One problem that perovskite solar cells face is reproducibility. This means that it is hard to consistently create perovskite crystal layers free of defects and holes, which means that deviations from design values are always likely to occur, which reduce their efficiency. On the bright side, researchers have found that the efficiency of these cells can be boosted by combining perovskite with carbon nanotubes (CNTs). The mechanism by which CNTs and perovskite bond together and how this affects the performance of CNT perovskite solar cells has not been studied in depth. In particular, the ability of pure CNTs to bond to perovskite is not very good, and this could compromise the structural and conducting properties at the interface of both materials.

Hence, a team from Tokyo Tech lead by Prof. Keiko Waki conducted a series of experiments on perovskite solar cells combined with different types of CNTs in an attempt to both improve their performance and stability and understand the underlying mechanisms. They used not just pure CNTs, but also CNTs that bore “oxygen-containing functional groups” in their structure, which have been known to strengthen the interaction between the CNTs and perovskite, resulting in better interfaces and enhancing the crystallization of perovskite.

This research consisted of several experiments that provided insight into many aspects of CNT-perovskite interactions. First, they demonstrated the superior electrical performance of cells with functionalized CNTs over those with pure CNTs and found evidence supporting that larger crystals and fewer surface defects occur when using functionalized CNTs. Then, the team inferred that the perovskite in the cells would undergo a recrystallization process if stored in the dark, and that the presence of the functional groups in CNT would have a significant effect on this process. This was confirmed by storing the cells for over two months and measuring their electrical characteristics afterwards (Fig. 1). “We have discovered the self-recrystallization ability of perovskite at room temperature, whose morphology greatly improved after long-term storage. However, the most interesting result was the ability of functionalized CNTs to make use of the self-recrystallization nature to form a stronger junction between the perovskite and CNTs through the reconstruction” explains Prof. Waki. Most notably, the functionalized CNTs improved the contact between the two materials greatly and the functional groups served as a protection against attacks from moisture on the perovskite, allowing the self-recrystallization and interface reconstruction to proceed without noticeable degradation. The research team also found that the recrystallization process could be immensely sped up by constantly subjecting the solar cells to frequent measurements, but this ultimately affected their stability and degraded them.

Such in-depth studies on perovskite solar cells and ways to enhance them are very valuable because they get us closer to new sources of clean energy. “We hope this study contributes toward the production of perovskites with higher stability and reproducibility,” concludes Prof. Waki. These findings will serve as another stepping stone so that we may one see perovskite solar cells as a key technology to preserve our planet.

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Called LOGAN, the deep neural network, i.e., a machine of sorts, can learn to transform the shapes of two different objects, for example, a chair and a table, in a natural way, without seeing any paired transforms between the shapes. All the machine had seen was a bunch of tables and a bunch of chairs, and it could automatically translate shapes between the two unpaired domains. LOGAN can also automatically perform both content and style transfers between two different types of shapes without any changes to its network architecture.

The team of researchers behind LOGAN, from Simon Fraser University, Shenzhen University, and Tel Aviv University, are set to present their work at ACM SIGGRAPH Asia held Nov. 17 to 20 in Brisbane, Australia. SIGGRAPH Asia, now in its 12th year, attracts the most respected technical and creative people from around the world in computer graphics, animation, interactivity, gaming, and emerging technologies.

“Shape transform is one of the most fundamental and frequently encountered problems in computer graphics and geometric modeling,” says senior coauthor of the work, Hao (Richard) Zhang, professor of computing science at Simon Fraser University. “What is new and emerging is to tie this important problem to deep learning — can a machine learn to transform shapes, particularly under the unsupervised or unpaired setting?”

In this work, the researchers turned to a well-known technique in machine learning, Generative Adversarial Network (GAN), for unpaired general-purpose shape transforms. Their network is trained on two sets of shapes, e.g., tables and chairs or different letters. There is neither a pairing between shapes in the two domains to guide shape translation nor any point-wise correspondence between any shapes. Once trained, the researchers’ method takes a point-set shape from one domain, a table or a chair, and transforms into the other.

LOGAN overcomes a key challenge in shape transform techniques. Given two sets of shapes — chairs and tables — it is challenging for the network to learn which particular shape features should be preserved or altered to result in realistic transformation of the object, from chair to table and vice versa. The team’s method learns the unique differences in features and can automatically determine which features should be kept or discarded in order to achieve the desired shape transform, and can do so without supervision.

