Monday, August 29th, 2016

Time	Event
12:00a	New solar cell is more efficient, costs less than its counterparts The following is adapted from a Masdar Institute article by Erica Solomon. The cost of solar power is beginning to reach price parity with cheaper fossil fuel-based electricity in many parts of the world, yet the clean energy source still accounts for just slightly more than 1 percent of the world’s electricity mix. Solar, or photovoltaic (PV), cells, which convert sunlight into electrical energy, have a large role to play in boosting solar power generation globally, but researchers still face limitations to scaling up this technology. For example, developing very high-efficiency solar cells that can convert a significant amount of sunlight into usable electrical energy at very low costs remains a significant challenge. A team of researchers from MIT and the Masdar Institute of Science and Technology may have found a way around this seemingly intractable tradeoff between efficiency and cost. The team has developed a new solar cell that combines two different layers of sunlight-absorbing material to harvest a broader range of the sun’s energy. The researchers call the device a “step cell,” because the two layers are arranged in a stepwise fashion, with the lower layer jutting out beneath the upper layer, in order to expose both layers to incoming sunlight. Such layered, or “multijunction,” solar cells are typically expensive to manufacture, but the researchers also used a novel, low-cost manufacturing process for their step cell. The team’s step-cell concept can reach theoretical efficiencies above 40 percent and estimated practical efficiencies of 35 percent, prompting the team’s principal investigators — Masdar Institute’s Ammar Nayfeh, associate professor of electrical engineering and computer science, and MIT’s Eugene Fitzgerald, the Merton C. Flemings-SMA Professor of Materials Science and Engineering — to plan a startup company to commercialize the promising solar cell. Fitzgerald, who has launched several startups, including AmberWave Systems Corporation, Paradigm Research LLC, and 4Power LLC, thinks the step cells might be ready for the PV market within the next year or two. The team presented its initial proof-of-concept step cell in June at the 43rd IEEE Photovoltaic Specialists Conference in Portland, Oregon. The researchers have also reported their findings at the 40th and 42nd annual conferences, and in the Journal of Applied Physics and IEEE Journal of Photovoltaics.  Beyond silicon Traditional silicon crystalline solar cells, which have been touted as the industry’s gold standard in terms of efficiency for over a decade, are relatively cheap to manufacture, but they are not very efficient at converting sunlight into electricity. On average, solar panels made from silicon-based solar cells convert between 15 and 20 percent of the sun’s energy into usable electricity. Silicon’s low sunlight-to-electrical energy efficiency is partially due to a property known as its bandgap, which prevents the semiconductor from efficiently converting higher-energy photons, such as those emitted by blue, green, and yellow light waves, into electrical energy. Instead, only the lower-energy photons, such as those emitted by the longer red light waves, are efficiently converted into electricity. To harness more of the sun’s higher-energy photons, scientists have explored different semiconductor materials, such as gallium arsenide and gallium phosphide. While these semiconductors have reached higher efficiencies than silicon, the highest-efficiency solar cells have been made by layering different semiconductor materials on top of each other and fine-tuning them so that each can absorb a different slice of the electromagnetic spectrum. These layered solar cells can reach theoretical efficiencies upward of 50 percent, but their very high manufacturing costs have relegated their use to niche applications, such as on satellites, where high costs are less important than low weight and high efficiency. The Masdar Institute-MIT step cell, in contrast, can be manufactured at a fraction of the cost because a key component is fabricated on a substrate that can be reused. The device may thus help boost commercial applications of high-efficiency, multijunction solar cells at the industrial level. Steps to success The step cell is made by layering a gallium arsenide phosphide-based solar cell, consisting of a semiconductor material that absorbs and efficiently converts higher-energy photons, on a low-cost silicon solar cell. The silicon layer is exposed, appearing like a bottom step. This intentional step design allows the top gallium arsenide phosphide (GaAsP) layer to absorb the high-energy photons (from blue, green, and yellow light) leaving the bottom silicon layer free to absorb lower-energy photons (from red light) not only transmitted through top layers but also from the entire visible light spectrum. “We realized that when the top gallium arsenide phosphide layer completely covered the bottom silicon layer, the lower-energy photons were absorbed by the silicon germanium — the substrate on which the gallium arsenide phosphide is grown — and thus the solar cell had a much lower efficiency,” explains Sabina Abdul Hadi, a PhD student at Masdar Institute whose doctoral dissertation provided the foundational research for the step-cell. “By etching away the top layer and exposing some of the silicon layer, we were able to increase the efficiency considerably.” Working under Nayfeh’s supervision, Abdul Hadi conducted simulations based on experimental results to determine the optimal levels and geometrical configuration of the GaAsP layer on silicon to yield