Scientists Just Set a New World Record in Solar Cell Efficiency

 Improving the efficiency of solar cells can make a huge difference to the amount of energy produced from the same surface area and the same amount of sunshine, and another world record has been beaten in the push for better yields.

Researchers have now hit efficiency of 29.15 percent in the perovskite/silicon tandem solar cell category, which is just one of several different types of cells. There are currently a variety of different technologies in use to convert solar energy into electricity.

For this type of panel, the long-term target of more than 30 percent is now tantalizingly within reach. The latest lab tests edge ahead of the maximum 28 percent efficiency that perovskite/silicon cells have managed up to this point.

The layers of the tandem solar cell. (Eike Köhnen/HZB)

"Tandem solar cells that pair silicon with a metal halide perovskite are a promising option for surpassing the single-cell efficiency limit," write the researchers in their published paper. "We report a monolithic perovskite/silicon tandem with a certified power conversion efficiency of 29.15 percent."

Perovskite and silicon have actually been developed separately as semiconductor materials for solar panel use: silicon cells have been around for longer, and are currently the standard in solar farms around the world.

Perovskite is the up and coming new challenger, which scientists think could eventually eclipse silicon in terms of usefulness.

That's why scientists have long been experimenting with different perovskite compound combinations and adding other materials – silicon, in this case. The so-called tandem cell uses two semiconductors that can capture two different parts of the light spectrum, extending beyond infrared light (captured by silicon) into visible light too (captured by the perovskite compounds).

More good news is that putting perovskite and silicon together doesn't substantially add to the cost of making the panels. Keeping the price down is important for getting solar technology rolled out as far and as quickly as possible.

In this new research, the 29.15 percent efficiency record was managed with a 1 cm x 1 cm (0.4-inch x 0.4 inch) panel, so some serious scaling up will be required. The team says that should be possible, however. After 300 hours of simulated use, the tandem cell retained 95 percent of its original efficiency, which is another promising sign.

The new record was actually first reported earlier this year, though the peer-reviewed paper detailing the feat has just been published. The scientists used specially tweaked layer compositions for both connecting the electrode layer and keeping the two types of cells together in order to reach their new record.

It's another moment to celebrate, but the scientists aren't stopping: previous research suggests that tandem solar cell technology should be able to reach efficiency rates of well above 30 percent, and the team says "initial ideas for this are already under discussion".

China Claims It's Achieved 'Quantum Supremacy' With The World's Fastest Quantum Computer

 A team of Chinese scientists has developed the foremost powerful quantum computer within the world, capable of engaging at least one task 100 trillion times faster than the world's fastest supercomputers.

In 2019, Google said it had built the primary machine to attain "quantum supremacy," the primary to outperform the world's best supercomputers at quantum calculation, Live Science previously reported. (IBM disputed Google's claim at the time.)

The Chinese team, based primarily at the University of Science and Technology of China in Hefei, reported their quantum computer, named Jiuzhang, is 10 billion times faster than Google's. an outline of Jiuzhang and its feat of calculation was published December 3 within the journal Science.

Assuming both claims interference, Jiuzhang would be the second quantum computer to realize quantum supremacy anywhere within the world.

China has invested heavily in quantum computing, with Xi Jinping's government spending US$10 billion on the country's National Laboratory for Quantum Information Sciences, NDTV reported.

The country is additionally a world leader in quantum networking, where data encoded using quantum physics is transmitted across great distances, as Live Science has reported.

Quantum computers can exploit the weird mathematics governing the quantum world to outperform classical computers on certain tasks, as Live Science reported.

Where classical computers perform calculations using bits, which may have one amongst two states (typically represented by a 1 or a 0), quantum bits, or qubits, can exist in many countries simultaneously. this permits them to unravel problems more quickly than classical computers.

But while the theories predicting that quantum computing would beat classical computing are around for many years, building practical quantum computers has proved far more challenging. 

The Chinese computer makes its calculations (limited to particular questions about the behavior of sunshine particles) using optical circuits.

Google's device, Sycamore, uses superconducting materials on a chip and more nearly resembles the fundamental structure of classical computers.

Neither would be particularly useful on its own as a computer and therefore the Chinese device was built to unravel only one kind of problem.

To test Jiuzhang, the researchers assigned it a "Gaussian boson sampling" (GBS) task, where the pc calculates the output of a fancy circuit that uses light. That output is expressed as a listing of numbers. (Light is formed of particles referred to as photons, which belongs to a category of particles called bosons.) 

Success is measured in terms of the number of photons detected. Jiuzhaigou, which itself is an optical circuit, detected a maximum of 76 photons in one test and a mean of 43 across several tests.

