Photovoltaic cell that works at night

What if solar cells worked at night? That’s no joke, according to Jeremy Munday, professor in the Department of Electrical and Computer Engineering at UC Davis. In fact, a specially designed photovoltaic cell could generate up to 50 watts of power per square meter under ideal conditions at night, about a quarter of what a conventional solar panel can generate in daytime, according to a concept paper by Munday and graduate student Tristan Deppe. The article was published in, and featured on the cover of, the January 2020 issue of ACS Photonics.

solar energy panels in night

There’s another kind of device called a thermoradiative cell that generates power by radiating heat to its surroundings. Researchers have explored using them to capture waste heat from engines. “We were thinking, what if we took one of these devices and put it in a warm area and pointed it at the sky,” Munday said.

This thermoradiative cell pointed at the night sky would emit infrared light because it is warmer than outer space. “A regular solar cell generates power by absorbing sunlight, which causes a voltage to appear across the device and for current to flow. In these new devices, light is instead emitted and the current and voltage go in the opposite direction, but you still generate power,” Munday said. “You have to use different materials, but the physics is the same.” The device would work during the day as well, if you took steps to either block direct sunlight or pointed it away from the sun. Because this new type of solar cell could potentially operate around the clock, it is an intriguing option to balance the power grid over the day-night cycle.

Source: University of California – Davis. “Anti-solar cells: A photovoltaic cell that works at night.” ScienceDaily. ScienceDaily, 29 January 2020.

Brewing a better espresso, with a shot of math

Mathematicians, physicists, and materials experts might not spring to mind as the first people to consult about whether you are brewing your coffee right. But a team of such researchers from around the globe — the United States, the United Kingdom, Ireland, Australia, and Switzerland — are challenging common espresso wisdom, finding that fewer coffee beans, ground more coarsely, are the key to a drink that is cheaper to make, more consistent from shot to shot, and just as strong.

espresso

Though lots of factors are involved, the norm for brewing an espresso shot is to grind a relatively large amount of coffee beans (~20 grams) almost as finely as possible. The fine grind, common sense goes, means more surface area exposed to the brewing liquid, which ought to boost extraction yield — the fraction of the ground coffee that actually dissolves and ends up in the final drink.

But when the researchers put together a mathematical model to explain the extraction yield based on the factors under a barista’s control — options such as the masses of water and dry coffee, the fineness or coarseness of the grounds, and the water pressure — and compared its predictions to brewing experiments, it became clear that the real relationship was more complicated. Grinding as finely as the industry standard clogged the coffee bed, reducing extraction yield, wasting raw material, and introducing variation in taste by sampling some grounds and missing others entirely.

Boosting the extraction yield through one or more of the routes illustrated by the model could also lead to economic gains for cafes and to sustainability benefits for the coffee industry as a whole. For example, at the current price of roasted coffee beans, dropping the mass of dry coffee from 20 grams to 15 grams per drink would add up to savings of a few thousand dollars per year for a small cafe, and $1.1 billion per year if scaled up to the whole US coffee industry. Being more efficient with coffee bean usage would also reduce waste at a time when coffee supply is under threat from changing climate in historic production areas.

Source

Original article: Michael I. Cameron, Dechen Morisco, Daniel Hofstetter, Erol Uman, Justin Wilkinson, Zachary C. Kennedy, Sean A. Fontenot, William T. Lee, Christopher H. Hendon, Jamie M. Foster. Systematically Improving Espresso: Insights from Mathematical Modeling and ExperimentMatter, 2020; DOI: 10.1016/j.matt.2019.12.019

Transistors can now both process and store information

Purdue University engineers have developed a way that the millions of tiny switches used to process information — called transistors — could also store that information as one device. The method, detailed in a paper published in Nature Electronics, accomplishes this by solving another problem: combining a transistor with higher-performing memory technology than is used in most computers, called ferroelectric RAM.

transistor-abc

a, Schematic of a Fe-FET. b, Schematic of a FeS-FET. In the FeS-FET, the conventional semiconductor channel is replaced by a ferroelectric semiconductor, while the gate insulator is still conventional dielectric. c, Polarization bound charge distribution in a FeS-FET in polarization down (after negative gate bias) and polarization up (after positive gate bias) states.

The material, alpha indium selenide, not only has ferroelectric properties, but also addresses the issue of a conventional ferroelectric material usually acting as an insulator rather than a semiconductor due to a so-called wide “band gap,” which means that electricity cannot pass through and no computing happens. Alpha indium selenide has a much smaller band gap, making it possible for the material to be a semiconductor without losing ferroelectric properties.

