Science & Technology, Australia (Commonwealth Union) – Hydrogen fuel cells are devices that generate electricity by combining hydrogen and oxygen to produce water, with the production of electricity as a byproduct. Fuel cells work by separating positively charged hydrogen ions (protons) from negatively charged electrons. The electrons flow through an external circuit to create an electrical current, while the protons pass through a membrane and combine with oxygen to produce water. The present global financial crisis with rising fuel costs and concern for the environment has put much focus alternative energy options such as Hydrogen fuel cells.
Scientists from the University of New South Wales (UNSW), have formed an algorithm to significantly improve images of hydrogen fuel cells, possibly having an application in medical scanning in future.
The algorithm forms high-resolution modelled images with the use of lower-resolution micro-X-ray computerized tomography (CT).
The new method has been tested with individual hydrogen fuel cells for precise modeling of the interior with accuracy and possibly enhanced the efficiency of them.
The researchers however indicated that it may also be utilized in future on human X-rays to provide medical professionals an enhanced knowledge of minute cellular structures within the body, that may pave the way for better and speedier diagnosis of a wide variety of diseases.
Researchers consisting of Professor Ryan Armstrong, Professor Peyman Mostaghimi, Dr Ying Da Wang, and Kunning Tang from the School of Mineral and Energy Resources Engineering along with Professor Chuan Zhao and Dr Quentin Meyer from the School of Chemistry, created the algorithm for the enhancement of our knowledge of what is taking place inside a Proton Exchange Membrane Fuel Cell (PEMFC).
The PEMFCs apply hydrogen fuel for the production of electricity and are a quiet, as well as clean energy source that may power homes, vehicles, and industries.
These fuel cells convert the hydrogen, through an electrochemical method, into electricity with the only by-product of the reaction resulting in pure water.
But, the PEMFCs may turn out to be inefficient if the water is unable to properly move out of the cell and if ends up overflowing the system. Until the present, it was extremely difficult for engineers to know the accurate ways in which water drains, or indeed pools, inside the fuel cells as a result of their lesser size and extremely complex structures.
The UNSW researchers, produced solution paves the way for a broad understanding in forming a detailed 3D model by utilizing a lower-resolution X-ray image of the cell, as they extrapolate data from an accompanying high-res scan of a tiny sub-section of it.
“One of the reasons this research is so novel is that we are pushing the limit of what can be produced from imaging,” says Professor Armstrong.
“It is very typical that when you use a piece of hardware, whether it’s a microscope or a CT scanner, the resolution of an image gets worse the more you zoom out.”
“Our machine learning technique resolves that problem, and the methodology is broadly applicable where any imaging is taking place, such as medical applications, or the oil and gas industry, or chemical engineering.”
“We have done preliminary super-resolution work with radiologists previously and we could surmise that by obtaining a higher resolution image from a larger field of view that it may be possible to diagnose diseases, such as tumour cells, earlier, when they are smaller.”
Dr Wang indicated that in the study their super-resolution algorithm, referred to as DualEDSR, enhances the field of view by roughly 100 times when contrasted to the high-res image.
The ability to magnify is likely to further enhance images with a clearer picture is likely to be of use across many fields as it can further improve diagnosis of many diseases by detecting previously undetectable cells and molecules, as well as magnify many procedures in greater detail.