Other techniques in computer vision for unpaired image-to-image translation have been developed and have been successful in translating style features, but most have not achieved shape translation. “In 2017, CycleGAN and DualGAN, two highly influential works from computer vision were developed for unpaired image-to-image style translation. LOGAN specifically produces realistic shape translations, both in style and content, for the first time,” notes Zhang. Additionally, the researchers demonstrate that LOGAN can learn “style-preserving” content transfers. For instance, the network can automatically transform a letter ‘R’ into a ‘P’ of the same font style, or with respect to style translation, their method is able to translate a bold-face letter ‘A’ into an italicized ‘A’.

To devise their method, the researchers train a neural network which encodes the two types of input shapes into a common latent space. In deep learning, the latent space is represented by the bottleneck layer where the network captures the features of the input data. LOGAN is not only trained to turn a chair code to a table code, but also trained to turn a table code to the same table code. The latter enables “feature preservation” and helps maintain certain table features during chair-to-table shape translations.

In ablation studies, the researchers demonstrate LOGAN’s superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. Their study shows that LOGAN is able to learn what shape features to keep during transforms, and the results accurately resemble the desired object.

In future work, the team aims to fine tune LOGAN to work on all domain pairs to make it truly general-purpose. The current version of LOGAN also is not yet smart enough to understand the meanings of the shapes, and the researchers are working on making the network “smarter” to incorporate this information.

While we sleep, the hippocampus reactivates itself spontaneously by generating activity similar to that while we are awake. It sends information to the cortex, which reacts in turn. This exchange is often followed by a period of silence called a ‘delta wave’ then by rhythmic activity called a ‘sleep spindle’. This is when the cortical circuits reorganize to form stable memories. However, the role of delta waves in the formation of new memories is still a puzzle: why does a period of silence interrupt the sequence of information exchanges between the hippocampus and the cortex, and the functional reorganisation of the cortex?

The authors here looked more closely at what happens during delta waves themselves. They discovered, surprisingly, that the cortex is not entirely silent but that a few neurons remain active and form assemblies, i.e. small, coactive sets that code information. This unexpected observation suggests that the small number of neurons that activate when all the others stay quiet can carry out important calculations while protected from possible disturbances. And the discoveries from this work go even further! Spontaneous reactivations of the hippocampus determine which cortical neurons remain active during the delta waves and reveal transmission of information between the two cerebral structures. In addition, the assemblies activated during the delta waves are formed of neurons that have participated in learning a spatial memory task during the day. Together these elements suggest that these processes are involved in memory consolidation. To demonstrate it, in rats the scientists caused artificial delta waves to isolate either neurons associated with reactivations in the hippocampus, or random neurons. Result: when the right neurons were isolated, the rats managed to stabilise their memories and succeed at the spatial test the next day.

These results substantially change how we understand the cortex. Delta waves are therefore a means of selectively isolating assemblies of chosen neurons, which send crucial information between the periods of hippocampo-cortical dialog and the reorganisation of cortical circuits, to form long-term memories.

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Croissants are made by pressing and folding dough to create a layered pastry. The researchers applied this technique to a dielectric capacitor, which is a device that stores energy like a battery.

By pressing and folding a polymer film capacitor — a capacitor with an insulating plastic film — they were able to store 30 times more energy than the best-performing commercially available dielectric capacitor, biaxially oriented polypropylene (BOPP).

The study, published in the journal Nature Communications, shows that this is the highest energy density ever reported in a polymer film capacitor.

Renewable and sustainable energy sources like solar and wind are intermittent by nature and to make them of wider practical use it is necessary to develop efficient, low-cost and environmentally friendly electric energy storage systems.

Dr Emiliano Bilotti, lead researcher of the study from Queen Mary University of London, said: “Storing energy can be surprisingly tricky and expensive and this is problematic with renewable energy sources which are not constant and rely on nature. With this technique we can store large amounts of renewable energy to be used when the sun is not shining and it is not windy.”

Currently, there are three main energy storage options: batteries, electrochemical capacitors and dielectric capacitors.

Dielectric capacitors typically have ultrahigh power density, which makes them suitable for high power and pulse power technologies that require accumulating energy over a period of time and then releasing it very quickly. Examples of this include motor drives, mobile power systems, space?vehicle power systems and electrochemical guns.

However, dielectric capacitors are limited by the low amounts of energy they can currently store. This research study tackles this limitation.

Professor Mike Reece, another author of the study from Queen Mary University of London, said: “This finding promises to have a significant impact on the field of pulse power applications and could produce a step change in the field of dielectric capacitors, so far limited by their low energy storage density.”