In the past, researchers hadn’t been able to build a high-performance ferroelectric tunneling junction because its wide band gap made the material too thick for electrical current to pass through. Since alpha indium selenide has a much smaller band gap, the material can be just 10 nanometers thick, allowing more current to flow through it. More current allows a device area to scale down to several nanometers, making chips more dense and energy efficient, Ye said. A thinner material — even down to an atomic layer thick — also means that the electrodes on either side of a tunneling junction can be much smaller, which would be useful for building circuits that mimic networks in the human brain.

Source

Paper: Mengwei Si, Atanu K. Saha, Shengjie Gao, Gang Qiu, Jingkai Qin, Yuqin Duan, Jie Jian, Chang Niu, Haiyan Wang, Wenzhuo Wu, Sumeet K. Gupta, Peide D. Ye. A ferroelectric semiconductor field-effect transistorNature Electronics, 2019; DOI: 10.1038/s41928-019-0338-7

New method to remove dust on solar panels

Taking a cue from the self-cleaning properties of the lotus leaf, researchers at Ben-Gurion University of the Negev have shed new light on microscopic forces and mechanisms that can be optimized to remove dust from solar panels to maintain efficiency and light absorption. The new technique removed 98% of dust particles. Dust adhesion on solar panels is a major challenge to energy harvesting through photovoltaic cells and solar thermal collectors. New solutions are necessary to maintain maximum collection efficiency in high dust density areas such as the Negev desert in Israel.

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The researchers explored the effect of modifying a silicon substrate (Si), a semiconductor used in photovoltaic cells, to mimic the self-cleaning properties of the lotus leaf, as water rolls down the leaves and removes contamination. It is known that superhydrophobicity reduces the friction between water droplets and the surface, thus allowing water drops to slide clean particles from surfaces. However, the forces that attach and detach particles from surfaces during the self-cleaning mechanism and the effect of nanotextures on these forces are not fully understood.

Particle removal increased from 41% on hydrophilic smooth Si wafers to 98% on superhydrophobic Si-based nanotextured surfaces. The researchers confirmed these results by measuring the adhesion of a micron-sized particle to the flat and nanotextured substrate using an atomic force microscope. They found that the adhesion in water is reduced by a factor of 30.

Source

Paper: Heckenthaler, T., Sadhujan, S., Morgenstern, Y., Natarajan, P., Bashouti, M. and Kaufman, Y., 2019. Self-Cleaning Mechanism: Why Nanotexture and Hydrophobicity Matter. Langmuir35(48), pp.15526-15534.

Daily exposure to blue light may accelerate aging, even if it doesn’t reach your eyes

Prolonged exposure to blue light, such as that which emanates from your phone, computer and household fixtures, could be affecting your longevity, even if it’s not shining in your eyes. New research at Oregon State University suggests that the blue wavelengths produced by light-emitting diodes damage cells in the brain as well as retinas. The study, involved a widely used organism, Drosophila melanogaster, the common fruit fly, an important model organism because of the cellular and developmental mechanisms it shares with other animals and humans.

young woman using the smart phone on bed before sleep

Jaga Giebultowicz, a researcher in the OSU College of Science who studies biological clocks, led a research collaboration that examined how flies responded to daily 12-hour exposures to blue LED light — similar to the prevalent blue wavelength in devices like phones and tablets — and found that the light accelerated aging. Flies subjected to daily cycles of 12 hours in light and 12 hours in darkness had shorter lives compared to flies kept in total darkness or those kept in light with the blue wavelengths filtered out. The flies exposed to blue light showed damage to their retinal cells and brain neurons and had impaired locomotion — the flies’ ability to climb the walls of their enclosures, a common behavior, was diminished.

Some of the flies in the experiment were mutants that do not develop eyes, and even those eyeless flies displayed brain damage and locomotion impairments, suggesting flies didn’t have to see the light to be harmed by it. “The fact that the light was accelerating aging in the flies was very surprising to us at first,” said Giebultowicz, a professor of integrative biology. “We’d measured expression of some genes in old flies, and found that stress-response, protective genes were expressed if flies were kept in light. We hypothesized that light was regulating those genes. Then we started asking, what is it in the light that is harmful to them, and we looked at the spectrum of light. It was very clear cut that although light without blue slightly shortened their lifespan, just blue light alone shortened their lifespan very dramatically.”

Natural light, Giebultowicz notes, is crucial for the body’s circadian rhythm — the 24-hour cycle of physiological processes such as brain wave activity, hormone production and cell regeneration that are important factors in feeding and sleeping patterns. “But there is evidence suggesting that increased exposure to artificial light is a risk factor for sleep and circadian disorders,” she said. “And with the prevalent use of LED lighting and device displays, humans are subjected to increasing amounts of light in the blue spectrum since commonly used LEDs emit a high fraction of blue light. But this technology, LED lighting, even in most developed countries, has not been used long enough to know its effects across the human lifespan.”