the highest efficiencies. Her findings resulted in the team’s initial proof-of-concept solar cell. Abdul Hadi will continue supporting the step cell’s technological development as a post-doctoral researcher at Masdar Institute. On the MIT side, the team developed the GaAsP, which they did by growing the semiconductor alloy on a substrate made of silicon germanium (SiGe). “Gallium arsenide phosphide cannot be grown directly on silicon, because its crystal lattices differ considerably from silicon’s, so the silicon crystals become degraded. That’s why we grew the gallium arsenide phosphide on the silicon germanium — it provides a more stable base,” explains Nayfeh. The problem with the silicon germanium under the GaAsP layer is that SiGe absorbs the lower-energy light waves before it reaches the bottom silicon layer, and SiGe does not convert these low-energy light waves into current. “To get around the optical problem posed by the silicon germanium, we developed the idea of the step cell, which allows us to leverage the different energy absorption bands of gallium arsenide phosphate and silicon,” says Nayfeh. The step cell concept led to an improved cell in which the SiGe template is removed and re-used, creating a solar cell in which GaAsP cell tiles are directly on top of a silicon cell.  The step-cell allows for SiGe reuse since the GaAsP cell tiles can be under-cut during the transfer process.  Explaining the future low-cost fabrication process, Fitzgerald says: “We grew the gallium arsenide phosphide on top of the silicon germanium, patterned it in the optimized geometric configuration, and bonded it to a silicon cell. Then we etched through the patterned channels and lifted off the silicon germanium alloys on silicon. What remains then, is a high-efficiency tandem solar cell and a silicon germanium template, ready to be reused.” Because the tandem cell is bonded together, rather than created as a monolithic solar cell (where all layers are grown onto a single substrate), the SiGe can be removed and reused repeatedly, which significantly reduces the manufacturing costs. “Adding that one layer of the gallium arsenide phosphide can really boost efficiency of the solar cell but because of the unique ability to etch away the silicon germanium and reuse it, the cost is kept low because you can amortize that silicon germanium cost over the course of manufacturing many cells,” Fitzgerald adds. Filling a market gap Fitzgerald believes the step cell fits well in the existing gap of the solar PV market, between the super high-efficiency and low-efficiency industrial applications. And as volume increases in this market gap, the manufacturing costs should be driven down even further over time. This project began as one of nine Masdar Institute-MIT Flagship Research Projects, which are high-potential projects involving faculty and students from both universities. The MIT and Masdar Institute Cooperative Program helped launch the Masdar Institute in 2007. Research collaborations between the two institutes address global energy and sustainability issues, and seek to develop research and development capabilities in Abu Dhabi. “This research project highlights the valuable role that research and international collaboration plays in developing a commercially-relevant technology-based innovation, and it is a perfect demonstration of how a research idea can transform into an entrepreneurial reality,” says Nayfeh. (Comment on this)
11:00a	Pushing through sand For those of you who take sandcastle building very seriously, listen up: MIT engineers now say you can trust a very simple equation to calculate the force required to push a shovel — and any other “intruder”— through sand. The team also found that the same concept, known as the resistive force theory, can generate useful equations  for cohesive materials like muds. Aside from calculating the elbow grease needed to carve out a beachside moat, the researchers say the equation can be used to optimize the way vehicles drive over gravel and soil, such as rovers navigating the Martian landscape. It can also help illuminate the ways in which animals such as lizards and worms burrow through earth. Resistive force theory (RFT) is not new and in fact was proposed in the 1950s to describe the way in which objects move through viscous fluids such as water (on small scales) and honey. It was only much later that scientists thought to apply the same idea to granular material such as sand; they found the theory predicted the force required to move objects through grains even better than its analog for fluids. The reason for this has been a mystery, particularly since predicting granular versus fluid behavior is notoriously difficult. Ken Kamrin, associate professor of mechanical engineering at MIT, says scientists have regarded granular RFT as “somewhat like magic,” unsure of what makes the concept work so spot-on for sand. In a paper published today in Nature Materials, Kamrin, along with former MIT postdoc Hesam Askari, have essentially solved this mystery. They report that they have identified a mechanical explanation for why the equation works so well for granular materials. Now, they say that scientists have reason to trust the resistive force theory to give accurate force estimates through sand, and even pastier materials like mud and gels. “People observed this concept worked but didn’t know why, and that’s really shaky ground for scientists — is it just a coincidence?” Kamrin says. “Now we can explain the backbone of the granular resistive force theory, so you can close your eyes and have confidence that it’s going to work. It gives us some fleeting hope that we might be able to design something that more efficiently