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Projects: Projects for Investigator
Reference Number GR/R82074/01
Title Predictive Modelling and Mechanochemical Processing of New Mg-Based Hydrogen Storage Materials
Status Completed
Energy Categories Hydrogen and Fuel Cells(Hydrogen, Hydrogen storage) 100%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Professor ZX (Zheng Xiao ) Guo
No email address given
University College London
Award Type Standard
Funding Source EPSRC
Start Date 01 December 2002
End Date 30 November 2005
Duration 36 months
Total Grant Value £210,456
Industrial Sectors Energy; No relevance to Underpinning Sectors
Region London
Programme Materials, Mechanical and Medical Eng, Process Environment and Sustainability
Investigators Principal Investigator Professor ZX (Zheng Xiao ) Guo , Chemistry, University College London (99.999%)
  Other Investigator Dr Y Song , Engineering and Materials Science, Queen Mary, University of London (0.001%)
  Industrial Collaborator Project Contact , QinetiQ Ltd (0.000%)
Project Contact , DSTL - Defence Science and Technology Laboratory (0.000%)
Web Site
Abstract Limited fossil fuels C02 induced global warming, and vehicle-related air pollution are causing a worldwide drive to develop alternative/clean energyresources, much of which depending on the safe storage of hydrogen in a solid of sufficient capacity and high charging/discharging kinetics at low temperature /pressure. Many experimental investigations of H-storage systems have been carried out in the past, but mainly by trial and error, and thesystems are unsatisfactory for current industrial requirements. Mg-based hydrides hold great promise among various candidates, but suffer from low kinetics and high dissociation temperature. Here, the proposal aims to clarify scientifically the effects of chemical alloying and processing on H storagecharacteristics of Mg-based materials using both artificial neural network (statistic) and electronic structural (deterministic) simulations; and to develop technologically a mechanochemical powder metallurgy route to synthesise finely/nano- structuredhydrogen storage materials of desirable phase composition, H-storage capacity, dissociation temperature, kinetics, and cycle life, guided by model predictions. A range of processing and characterisation techniques, including a novel powder coating method, will be used to process, evaluate and optimise the storage materials
Publications (none)
Final Report (none)
Added to Database 01/01/07