Researcher at TU Graz demonstrates in Nature Materials that it is possible to combine the high-energy density of batteries with the high-power output of super capacitors in a single system – thanks to liquid energy storage materials.
Batteries and super capacitors are electrochemical energy storage media, but they are as different as night and day. Both are capable of energy storage and targeted energy release – and yet there are major differences between the two. Batteries store very large amounts of energy that is released slowly but constantly. By contrast, super capacitors can only store small amounts of energy, but they release this energy much faster and more powerfully with large short-term peak currents.
Stefan Freunberger at TU Graz together with a group of researchers from Université de Montpellier in Southern France had a sudden flash of insight. Why not exploit the benefits of batteries and super capacitors simultaneously and combine them in some kind of energy hybrid, they asked themselves. In the current issue of renowned scientific journal Nature Materials the group introduces its approach, describing a liquid energy storage material for the first time in a European Research Council (ERC) sponsored study. While the energy density of this material is comparable to that of a battery, its power output equals that of a super capacitor.
Ions with an urge to move
“Batteries release energy so slowly and take so long to charge because their energy storage materials are solid. This make it difficult for the ions to move. But as the ions in a super capacitor move in a liquid, they are much more mobile than in a solid body,” explains Stefan Freunberger from the Institute of Chemistry and Technology of Materials at TU Graz. The novel redox active ionic liquid developed by Freunberger in co-operation with the French colleagues consists of an organic salt that is liquid at a temperature of just below 30 °C – only slightly above room temperature. Similar to a solid storage medium this liquid can store many ions, but allows them to be much more mobile.
The sudden flash of insight of Freunberger and colleagues culminated in a first approach to create an integrated energy supply system that offers a constant energy supply with high-power output. In some cases we are still faced with an either/or decision. Automatic doors, for example in trams or trains, are typical candidates for super capacitors. Energy is only needed for a very short time but when it is, a high-power output is of the essence. In other cases batteries are clearly the first choice. “But our principle of an energy hybrid can offer enormous advantages, for example when applied in electric vehicles. So far, electric vehicles often carry a combination of different battery types or battery systems together with super capacitors. If we had a single system that combines the benefits of both energy storage types, we could save considerable space and resources,” remarks Freunberger.
Learn more: Energy hybrid: Battery meets supercapacitor
New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain.
Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses; a key component that every ANN requires in large numbers.
In the study, published in Nature Communications, the Southampton research team experimentally demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data.
Memristors are electrical components that limit or regulate the flow of electrical current in a circuit and can remember the amount of charge that was flowing through it and retain the data, even when the power is turned off.
Lead author Dr Alex Serb, from Electronics and Computer Science at the University of Southampton, said: “If we want to build artificial systems that can mimic the brain in function and power we need to use hundreds of billions, perhaps even trillions of artificial synapses, many of which must be able to implement learning rules of varying degrees of complexity. Whilst currently available electronic components can certainly be pieced together to create such synapses, the required power and area efficiency benchmarks will be extremely difficult to meet -if even possible at all- without designing new and bespoke ‘synapse components’.
“Memristors offer a possible route towards that end by supporting many fundamental features of learning synapses (memory storage, on-line learning, computationally powerful learning rule implementation, two-terminal structure) in extremely compact volumes and at exceptionally low energy costs. If artificial brains are ever going to become reality, therefore, memristive synapses have to succeed.”
Acting like synapses in the brain, the metal-oxide memristor array was capable of learning and re-learning input patterns in an unsupervised manner within a probabilistic winner-take-all (WTA) network. This is extremely useful for enabling low-power embedded processors (needed for the Internet of Things) that can process in real-time big data without any prior knowledge of the data.
Co-author Dr Themis Prodromakis, Reader in Nanoelectronics and EPSRC Fellow in Electronics and Computer Science at the University of Southampton, said: “The uptake of any new technology is typically hampered by the lack of practical demonstrators that showcase the technology’s benefits in practical applications. Our work establishes such a technological paradigm shift, proving that nanoscale memristors can indeed be used to formulate in-silico neural circuits for processing big-data in real-time; a key challenge of modern society.
“We have shown that such hardware platforms can independently adapt to its environment without any human intervention and are very resilient in processing even noisy data in real-time reliably. This new type of hardware could find a diverse range of applications in pervasive sensing technologies to fuel real-time monitoring in harsh or inaccessible environments; a highly desirable capability for enabling the Internet of Things vision.”
The Graz University of Technology (German: Technische Universität Graz, short TU Graz) is the second largest university in Styria, Austria, after the University of Graz. Austria has three universities of technology – in Graz, in Leoben, and in Vienna.
The Graz University of Technology was founded in 1811 by Archduke John of Austria. TU Graz is a public university. In the academic year 2014/15, 16.9% of the students were from abroad and 22.5% of the students were female out of the 12,777 students enrolled at the TU Graz.