moves, swims, or drives over sand.” An intrusion problem Granular RFT works like this: Imagine you are working with a shovel, buried at a certain depth in the sand. You want to know how much to push on the shovel, to move it in a particular direction. To answer this question, you first need to do some experiments with a small, square plate, made from the same material as your shovel. Push the plate through sand, starting from all possible orientations and moving in all possible directions. During each test, measure the amount of force it takes to move the plate. According to the theory, you can think of the shovel as an assemblage of similar small plates. To estimate the force required to move the shovel, simply imagine each plate is on its own and add up all the tiny, individual forces of each plate, at each specific location and orientation along the shovel. As it turns out, this theory works remarkably well for granular materials, and somewhat well in fluids. “If something is working well, it would be nice to know why,” Kamrin says. “There may be a large set of problems you might solve if you knew why the intrusion problem is so easy to figure out in sand.” A push and a shove Kamrin set out to write the simplest equation he could think of that would represent granular flows, to see whether the equation, and the mechanical relationships it defines, could also reproduce the simplified picture assumed in resistive force theory. If so, he reasoned, the equation — also called a continuum model — could give a mechanical explanation for why RFT works, and furthermore, validate the theory. The equation he came up with is a variant of a standard model, based on Coulomb’s yield criterion, a simple criterion that determines whether granular material will flow or not. Imagine a collection of sand compressed between your hands. Coulomb’s equation states that in order to slide one hand against the other the shear stress — akin to the force applied to slide your hands — divided by the surrounding pressure — squeezing the sand together — must equal something called the friction coefficient. If this ratio reaches the friction coefficient (determined by the sand’s properties), your hand will move.  Kamrin added one more ingredient to the equation: a separation rule, to account for the fact that sand, in general, does not stick together. For example, if you move a shovel through sand, it will create a temporary hole behind the shovel that is immediately refilled with in-falling sand — a realistic phenomenon that Kamrin says is important to include, to accurately represent sand flow, particularly in “intrusion” scenarios such as pushing a shovel through sand. Kamrin and Askari applied their continuum model in finite element simulations in which they simulated a simple plate moving through granular media in many ways. The simulation was designed to mimic actual experiments performed by others. They found that both the flow of the grains and the force against the plate matched what others had observed in their experiments. The team then simulated more complex objects, such as a circle and a diamond, moving through sand, using first their continuum model and then RFT with their previous plate simulations serving as the RFT inputs. Both simulations produced nearly identical results and predicted the same force value needed to move both objects. When the researchers pushed the simulation to model three-dimensional objects, both the continuum model and RFT again generated the same answers. “The agreement is unbelievably good,” Kamrin says. “It turns out RFT happens to work really well, thanks to an interesting property in the Coulomb continuum model.” “Out of a sticky situation” Interestingly, this simplification does less well in predicting the force applied to an object through fluid. When Kamrin and Askari modeled an object — in this case, a simple garden hoe — through fluid, the force from the viscous flow equations was inherently incompatible with the sum of forces from separate small plates.  When the material model was switched to the granular model, the total force exactly matched what a sum of small independent plate forces would give. “In some sense, this is a litmus test,” Kamrin says. “In the end, it proves the granular continuum model perfectly agrees with the resistive force theory in a class of representative problems.” To see if RFT could make accurate predictions in any other material besides grains, the researchers “went through the Rolodex of materials that have modeling equations,” and found using a similar test that indeed, RFT could also apply to certain cohesive materials like pastes, gels, and mud. Kamrin says now scientists can rely on RFT to help solve many traction-related problems. But could the equation also help one get out of, say, quicksand? “Let’s put it this way: Either way, you need to do a bit of work to figure out how to push yourself out of quicksand,” Kamrin says. “But in the right circumstances RFT divides the amount of work by a whole lot. You don’t have to solve differential equations anymore. Just give me a couple charts and a piece of paper and a pen, and I can calculate my way out of a sticky situation.” “Now that we know that RFT is a consequence of plasticity, scaling relations can be developed to understand pros and cons of different vehicle running gear and animal appendages, like, how do large and small tires compare? How do flipper-like feet versus long skinny feet compare? How do different body shapes affect sand-swimming performance?” says Daniel Goldman, associate professor of physics at Georgia Tech, who was not involved in the research. “There are still many aspects of these interactions that are not yet tested against RFT, like situations when animals step into material that their feet have previously disturbed, but Ken's work can lead to predictions that we can test.” This research was supported, in part, by the Army Research Office. (Comment on this)
