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[inhandnetworks-blog]

New Fabricatio Industrial VPN router  n Method Improves Efficiency of Perovskite Solar Cells

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A new fabrication method enabled researchers to make larger perovskite cells with few defects, resulting in higher efficiency. Image: Brown University/NREL

Scientists from Brown University and the National Renewable Energy Laboratory have developed a method for making perovskite cells larger while maintaining efficiency.

Using a newly developed fabrication method, a research team has attained better than a 15-percent energy conversion efficiency from perovskite solar cells larger than one square centimeter area. The researchers, from Brown University and the National Renewable Energy Lab (NREL), have reported their findings in the journal Advanced Materials.

Perovskites, materials with a particular crystalline structure, have caused quite a buzz in the solar energy world. Perovskite solar cells are relatively cheap to make, and the efficiency with which they can convert sunlight into electricity has been increasing rapidly in recent years. Researchers have reported efficiency in perovskite cells of higher than 20 percent, which rivals traditional silicon cells. Those high efficiency ratings, however, have been achieved using cells only a tenth of a square centimeter — fine for lab testing, but too small to be used in a solar panel.

“The use of tiny cells for efficiency testing has prompted some to question comparison of perovskite solar cells with other established photovoltaic technologies,” said Nitin Padture, professor of engineering at Brown, director of Brown’s Institute for Molecular and Nanoscale Innovation, and one of the senior authors of the new research. “But here we have shown that it is feasible to obtain 15-percent efficiency on cells larger than a square centimeter through improved processing. This is  m2m vpn  real progress.”

Maintaining high efficiency on larger perovskite cells has proved to be a challenge, Padture says. “The problem with perovskite has been that when you try to make larger films using traditional methods, you get defects in the film that decrease efficiency.”

The fabrication process that the Brown and NREL researchers reported in this latest paper builds on a previously reported method developed by Yuanyuan Zhou, a graduate student in Padture’s lab. Perovskite precursors are dissolved in a solvent and coated onto a substrate. Then the substrate is bathed in a second solvent (called anti-solvent) that selectively grabs the precursor-solvent and whisks it away. What’s left is an ultra-smooth film of perovskite crystals.

In this new study Zhou and Mengjin Yang, a postdoctoral researcher at NREL, developed a trick to grow the perovskite crystals to a larger size. The trick is to add excess organic precursor that initially “glues” the small perovskite crystals and helps them merge into larger ones during a heat-treatment, which then bakes away the excess precursor.

“The full coverage and uniformity over a large area come from the solvent method,” Padture said. “Once we have that coverage, then we increase the size of the crystals. That gi Industrial 3G Router  ves us a film with fewer defects and higher efficiency.” The 15- ethernet  percent efficiency reached in this latest work is a good start, Padture said, but there’s still room to improve. Ultimately, he would like to reach 20 to 25 percent in large-area cells, and he thinks that mark could be within reach using this method or a similar one.

Padture and colleagues at the University of Nebraska–Lincoln were recently awarded a $4-million grant by the National Science Foundation to expand their perovskite research.

Other authors on the paper were Yining Zeng, Chun-Sheng Jiang, and Kai Zhu of NREL. The work was supported by the U.S. Department of Energy (DE-AC36-08-GO28308 and DE-FOA-0000990) and the National Science Foundation (DMR-1305913).

Publication: Mengjin Yang, etla., “Square-Centimeter Solution-Processed Planar CH3NH3PbI3 Perovskite Solar Cells with Efficiency Exceeding 15%,” Advanced Materials, 2015; DOI: 10.1002/adma.201502586

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[inhandnetworks-blog]

Halide, A New and Improved Programming Language for Image cashless payment   Processing Software

www.inhandnetworks.com

An image undergoing basic processing steps; exposure adjustments, then noise reduction, to arrive at the final image (bottom). Graphic: Christine Daniloff

In an effort to improve image-processing software, a team of researchers developed a new programming language called Halide, which is easier to read, write and revise and is significantly faster because Halide automates code-optimization procedures.

Image-processing software is a hot commodity: Just look at Instagram, a company built around image processing that Facebook is trying to buy for a billion dollars. Image processing is also going mobile, as more and more people are sending cellphone photos directly to the Web, without transferring them to a computer first.

At the same time, digital-photo files are getting so big that, without a lot of clever software engineering, processing them would take a painfully long time on a desktop computer, let alone a cellphone. Unfortunately, the tricks that engineers use to speed up their image-processing algorithms make their code almost unreadable, and rarely reusable. Adding a new function to an image-processing program, or modifying it to run on a different device, often requires rethinking an industrial IoT Gateway  d revising it from top to bottom.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) aim to change that, with a new programming language called Halide. Not only are Halide programs easier to read, write and revise than image-processing programs written in a conventional language, but because Halide automates code-optimization procedures that would ordinarily take hours to perform by hand, they’re also significantly faster.