Its calculation time to provide the list of numbers for every experimental run was about 200 seconds, while the fastest Chinese supercomputer, TaihuLight, would have taken 2.5 billion years to gain an identical result.

That suggests the quantum computer can do GBS 100 trillion times faster than a classical supercomputer.

This doesn't mean that China includes a fully practical quantum computer yet, in keeping with Xinhua. China's device is specialized and mostly useful as a tool for doing GBS. But it is a major milestone on the way there.

New “Flying-V” Plane Burns 20 Percent Less Fuel & Can Carry More Than 300 Passengers

  

Airlines are testing all forms of ways to create planes less of a haul on the environment. Virgin Atlantic recently used recycled waste to power a billboard flight, while Boeing and JetBlue have backed a shot to make hybrid-electric planes. The Netherlands ’ KLM Royal Dutch Airlines is taking a distinct approach.

It just partnered with a university to develop the “Flying-V,” a radical new airplane design that puts passenger seats inside the plane’s wings — and it could decrease the number of fuel needed for flights by a considerable 20 percent.

On Monday, KLM announced plans to collaborate with the Delft University of Technology on the school’s in-development Flying-V airplane design. And it doesn’t just put passengers within the plane’s wings — the fuel tanks and hold also will find a brand new home there.

Based on the researchers’ calculations, the new design should allow the Flying-V to move approximately the identical number of passengers as an Airbus A350 using 20 percent less fuel.

“We’ve been flying these tube and wing airplanes for many years now, but it looks as if the configuration is reaching a plateau in terms of energy efficiency,” TU Delft project leader Roelof Vos told CNN. “The new configuration that we propose realizes some synergy between the fuselage and also the wing. The fuselage actively contributes to the lift of the airplane, and creates less aerodynamic drag.”

Artificial Intelligence Is Now Smart Enough to Know When It Can't Be Trusted

 How might The Terminator have played out if Skynet had decided it probably wasn't responsible enough to carry the keys to the complete US nuclear arsenal? because it seems, scientists could have saved us from such a future AI-led apocalypse, by creating neural networks that know when they're untrustworthy.

These deep learning neural networks are designed to mimic the human brain by weighing up a large number of things in balance with one another, spotting patterns in masses of knowledge that humans haven't got the capacity to analyze.

While Skynet might still be how off, AI is already making decisions in fields that affect human lives like autonomous driving and diagnosing, which means it is important that they are as accurate as possible. to assist towards this goal, this newly created neural network system can generate its confidence level moreover as its predictions.

"We need the flexibility to not only have high-performance models but also to know after we cannot trust those models," says scientist Alexander Amini from the MIT technology and computing Laboratory (CSAIL).

This self-awareness of trustworthiness has been given the name Deep Evidential Regression, and it bases its scoring on the standard of the available data it's to figure with – the more accurate and comprehensive the training data, the more likely it's that future predictions are visiting figure out.

The research team compares it to a self-driving car having different levels of certainty about whether to proceed through a junction or whether to attend, just just in case, if the neural network is a smaller amount confident in its predictions. the boldness rating even includes tips for getting the rating higher (by tweaking the network or the computer file, for instance).

While similar safeguards are built into neural networks before, what sets this one apart is that the speed at which it works, without excessive computing demands – it are often completed in one run through the network, instead of several, with a confidence level outputted at the identical time as a call.

"This idea is vital and applicable broadly," says the man of science Daniela Rus. "It will be wont to assess products that depend upon learned models. By estimating the uncertainty of a learned model, we also learn the way much error to expect from the model, and what missing data could improve the model."

The researchers tested their new system by getting it to gauge depths in several parts of a picture, very like a self-driving car might judge distance. The network compared well to existing setups, while also estimating its own uncertainty – the days it had been least certain were indeed the days it got the depths wrong.

As an additional bonus, the network was able to flag up times when it encountered images outside of its usual remit (so very different to the info it had been trained on) – which is a very medical situation could mean getting a doctor to require a review.

Even if a neural network is true 99 percent of the time, that missing 1 percent can have serious consequences, looking at the scenario. The researchers say they're confident that their new, streamlined trust test can help improve safety in real-time, although the work has not yet been peer-reviewed.

"We're beginning to see plenty more of those [neural network] models trickle out of the science lab and into the important world, into situations that are touching humans with potentially life-threatening consequences," says Amini.

"Any user of the tactic, whether it is a doctor or an individual within the passenger seat of a vehicle, must bear in mind of any risk or uncertainty related to that call."

Passengers Just Took First-Ever Test Ride in Virgin's Hyperloop And Didn't Throw Up

 The Virgin Hyperloop made its first journey carrying passengers Sunday, during a test the corporate claimed represented a serious revolution for the "groundbreaking" technology capable of transporting people at 1,000 kilometers (620 miles) an hour.

