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Category: sustainability – Page 245

“This makes agri-PV systems increasingly attractive for agriculture, because it provides a way to keep domestic agriculture competitive with the international market and to enable farmers to earn additional income,” explains Max Trommsdorff, project manager at Fraunhofer ISE. “At the same time, we can drive the expansion of renewable energies, reduce pressure on scarce land and increase resilience to weather extremes and climate change in different farming systems.”

Nevertheless, only a few projects have been realised so far. Those involved in the project see one of the crucial hurdles in the existing legal framework. These include inadequate incentive systems and comparatively complex approval processes. In addition, there are growing concerns about the acceptance of the local population and the attractiveness of the landscape.

Such economic, legal and social hurdles are to be compiled within the framework of the project. Subsequently, the participants want to work out proposals for solutions on how to reduce and overcome these hurdles. The focus should be on the optimal use of the potentials and the avoidance of wrong decisions in the application of agriphotovoltaics.

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Researchers have developed a new type of high-efficiency photodetector inspired by the photosynthetic complexes plants use to turn sunlight into energy. Photodetectors are used in cameras, optical communication systems and many other applications to turn photons into electrical signals.

Researchers developed a new type of high-efficiency photodetector that is similar to the photosynthetic complexes plants use to turn sunlight into energy. The new design integrates a simple organic detector into the propagation region to produce efficient polariton-to-charge conversion over distances of up to 100 microns. (Image: Bin Liu, University of Michigan)

“Our devices combine long-range transport of optical energy with long-range conversion to electrical current,” said research team leader Stephen Forrest from the University of Michigan. “This arrangement, analogous to what is seen in plants, has the potential to greatly enhance the power generation efficiency of solar cells, which use devices similar to photodetectors to convert sunlight into energy.”

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Circa 2016

A radically new form of lithium-oxygen batteries avoids many of the problems that have prevented the uptake of what is, in theory, the ultimate transportation battery. If the work can be scaled up, it could mark the end of gasoline-powered cars.

The cost, weight, and insufficient lifespan of batteries represents a major obstacle to electric cars replacing internal combustion engines on our roads. There are two paths to address this: One, like Aesop’s tortoise, involves slow incremental improvements in existing lithium-ion batteries, collectively bringing down the cost and extending the range of electric vehicles.

The other path involves a shift to a radically better technology, of which the one with the greatest potential is lithium-oxygen, also known as lithium-air. The announcement in Nature Energy of a very different way of making lithium-oxygen batteries indicates it is not time to write off the hare in this race.

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The global rush to recycled batteries is excellent news for automakers, however, there isn’t nearly enough scrap to feed them all.

The wave of new factories poses a significant risk for the recycling industry itself, Bloomberg reported on Thursday.

“Nobody is really looking at each other, and they seem to think there will be a lot of scrap and end-of-life batteries,” Hans Eric Melin, the founder of a UK-based consultancy Circular Energy Storage(CES), told Bloomberg.

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Researchers have developed a machine learning algorithm that could help reduce charging times and prolong battery life in electric vehicles by predicting how different driving patterns affect battery performance, improving safety and reliability.

The researchers, from the University of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power by suggesting routes and driving patterns that minimize battery degradation and charging times.

The team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.

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