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These materials function as tunable qubits that preserve spin states at room temperature, improving standard quantum computing systems. Quantum computers have the potential to perform simulations and make calculations infeasibly intensive or complex for classical computers. Quantum information science can help develop new medications, better machine learning, more secure encryption, and more realistic simulations for cutting-edge scientific research. However, available quantum systems store quantum information using qubit material that must remain cooled to absolute zero, and the information is only accessible using magnetic techniques. This makes developing useful quantum computers difficult.
Researchers at the University of Florida and Universidad de los Andes have developed metal-organic nanoparticle matrix qubit materials that store quantum data at room temperature and enable easier access to the data via electrical charge. These materials facilitate more practical quantum systems with greater accessibility, scalability, and functionality.
More practical quantum computing systems using enhanced qubit materials that work at room temperature
The material consists of multiferroic bismuth iron oxide (BFO) nanoparticles embedded in a metal-organic matrix. It has a unique combination of properties that makes it fit for use as a qubit in tunable quantum spin systems. The material conserves quantum spin states at room temperature and allows control of those states using charge currents due to its enhanced magnetoelectric coupling. Varying the geometric and chemical characteristics of the material can optimize the spin coherence time for a given quantum system.
This thermochemical process converts large-scale power plants’ flue exhaust into synthesis gas, which refines into high-quality synthetic fuels such as diesel or methanol. University of Florida researchers propose to do this via integration with a methane reformation driven redox cycle powered by solar energy, where the solar step and flue gas utilization step are separated in both space and time. This is vitally important for 24/7 operation and utilization of flue gas because at the industrial scales at which it is generated, storage is not a viable option. In total, this process affords the economical utilization and transformation of solar energy, flue gases and methane or natural gas to a valuable synthesis gas that can be further converted to a high quality, drop-in, diesel fuel and other added value chemicals that can be stored and transported.
Continuous conversion of industrial flue gases into valuable synthesis gas suitable for producing high quality liquid fuels and other value added chemicals
Natural gas and solar energy work in tandem continuously to process flue gas effluent from large-scale plants that consume fossil fuels. In a drop tube reactor, solar energy drives an endothermic reaction that reduces highly reactive ceria-based (CeO2) particles and oxidizes the methane found in natural gas to produce synthesis gas. A second reactor collects and stores the reduced ceria particles for later use in an exothermic reaction. The particles re-oxidize upon exposure to carbon dioxide and water vapor captured from flue gases, which themselves reduce selectively to generate synthesis gas. The re-oxidized ceria particles then return to the solar reactor for the synthesis gas production cycle to begin again.
These RANA batteries carry out more efficient energy conversion than available betavoltaic batteries and their long lifespans allow them to power implantable medical devices, including pacemakers. Like other nuclear batteries, they include a radiation source that works with other materials to convert decay energy into an electric current. While the typical betavoltaic battery has a layered structure of alternating plates of radioisotopes, decay energy converters, and photovoltaic (PV) cells, these new batteries, developed by University of Florida researchers, do not require alternating sheets. The RANA batteries are conformable, easy to manufacture, and offer more efficient energy conversion, making them attractive components for biotechnology applications. The lifespan of an in vivo bioimplant fitted with a RANA battery can exceed one hundred years.
Nuclear batteries that carry out more efficient energy conversion to power implantable medical devices, including pacemakers
Researchers at the University of Florida have developed more efficient nuclear batteries by encapsulating select radioactive nuclides within materials that can either convert or multiply radioisotope decay emissions into photons, which then interact with organic photovoltaic (OPV) cells. The OPV electricity generation mechanism is similar to conventional solar cells with the exception that the photons are initiated by the decay of a radioisotope. Depending on the source radionuclide, the decay process used in these batteries can be beta (electron emission) or gamma (photon emission). Doping the radioactive beta or gamma emitters directly into the photon conversion material and dispersing the particles into a translucent medium eliminates several of the major efficiency loss mechanisms found in traditional multi-layered designs.