Expensive and complex synthesis and processing routes are normally necessary to achieve high energy density in polymer film capacitors but this newly developed processing, pressing and folding, is unique for its simplicity, record high energy density and potential to be adopted by industry.

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Scientists at the University of Wisconsin-Madison, the U.S. Geological Survey National Wildlife Health Center and the Wisconsin Department of Natural Resources found the virus while searching for the cause of Wisconsin River Eagle Syndrome, an enigmatic disease endemic to bald eagles near the Lower Wisconsin River. The newly identified bald eagle hepacivirus, or BeHV, may contribute to the fatal disease, which causes eagles to stumble and have seizures.

But BeHV was also found in eagles without symptoms of the syndrome, making a direct link between virus and disease difficult to confirm. The virus is related to human hepatitis C virus, which causes liver damage in people, and some birds with BeHV show similar effects. BeHV infects eagles from Washington to Florida but is most common in Wisconsin’s eagles.

The researchers published their findings Oct. 18 in the journal Scientific Reports. Tony Goldberg, a UW-Madison professor of pathobiological sciences in the School of Veterinary Medicine, led the study. He collaborated with LeAnn White at the USGS National Wildlife Health Center and Sean Strom at the Wisconsin Department of Natural Resources.

Wisconsin River Eagle Syndrome (WRES) was first described in the 1990s. The river attracts eagles year-round because its open waters allow the birds to fish through the winter. Observers near the river spotted eagles vomiting or staggering, and all of these birds either succumbed to the disease or were euthanized.

Officials at the USGS National Wildlife Health Center conducted necropsies to determine the cause of death and found liver damage in most of the birds. Damage to brain tissue was also common. Searches for environmental toxins underlying the disease came up empty.

“Everything was pointing toward some unknown virus, but standard diagnostic techniques couldn’t find one,” says Goldberg.

Goldberg’s lab specializes in discovering new viruses. By analyzing all of the genetic material in infected tissue, his team can scan for the genetic signature of any virus within the sample. The technique does not require prior knowledge of what kind of virus might lurk within an animal.

Working with White and Strom, Goldberg’s lab analyzed liver tissue from nine birds diagnosed with WRES. The team first spotted the new virus in a bald eagle collected in 2002 in Sauk County. This family of viruses had not been found in birds before, although it has since been identified in ducks as well.

Testing 47 eagles from 19 states across the contiguous United States, the researcher team found that 32 percent harbored BeHV. Researchers found the virus in seven states, including several in the Midwest, but also in states as far apart as Washington and Florida. Eagles in Wisconsin were nine times more likely to carry BeHV than birds from other states, and the virus was 14 times more common in counties surrounding the Lower Wisconsin River than elsewhere.

Yet the presence of BeHV in birds collected outside of Wisconsin — none of which had been diagnosed with WRES — suggests the virus may not be responsible for the enigmatic disease, or that the story is more complex.

“Is BeHV the cause of WRES?” asks Goldberg. “Or is it more complicated than that?”

There is a long list of possible explanations. Infected birds may be more likely to die of starvation or other illnesses before they show symptoms of WRES, but eagles collected from outside Wisconsin did not show the liver damage characteristic of WRES. It’s also possible that the prime conditions near the Wisconsin River allow birds to survive long enough for the disease to progress to its end stage.

“It is curious that the liver pathology of the eagles resembles the damage to human livers caused by hepaciviruses,” says White, branch chief for wildlife epidemiology and emerging diseases at the USGS National Wildlife Health Center. “But since the lesions are nonspecific there’s more that needs to be explored if we want to understand the virus itself or really get at what is the cause of WRES.”

The virus and the syndrome do not appear to endanger the resurgence of the bald eagle, which was removed from the endangered species list in 2007 after recovering from a low of 412 nesting pairs in the contiguous United States. There are now about 1,700 nesting pairs in Wisconsin alone.

“We don’t think this virus is having a serious impact on the bald eagle population, but the fact that WRES is an unknown condition keeps our interest,” says Strom, an environmental toxicologist with the DNR. “This study is another piece of the puzzle. Hopefully we can find more pieces and figure out what is happening.”

“This study has opened our eyes to glaring knowledge gaps about infection in a species of great national importance,” says Goldberg. “It’s a more complicated story than we thought it might be at first, but that makes it more interesting.”

This work was supported in part by the U.S. Geological Survey (award number G14AC00364).