Giebultowicz says that the flies, if given a choice, avoid blue light. In the meantime, there are a few things people can do to help themselves that don’t involve sitting for hours in darkness, the researchers say. Eyeglasses with amber lenses will filter out the blue light and protect your retinas. And phones, laptops and other devices can be set to block blue emissions.

Source

Neptune moons ‘dance’ to avoid cosmic collision

The orbits of Neptune’s two innermost moons are very odd, according to new research released by NASA. This planet boasts at least 14 moons. Neso, the farthest-flung of them, orbits in an extremely elliptical loop carrying it almost 46 million miles (74 million km) away from the planet, taking 27 years to complete. Naiad and Thalassa are small and shaped like Tic Tacs, spanning only about 60 miles (100 km) in length. Orbital dynamics experts have dubbed the cosmic choreography of these tiny moons as a “dance of avoidance”. The pair orbit Neptune only 1,150 miles (1,850km) apart, yet  they miraculously always manage to miss each other as Naiad’s orbit is tilted and perfectly timed.

Every time the moon passes the slower-moving Thalassa, the couple move 2,200 miles (3,540km) apart. These trajectories sees Naiad hurtle around the frozen planet every seven hours, while the closer Thalassa takes half an hour more. An astronaut sitting on Thalassa would see Naiad in an apparently wild zigzagging orbit, passing by twice from above and then twice from below.

This pattern repeats every time Naiad gains four laps on Thalassa. And NASA has revealed how although the dance may appear odd, it keeps the orbits stable. Marina Brozović, an expert in solar system dynamics at NASA, said: “We refer to this repeating pattern as a resonance. “There are many different types of ‘dances’ that planets, moons and asteroids can follow, but this one has never been seen before.”

Source

Weaklier Supervised Semantic Segmentation

Weakly supervised learning methods have brought improvements to the semantic segmentation problem. By simplifying the labeling work, more attention is given to the network architecture. In the paper entitled “Weaklier Supervised Semantic Segmentation With Only One Image Level Annotation per Category”, the authros propose a three-stage semantic segmentation framework that deals with image and pixel level understanding at a coarse level and goes deeper towards objects feature learning at a fine grained level.

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The novelty consists of using only one sample with image level annotation per category, whose labeling form is more closer to prior conditions required by human to learn new objects. For image classification, response activation clustering (RAC) is proposed to achieve image level labeling, while multi heat map slices fusion (MSF) and saliency-edge-color-texture (SECT) based modification are utilized to generate pixel level annotations, which combine high-level semantic features and imaging prior based low-level attributes. For object common feature learning, dual-branch iterative structure is introduced. Based on conservative and radical strategies, information integration are realized iteratively, the completeness and accuracy of object region are gradually improved.

In the first stage, image level semantic information is extracted in form of response vector, and the relationship of each pair of feature dimensions is analyzed to achieve accurate image level object category annotations. Then, heat maps based on high-level semantics and low-level imaging attributes are utilized in combination to generate pixel level pseudo supervised annotations. In the first two phases, multi attention mechanism is introduced to achieve a better understanding of objects which are not salient or with small scale, as well as to mine detailed expression in images. Using a number of obtained annotations, dual branch network model is designed to learn common features of objects from different instances, more complete and accurate object regions can be obtained iteratively. Based on the methods, semantic segmentation task is implemented through a learning process which takes advantage of prior knowledge as much as possible.

Li, Xi, Huimin Ma, and Xiong Luo. “Weaklier Supervised Semantic Segmentation With Only One Image Level Annotation per Category.” IEEE Transactions on Image Processing 29 (2019): 128-141.

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Automatic Detection and Classification of External Olive Fruits Defects

Olives are an important agricultural product, therefore the industry is interested in detecting their external defects. The researchers Nashat Hussain Hassan and Ahmed Nashat from Fayout University, Egypt, have developed an image processing method that can classify healthy or defected olive fruits. Furthermore, a series of techniques have been compared to find the most appropriate low-cost kit that can be used in a real application.

olivesUntitled

a Healthy olives, b defected class (A), c defected class (B)

The first developed algorithm is called Texture Homogeneity Measuring Techique (T.H.M.T) and it consists of five steps. First, images are collected and then pre-processed by applying a grayscale conversion. The next step is to extract objects by segmenting the images into olives and background. The defects are obtained by scanning the image horizontally and pixels are labeled accordingly with ‘0’ for a healthy area and ‘1’ if a defect is present.

The second proposed method is called Special Image Convolution Algorithm (S.I.C.A.) and it is similar to edge detection, but with specific kernels of 7×7 which are applied both horizontally and vertically. The results are then thresholded based on the values observed by the authors.

Hassan, Nashat M. Hussain, and Ahmed A. Nashat. “New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques.” Multidimensional Systems and Signal Processing 30.2 (2019): 571-589.

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