogen

Proton exchange membranes (PEM’s) are currently under investigation for membrane water electrolysis (PEMWE) to deliver efficient production of the high purity hydrogen needed to supply emerging clean-energy technologies such as hydrogen fuel cells. The microblock aromatic ionomer described in this work achieves high mechanical strength in an aqueous environment as a result of its designed, biphasic morphology and displays many of the qualities required in a PEM. The new ionomer membrane thus shows good proton conductivity (63 mS cm−1 at 80 °C and 100% RH), while retaining mechanical integrity under high temperature, hydrated conditions. Testing in electrolysis has shown good energy efficiency (1.67 V at 1 A cm−2 and 80 °C, corresponding to 4 kWh/Nm3 of H2), making this ionomer a potential candidate for commercial application in PEMWE.

Model car-exhaust catalyst studied by TPD and TP-RAIRS: Surface reactions of NO on clean and O-covered Ir{100}

The adsorption of NO on Ir{100} has been studied as a function of NO coverage and temperature using temperature programmed reflection absorption infrared spectroscopy (TP-RAIRS), low energy electron diffraction (LEED) and temperature programmed desorption (TPD). After saturating the clean (1 x 5)-reconstructed surface with NO at 95 K. two N-2, desorption peaks are observed upon heating. The first N-2 peak at 346 K results from the decomposition of bridge-bonded NO, and the second at 475 K from the decomposition of atop-bonded NO molecules. NO decomposition is proposed to be the rate limiting step for both N-2 desorption states. For high NO coverages on the (1 x 5) surface, the narrow width of the first N-2 desorption peak is indicative of an autocatalytic process for which the parallel formation of N2O appears to be the crucial step. When NO is adsorbed on the metastable unreconstructed (1 x 1) phase of clean Ir{100} N-2 desorption starts at lower temperatures, indicating that this surface modification is more reactive. When a high coverage of oxygen, near 0.5 ML, is pre-adsorbed on the surface, the decomposition of NO is inhibited and mainly desorption of intact NO is observed.

Governing the clean development mechanism: global rhetoric versus local realities in carbon sequestration projects

Global agreements have proliferated in the past ten years. One of these is the Kyoto Protocol, which contains provisions for emissions reductions by trading carbon through the Clean Development Mechanism (CDM). The CDM is a market-based instrument that allows companies in Annex I countries to offset their greenhouse gas emissions through energy and tree offset projects in the global South. I set out to examine the governance challenges posed by the institutional design of carbon sequestration projects under the CDM. I examine three global narratives associated with the design of CDM forest projects, specifically North – South knowledge politics, green developmentalism, and community participation, and subsequently assess how these narratives match with local practices in two projects in Latin America. Findings suggest that governance problems are operating at multiple levels and that the rhetoric of global carbon actors often asserts these schemes in one light, while the rhetoric of those who are immediately involved locally may be different. I also stress the alarmist’s discourse that blames local people for the problems of environmental change. The case studies illustrate the need for vertical communication and interaction and nested governance arrangements as well as horizontal arrangements. I conclude that the global framing of forests as offsets requires better integration of local relationships to forests and their management and more effective institutions at multiple levels to link the very local to the very large scale when dealing with carbon sequestration in the CDM.

A new error measure for forecasts of household-level, high resolution electrical energy consumption

As low carbon technologies become more pervasive, distribution network operators are looking to support the expected changes in the demands on the low voltage networks through the smarter control of storage devices. Accurate forecasts of demand at the single household-level, or of small aggregations of households, can improve the peak demand reduction brought about through such devices by helping to plan the appropriate charging and discharging cycles. However, before such methods can be developed, validation measures are required which can assess the accuracy and usefulness of forecasts of volatile and noisy household-level demand. In this paper we introduce a new forecast verification error measure that reduces the so called “double penalty” effect, incurred by forecasts whose features are displaced in space or time, compared to traditional point-wise metrics, such as Mean Absolute Error and p-norms in general. The measure that we propose is based on finding a restricted permutation of the original forecast that minimises the point wise error, according to a given metric. We illustrate the advantages of our error measure using half-hourly domestic household electrical energy usage data recorded by smart meters and discuss the effect of the permutation restriction.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.