2:59p	Inferring urban travel patterns from cellphone data In making decisions about infrastructure development and resource allocation, city planners rely on models of how people move through their cities, on foot, in cars, and on public transportation. Those models are largely based on surveys of residents’ travel habits. But conducting surveys and analyzing their results is costly and time consuming: A city might go more than a decade between surveys. And even a broad survey will cover only a tiny fraction of a city’s population. In the latest issue of the Proceedings of the National Academy of Sciences, researchers from MIT and Ford Motor Company describe a new computational system that uses cellphone location data to infer urban mobility patterns. Applying the system to six weeks of data from residents of the Boston area, the researchers were able to quickly assemble the kind of model of urban mobility patterns that typically takes years to build. The system holds the promise of not only more accurate and timely data about urban mobility but the ability to quickly determine whether particular attempts to address cities’ transportation needs are working. “In the U.S., every metropolitan area has an MPO, which is a metropolitan planning organization, and their main job is to use travel surveys to derive the travel demand model, which is their baseline for predicting and forecasting travel demand to build infrastructure,” says Shan Jiang, a postdoc in the Human Mobility and Networks Lab in MIT’s Department of Civil and Environmental Engineering and first author on the new paper. “So our method and model could be the next generation of tools for the planners to plan for the next generation of infrastructure.” To validate their new system, the researchers compared the model it generated to the model currently used by Boston’s MPO. The two models accorded very well. “The great advantage of our framework is that it learns mobility features from a large number of users, without having to ask them directly about their mobility choices,” says Marta González, an associate professor of civil and environmental engineering (CEE) at MIT and senior author on the paper. “Based on that, we create individual models to estimate complete daily trajectories of the vast majority of mobile-phone users. Likely, in time, we will see that this brings the comparative advantage of making urban transportation planning faster and smarter and even allows directly communicating recommendations to device users.” Joining Jiang and González on the paper are Daniele Veneziano, a professor of CEE at MIT; Yingxiang Yang, a graduate student in CEE; Siddharth Gupta, a research assistant in the Human Mobility and Networks Lab, which González leads; and Shounak Athavale, an information technology manager at Ford Motor’s Palo Alto Research and Innovation Center. Model building The Boston MPO’s practices are fairly typical of a major city’s. Boston conducted one urban mobility survey in 1994 and another in 2010. Its current mobility model, however, still uses the data from 1994. That’s because it’s taken the intervening six years simply to sort through all the data collected in 2010. Only now has the work of organizing that data into a predictive model begun. The 2010 survey asked each of 25,000 residents of the Boston area to keep a travel diary for a single day. From those diaries, combined with census data and information from traffic sensors, the MPO attempts to model the movements of 3.5 million residents of the greater Boston area. While the MIT researchers had access to much more data — six weeks’ worth from each of 1.92 million residents — it was less complete. Cellphone records report only the locations at which users place calls or access the Internet. The researchers had to discard 25 percent of their data because it was too scanty. From the rest, however, their algorithm was able to infer patterns of activity that recurred over the course of the six-week period. To piece together a picture of a cellphone user’s day, the algorithm makes a few assumptions. One is that the location from which a user departs in the morning and to which she returns at night is her home. Another is that the location of the longest recurring stays during weekday daytime hours indicates the user’s workplace. Finally, the algorithm assumes that the lengths of most people’s workdays accord with national averages. For instance, if a given user makes phone calls from work only between the hours of 12 p.m. and 2 p.m., the system does not interpret that as evidence of a two-hour workday — unless that interpretation is corroborated by other data, such as regular calls from home at 11:30 a.m. and 2:30 p.m. The estimates of workday length are probabilistic, however; the model doesn’t assume that people arrive at work at exactly the same time every morning. Any locations other than work and home are treated alike. From the available data, the system builds a probabilistic mobility model for each user, breaking every day of the week into 10-minute increments. For each increment, the model indicates the likeliness of a location change, possible destinations, and amount of time likely to be spent at each of those destinations. The system then generalizes those probabilities across communities, on the basis of census data, and deduces cumulative traffic flows from the resulting probability map. (Comment on this)

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