Related to 14778
This tissue harvester creates an active apparatus upon which amniotic, progenitor, mesenchymal, and embryonic stem cells can rapidly proliferate and mature into more distinct forms and functions due to the versatile airfoil shape of the tissue harvester and its surface roughness. Stem cell advancements like this tissue harvester have the potential to revolutionize the way human diseases are treated. Many companies and nations have invested greatly in the future research and commercialization of stem cells and stem cell treatments. In 2016, more than $3 billion will go toward stem cell research. Stem cells are artificially grown and transformed into specialized cells, which have characteristics consistent with cells of other tissues. Additionally, many of the procedures used to produce stem cells only can be used in specific scenarios. Thus, researchers at the University of Florida have developed a tissue harvester that can generate cells under a variety of conditions for use in various medical therapies. By manipulating the surface texture of the substrate, researchers can use this apparatus for a variety of applications in both the academic and industrial sector. This tissue harvester also has the ability to quantify the amount of shear force to which each cell is subjected, helpful when studying the interaction of different cell types.
Tissue harvester that creates an active apparatus that allows stem cells to proliferate, mature and differentiate
This tissue harvester allows pluripotent stem cells to proliferate and undergo differentiation under a variety of conditions for use in various medical therapies. The wing-shaped profile of the apparatus exerts a gradient of shear force onto the stem cells to enable the harvesting of multiple cell types efficiently. The harvested stem cells are then placed on the tissue harvester and allowed to proliferate. The apparatus possesses a textured surface that comprises nanometer- or micrometer-sized pillars of varying cross-sections and spacing. These pillars can be manipulated and transformed into different shapes depending on which cells and structures are being targeted and their intended uses. Then the apparatus is placed within a flow field such that the cells disposed on the tissue harvester can experience different flow fields. The versatile geometry of the airfoil shape tissue harvester allows for the stem cells placed on the textured surface to be swept up and distributed by laminar and turbulent flow facilitating a large scale generation of a cell viable cell source.
This neuromorphic architecture model uses memristive nanofibers to simulate neural networks. Artificial neural networks refer to computer systems inspired by the biological neural networks in the brain, comprising powerful tools in computer science and artificial intelligence. Areas such as speech recognition, object tracking, data analysis, fraud detection, and more commonly use artificial neural networks. Most current neural networks integrate into software running on conventional central processing units (CPUs) and graphic processors. While these networks can simulate millions of neurons, they are highly inefficient regarding connectivity, power expenditure, and neural density. They are also time-consuming. Any significant improvement over current paradigms of neural networks and neuromorphic hardware will have immediate relevance to the global marketplace and may even aid in understanding the human brain.
The field of neuromorphic engineering aims to create specific hardware for artificial neural networks with neurons capable of running in real-time and independently of one another, free of the serial processing constraints of conventional computers. However, modeling synapses between neurons requires multiple transistors to implement, making it the most complicated component of neuromorphic hardware. The invention of the memristor revolutionized the field of neuromorphic engineering. This two-terminal nanoscale device can modify its resistance by passing current in one direction or another through the device, enabling efficient modeling of modifiable synapses between neurons. However, designs using memristors still face several challenges, including poor scaling properties due to the requirement for large amounts of space for the wiring between neurons. Additionally, these architectures comprise neurons fully connecting, compared to sparse connections in biological neural networks. Sparse connectivity in artificial neural networks could enable more efficient scaling while keeping the computational capacity intact.
Researchers at the University of Florida have developed a sparse, scalable neural network using memristive nanofibers. Creating randomized memristive wiring between artificial neurons improves connection efficiency and scalability.
Scalable neuromorphic hardware architecture using core-shell memristive nanofibers for randomly connecting neural nodes
This artificial neural network architecture consists of a randomly aligned network of nanofibers with a conductive core and a memristive shell. The nanofibers are the connections, or artificial synapses, between artificial silicon neurons. The connection between the nanofibers and the neural nodes is random, resulting in a network with sparse connectivity, increasing performance and efficiency. Each nanofiber also includes one or more electrodes, serving as a conductive attachment point between the memristive nanofiber and an input or output terminal of an artificial neuron. The conductive core of the nanofibers enables the transmission of signals between the neurons, while the memristive shell creates adjustable connections between the neurons and the fiber core. Controlling the amount and direction of current passing through the memristors enables regulation of the intensity of the synapses.
DEPARTMENT OF MATERIALS SCIENCE AND ENG. UNIVERSITY OF FLORIDA P.O. BOX 116400 GAINESVILLE, FL 32611-6400
