Development of highly selective ligands for separations of actinides from lanthanides in the nuclear fuel cycle

This account summarizes recent work by us and others on the development of ligands for the separation of actinides from lanthanides contained in nuclear waste streams in the context of a future European strategy for nuclear waste management. The current status of actinide/lanthanide separations worldwide is briefly discussed, and the synthesis, development, and testing of different classes of heterocyclic soft N- and S-donor ligands in Europe over the last 20 years is presented. This work has led to the current benchmark ligand that displays many of the desirable qualities for industrial use. The improvement of radiolytic stability through ligand design is also discussed.

Can microgrids make a major contribution to UK energy supply?

Almost all the electricity currently produced in the UK is generated as part of a centralised power system designed around large fossil fuel or nuclear power stations. This power system is robust and reliable but the efficiency of power generation is low, resulting in large quantities of waste heat. The principal aim of this paper is to investigate an alternative concept: the energy production by small scale generators in close proximity to the energy users, integrated into microgrids. Microgrids—de-centralised electricity generation combined with on-site production of heat—bear the promise of substantial environmental benefits, brought about by a higher energy efficiency and by facilitating the integration of renewable sources such as photovoltaic arrays or wind turbines. By virtue of good match between generation and load, microgrids have a low impact on the electricity network, despite a potentially significant level of generation by intermittent energy sources. The paper discusses the technical and economic issues associated with this novel concept, giving an overview of the generator technologies, the current regulatory framework in the UK, and the barriers that have to be overcome if microgrids are to make a major contribution to the UK energy supply. The focus of this study is a microgrid of domestic users powered by small Combined Heat and Power generators and photovoltaics. Focusing on the energy balance between the generation and load, it is found that the optimum combination of the generators in the microgrid- consisting of around 1.4 kWp PV array per household and 45% household ownership of micro-CHP generators- will maintain energy balance on a yearly basis if supplemented by energy storage of 2.7 kWh per household. We find that there is no fundamental technological reason why microgrids cannot contribute an appreciable part of the UK energy demand. Indeed, an estimate of cost indicates that the microgrids considered in this study would supply electricity at a cost comparable with the present electricity supply if the current support mechanisms for photovoltaics were maintained. Combining photovoltaics and micro-CHP and a small battery requirement gives a microgrid that is independent of the national electricity network. In the short term, this has particular benefits for remote communities but more wide-ranging possibilities open up in the medium to long term. Microgrids could meet the need to replace current generation nuclear and coal fired power stations, greatly reducing the demand on the transmission and distribution network.

Commodity currency: an alternative route to currency union

Proposals have been made for a common currency for East Asia, but the countries preparing to participate need to be in a state of economic convergence. We show that at least six countries of East Asia already satisfy this condition. There also needs to be a mechanism by which the new currency relates to other reserve currencies. We demonstrate that a numéraire could be defined solely from the actual worldwide consumption of food and energy per capita, linked to fiat currencies via world market prices. We show that real resource prices are stable in real terms, and likely to remain so. Furthermore, the link from energy prices to food commodity prices is permanent, arising from energy inputs in agriculture, food processing and distribu-tion. Calibration of currency value using a yardstick such as our SI numéraire offers an unbiased measure of the con-sistently stable cost of subsistence in the face of volatile currency exchange rates. This has the advantage that the par-ticipating countries need only agree to currency governance based on a common standards institution, a much less on-erous form of agreement than would be required in the creation of a common central bank.

Nuclear fuel cycles: interfaces with the environment

The waste materials generated in the nuclear fuel cycle are very varied,ranging from the tailings arising from mining and processing uranium ore, depleted uranium in a range of chemical forms, to a range of process wastes of differing activities and properties. Indeed, the wastes generated are intimately linked to the options selected in operating the nuclear fuel cycle, most obviously to the management of spent fuel. An open fuel cycle implies the disposal of highly radioactive spent fuel, whereas a closed fuel cycle generates a complex array of waste streams. On the other hand, a closed fuel cycle offers options for waste management, for example reduction in highly active waste volume, decreased radiotoxicity, and removal of fissile material. Many technological options have been proposed or explored, and each brings its own particular mix of wastes and environmental challenges.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

A novel alternative to cryopreservation for the short-term storage of stem cells for use in cell therapy using alginate encapsulation

Efficient transport of stem/progenitor cells without affecting their survival and function is a key factor in any practical cell-based therapy. However, the current approach using liquid nitrogen for the transfer of stem cells requires a short delivery time window is technically challenging and financially expensive. The present study aims to use semipermeable alginate hydrogels (crosslinked by strontium) to encapsulate, store, and release stem cells, to replace the conventional cryopreservation method for the transport of therapeutic cells within world-wide distribution time frame. Human mesenchymal stem cell (hMSC) and mouse embryonic stem cells (mESCs) were successfully stored inside alginate hydrogels for 5 days under ambient conditions in an air-tight environment (sealed cryovial). Cell viability, of the cells extracted from alginate gel, gave 74% (mESC) and 80% (hMSC) survival rates, which compared favorably to cryopreservation. More importantly, the subsequent proliferation rate and detection of common stem cell markers (both in mRNA and protein level) from hMSCs and mESCs retrieved from alginate hydrogels were also comparable to (if not better than) results gained following cryopreservation. In conclusion, this new and simple application of alginate hydrogel encapsulation may offer a cheap and robust alternative to cryopreservation for the transport and storage of stem cells for both clinical and research purposes.

Classification and fault detection methods for fuel cell monitoring and quality control

In this paper, various types of fault detection methods for fuel cells are compared. For example, those that use a model based approach or a data driven approach or a combination of the two. The potential advantages and drawbacks of each method are discussed and comparisons between methods are made. In particular, classification algorithms are investigated, which separate a data set into classes or clusters based on some prior knowledge or measure of similarity. In particular, the application of classification methods to vectors of reconstructed currents by magnetic tomography or to vectors of magnetic field measurements directly is explored. Bases are simulated using the finite integration technique (FIT) and regularization techniques are employed to overcome ill-posedness. Fisher's linear discriminant is used to illustrate these concepts. Numerical experiments show that the ill-posedness of the magnetic tomography problem is a part of the classification problem on magnetic field measurements as well. This is independent of the particular working mode of the cell but influenced by the type of faulty behavior that is studied. The numerical results demonstrate the ill-posedness by the exponential decay behavior of the singular values for three examples of fault classes.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.