In 4g-lte   tests, the MIT researchers used Halide to rewrite several common image-processing algorithms whose performance had already been optimized by seasoned programmers. The Halide versions were typically about one-third as long but offered significant performance gains — two-, three-, or even six-fold speedups. In one instance, the Halide program was actually longer than the original — but the speedup was 70-fold.

Jonathan Ragan-Kelley, a graduate student in the Department of Electrical Engineering and Computer Science, and Andrew Adams, a CSAIL postdoc, led the development of Halide, and they’ve released the code online. At this month’s Siggraph, the premier graphics conference, they’ll present a paper on Halide, which they co-wrote with MIT computer science professors Saman Amarasinghe and Fredo Durand and with colleagues at Adobe and Stanford University.

Parallel pipelines

One reason that image processing is so computationally intensive is that it generally requires a succession of discrete operations. After light strikes the sensor in a cellphone camera, the phone combs through the image data for values that indicate malfunctioning sensor pixels and corrects them. Then it correlates the readings from pixels sensitive to different colors to deduce the actual colors of image regions. Then it does some color correction, and then some contrast adjustment, to make the image colors better correspond to what the human eye sees. At this point, the phone has done so much processing that it takes another pass through the data to clean it up.

And that’s just to display the image on the phone screen. Software that does anything more complicated, like removing red eye, or softening shadows, or boosting color saturation — or making the image look like an old Polaroid photo — introduces still more layers of procePredictive Grid Analyticsssing. Moreover, high-level modifications often require the software to go back and recompute prior stages in the pipeline.

In today’s multicore chips, distributing different segments of the image to cores working in parallel can make image processing more efficient. But the way parallel processing is usually done, after each step in the image-processing pipeline, the cores would send the results of their computations back to main memory. Because data transfer is much slower than computation, this can eat up all the performance gains offered by parallelization.

So software engineers try to keep the individual cores busy for as long as possible before they have to ship their results to memory. That means that the cores have to execute several steps in the processing pipeline on their separate chunks of data without aggregating their results. Keeping track of all the dependencies between pixels being processed on separate cores is what makes the code for efficient image processors so complicated. Moreover, the trade-offs between the number of cores, the processing power of the cores, the amount of local memory available to each core, and the time it takes to move data off-core varies from machine to machine, so a program optimized for one device may offer no speed advantages on a different one.

Divide and conquer

Halide doesn’t spare the programmer from thinking about how to parallelize efficiently on particular machines, but it splits that problem off from the description of the image-processing algorithms. A Halide program has two sections: one for the algorithms, and one for the processing “schedule.” The schedule can specify the size and shape of the image chunks that each core needs to process at each step in the pipeline, and it can specify data dependencies — for instance, that steps being executed on particular cores will need access to the results of previous steps on different cores. Once the schedule is drawn up, however, Halide handles all the accounting automatically.

A programmer who wants to export a program to a different machine just changes the schedule, not the algorithm description. A programmer who wants to add a new processing step to the pipeline just plugs in a description of the new procedure, without having to modify the existing ones. (A new step in the pipeline will require a corresponding specification in the schedule, however.)

“When you have the idea that you might want to parallelize something a certain way or use stages a certain way, when writing that manually, it’s really hard to express that idea correctly,” Ragan-Kelley says. “If you have a new optimization idea that you want to apply, chances are you’re going to spend three days debugging it because you’ve broken it in the process. With this, you change one line that expresses that idea, and it synthesizes the correct thing.”

Although Halide programs are simpler to write and to read than ordinary image-processing programs, because the scheduling is handled automatically, they still frequently offer performance gains over even the most carefully hand-engineered code. Moreover, Halide code is so easy to modify that programmers could simply experiment with half-baked ideas to see if they improve performance.

“You can just flail around and try different things at random, and you’ll often find something really good,” Adams says. “Only much later, when you’ve thought about it very hard, will you figure out why it’s good.”

“There’s a lot of excitement about using a domain-specific language like the one they chose to do, but there really haven’t been a lot of success stories,” says John Owens, an associate professor of electrical and computer engineering at the University of California at Davis. “And I think this is going to turn out to be a terrific success story. It really has all the pieces you want from a completed system, and it’s in a really important application domain.”

“I can also see it inspiring a lot of future work,” Owens adds. “I think this is going to be a really high-impact project.”

Image: Christine Daniloff

Reprinted with permission of MIT News

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