The Hyperloop is meant to hold passengers in small pods through a thermionic tube, with proponents arguing it could revolutionize high-speed travel.

Virgin says the Hyperloop is ready to reach top speeds of 1,080 kilometers an hour (671 mph) - projecting a 45-minute journey from la to the port of entry - and can produce no carbon emissions.

But until Sunday the technology, first proposed by eccentric US tech magnate Elon Musk in 2012, had not been tested with people on board.

Two Virgin employees made the 500-meter journey in a very two-person vehicle in precisely 15 seconds at a test site within the Nevada desert.

Passenger Sara Luchian told the BBC she felt the trip was "exhilarating both psychologically and physically", and reported no discomfort.

Once brought into regular use, the pods are going to be ready to transport up to twenty-eight people at a time, Virgin says, with larger models for moving goods also in development.

(Virgin Hyperloop)

Virgin's Hyperloop has raised quite US$400 million, largely from company CEO Richard Branson and also the logistics company DP World, which is owned by the Dubai government. Virgin is one in every variety of companies working to develop the technology.

But while Branson on Sunday hailed the success of the "groundbreaking" Hyperloop, concerns have dogged developers about just how safe the technology would be.

One researcher at Sweden's Royal Institute of Technology argued that the high speeds involved could turn the Hyperloop into a "barf ride."

Bees Robot, A Realistic Alternative To Increase The Production Of Strawberries?

It is an incontrovertible fact that bees are disappearing from our world. There are many reasons for this, including pesticides and poor nutrition, although the causes aren't fully understood.

Most beekeepers need to buy or rent them. These losses are causing a rise in prices. it's estimated that US beekeepers have lost 40% of their honey bee colonies, in line with the US Bee Informed Partnership.

Russian scientists from the Polytechnic University of Tomsk consider an alternative: the employment of robot bees. The researchers shall launch the project in 2019. in step with their plans, the dimensions of the prototypes would be a minimum of seven times larger than the 000 bees, that is, they'd reach the dimensions of the palm of a hand.

For use in greenhouses

As explained by Alexéi Yákovlev, director of the Polytechnic University of Tomsk, artificial bees would be especially beneficial for strawberries and other plants that grow in greenhouses throughout the year. 

"We are progressing to develop bees, algorithms, and software, similarly as optical systems and image recognition methods to realize accurate positioning," explains Yákovlev. The creation of the primary batch of 100 flying robots will cost about 1.4 million dollars.

"Farmers are using bumblebees for pollination in large greenhouses throughout the year," explained Yákovlev. "A bumblebee family costs around $ 500. In winter they fly with infrared, which simulates the warmth of the sun. However, in spring bumblebees can escape, which is an economic loss. " While the robots would work non-stop and never escape.

In any case, artificial bees don't solve the matter of extinction, Yakovlev told Russia Beyond. "We discussed the chance of using robot bees only within the greenhouse, outside their natural habitat."

However, farmers who grow apples, cherries, and other fruits use bees in open spaces. within the US almond producers pay about $ 200 per hive, while blueberry growers spend $ 110 and apple producers pay around $ 70.

In some farms, they're considering the chance of pollinating with alternative species. in step with experts, there are three other important pollinating animals: bats, flies and mosquitoes.

When are these robots visiting fly?

To date, no try to create a man-made alternative to bees has been successful. In 2017, Eijiro Miyako, of the National Institute of Advanced Industrial Science and Technology of Japan, developed a sort of drone aircraft to move pollen among the flowers.

The bottom of the device is roofed with horsehair and covered with a special sticky gel. When this drone flies over a flower, the pollen grains adhere slightly to the gel so release into the subsequent flower.

A series of tests were drained which the drone pollinated Japanese lilies. The soft and versatile hairs of the animals didn't damage the stamens or the pistils after they fell on the flowers.

Robot trained in a game-like simulation performs better in real life

 Training a robot during a simulation that permits it to recollect the way to get out of sticky situations lets it traverse difficult terrain more smoothly in the real world.

Joonho Lee at ETH Zurich in Switzerland and his colleagues trained a neural network algorithm, which was designed to manage a four-legged robot, during a simulated environment just like a computer game, which was stuffed with hills, steps, and stairs.

The researchers told the algorithm which direction it should be trying to maneuver in, additionally limiting how quickly it could turn, reflecting the capabilities of the 000 robots. They then started the algorithm making random movements within the simulation, rewarding it for getting the proper way, and penalizing it otherwise. By accumulating rewards, the neural network learned the way to give way a spread of terrain.

