Projects
Projects: Projects for Investigator |
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| Reference Number | EP/V036106/1 | |
| Title | EPSRC Core Equipment for Aston University | |
| Status | Completed | |
| Energy Categories | Not Energy Related 90%; Energy Efficiency(Industry) 10%; |
|
| Research Types | Equipment 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Chemistry) 50%; PHYSICAL SCIENCES AND MATHEMATICS (Physics) 50%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Professor SO Green Research and Knowledge Exchange Aston University |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 November 2020 | |
| End Date | 30 April 2022 | |
| Duration | 18 months | |
| Total Grant Value | £390,000 | |
| Industrial Sectors | Chemicals; Information Technologies | |
| Region | West Midlands | |
| Programme | NC : Infrastructure | |
| Investigators | Principal Investigator | Professor SO Green , Research and Knowledge Exchange, Aston University (99.999%) |
| Other Investigator | Prof P (Patricia ) Thornley , Mechanical, Aerospace and Civil Engineering, University of Manchester (0.001%) |
|
| Web Site | ||
| Objectives | ||
| Abstract | The college of engineering carries out world-leading research across 3 interdisciplinary institutes/groups:EBRI (Energy and Bioproducts Research Institute )AIPT (Aston Institute of Photonic Technologies)ASTUTE (Aston Centre for Urban Technology and the Environment) The success and impact of these institutes is critically dependent on their access to state-of-the art equipment to deliver internationally competitive research. Investment in a Microactivity Effi at EBRI will allow us to analyze and develop innovative chemcial processes that deliver much-needed industrial products with lower energy, water, carbon and resource footprints. Investment in a dual comb laser at AIPT will allow more accurate measuremetns of a variety of liquid and chemcial compounds, including atmospheric pollutants harmful to health. The procurement of a machine learning server will allow our researchers to process vastly increased quantities of data more quickly and effcieintly to deliver accurate and meaningful results in a timescale useful to industry, policy makers and other researchers | |
| Publications | (none) |
|
| Final Report | (none) |
|
| Added to Database | 20/09/21 | |