Currently, most robots respond in real-time to measurements of their surroundings using preprogrammed reactions, encountering every problem for the primary time. A neural network allows the robot to learn from its mistakes, and by first training the algorithm in an exceeding simulation, the team is ready to limit risks and reduce costs.

“We can’t bring it to the important terrains we wish to coach it on because it'd damage the robot, which is extremely expensive,” says Lee.

The researchers initially used a tiny low neural network that was preprogrammed with knowledge about the simulated environment, enabling the algorithm to find out quickly by taking inputs from virtual sensors and remembering them. They then transferred this data to an outsized network accustomed control the important robot.

Using this experience, the robot was ready to move 0.452 meters per second on mossy land – quite twice as fast because it can move with its default programming.

Facebook AI can translate directly between any of 100 languages

 Facebook has developed a man-made intelligence capable of accurately translating between any pair of 100 languages without wishing on first translating to English, as many existing systems do.

The AI outperforms such systems by 10 points on a 100-point scale utilized by academics to automatically evaluate the standard of machine translations. Translations produced by the model were also assessed by humans, who scored it as around 90 per cent accurate.

Facebook’s system was trained on a knowledge set of seven.5 billion sentence pairs gathered from the online across 100 languages, though not all the languages had an equal number of sentence pairs. “What I actually was curious about was operation English as a middle man. Globally there are many regions where they speak two languages that aren’t English,” says Angela Fan of Facebook AI, who led the work.

The model was trained by specializing in languages that are commonly translated to and from one another, grouping languages into 14 separate collections supported geography and cultural similarities. This was done to make sure top quality translation of more commonly used connections and to coach the model more accurately.

For some language pairs, the new system shows significant improvements over existing translation quality. for instance, translating from Spanish to Portuguese is especially strong because Spanish is that the second-most spoken tongue worldwide, meaning the researchers had access to an oversized amount of coaching data. Translation between English and Belarusian also improved over existing efforts because the AI learns from translating Russian, which shares similarities with Belarusian.

While the system isn’t yet in use on the social network site, Facebook plans to place it to figure soon to handle the 20 billion translations made each day when people click “Translate” on posts written in additional than 160 languages. Future work is done on other languages, says Fan, “especially for languages where we don’t have lots of information, like South-East Asian and African languages”.

The work “breaks faraway from the English-centric models and tries to create more diverse multilingual ones”, says Sheila Castilho of the ADAPT Centre at Dublin University, Ireland. “That’s refreshing.” But, says Castilho, the human assessments only checked out a tiny low fraction of examples, making it hard to understand if this can be an accurate judgement of how the AI performs.

She also worries that the evaluation was done by bilingual volunteers, instead of professional translators. “Non-professionals lack knowledge of translation so may not notice subtle differences that make one translation better than another,” she says.

Her colleague at the ADAPT Centre, Andy Way, suggests Facebook isn’t making a good comparison with state-of-the-art translation systems. “Their claim to possess such an oversized improvement over ‘English-centric’ models could be a bit empty, as most of the time, people don’t do that anymore,” he says. Facebook disagrees, saying translation through English continues to be commonplace.

Microwaving plastic waste can generate clean hydrogen

 Chemists have used microwaves to convert plastic bags, milk bottles, and other supermarket packaging into a clean source of hydrogen.

Plastic waste can already be converted to hydrogen using other methods, and commercial facilities are being developed to rework the plastic. However, a brand new approach holds the promise of being quicker and fewer energy-intensive.

Peter Edwards at the University of Oxford says he and his colleagues wanted to “confront the grim reality” of plastic waste, with the united kingdom alone producing 1.5 million tonnes every year. because the density of hydrogen in plastic bags is about 14 percent by weight, plastic offers a possible new source for countries eyeing cleanly produced hydrogen to tackle temperature change.

Most existing approaches involve first using very high temperatures of quite 750°C to decompose plastic into syngas, a mix of hydrogen and carbon monoxide gas, then employing a second step to filter the hydrogen.

Edwards and his team instead broke the plastic into small pieces with a kitchen blender and mixed it with a catalyst of iron oxide and alumina. When blasted with a microwave generator at 1000 watts, the catalyst created hot spots within the plastic and stripped out the hydrogen – recovering 97 percent of the gas within the plastic within seconds.

The solid material left over was almost exclusively carbon nanotubes. The single-step approach has the advantage of just heating the catalyst, not all of the plastic, leading to less energy use, because the plastic doesn't absorb microwaves.

The results hold out “an attractive potential solution for plastic waste”, says Edwards. Although only done at a little scale, using about 300 grams of plastic for every test, larger experiments are already being planned.