From SLAC Lab: “X-Ray Experiment Confirms Theoretical Model for Making New Materials”

From SLAC Lab

July 2, 2018
Glennda Chui

In an experiment at SLAC, scientists loaded ingredients for making a material into a thin glass tube and used X-rays (top left) to observe the phases it went through as it was forming (shown in bubbles). The experiment verified theoretical predictions made by scientists at Berkeley Lab with the help of supercomputers (right). (Greg Stewart/SLAC National Accelerator Laboratory)

By observing changes in materials as they’re being synthesized, scientists hope to learn how they form and come up with recipes for making the materials they need for next-gen energy technologies.

Over the last decade, scientists have used supercomputers and advanced simulation software to predict hundreds of new materials with exciting properties for next-generation energy technologies.

Now they need to figure out how to make them.

To predict the best recipe for making a material, they first need a better understanding of how it forms, including all the intermediate phases it goes through along the way – some of which may be useful in their own right.

Now experiments at the Department of Energy’s SLAC National Accelerator Laboratory have confirmed the predictive power of a new computational approach to materials synthesis. Researchers say that this approach, developed at the DOE’s Lawrence Berkeley National Laboratory, could streamline the creation of novel materials for solar cells, batteries and other sustainable technologies.

“In the last 10 years, computational scientists have gotten really good at predicting the properties of new materials, but not so good at telling experimentalists like me how to make them,” said Michael Toney, a distinguished staff scientist at SLAC. “The theoretical framework developed at Berkeley Lab can help guide us in thinking about ways to synthesize and test these promising materials.”

This team described their findings June 29 in Nature Communications.

Metastable Materials

“Most theoretical approaches are great for predicting the endpoints of a reaction – what chemicals you start with, and what material you get at the end,” said study co-author Laura Schelhas, an associate staff scientist with SLAC’s Applied Energy Program. “But other interesting materials that form along the reaction pathway are often overlooked.”

These intermediate materials are said to exist in a state of metastability.

“Materials always want to be in their lowest-energy phase or ground state,” Schelhas explained. “Materials in a metastable state are higher in energy and will eventually transition to the more stable ground state. A diamond, for example, is a metastable state of carbon that will revert to its ground state, graphite, over millions of years.”

During synthesis, materials can crystallize into a series of metastable phases – some lasting only a few minutes, others persisting for hours. Some of these phases have properties that are potentially useful for technological applications. Others may block the formation of a material you want to make. Scientists want to isolate the useful phases and avoid creating the undesirable ones.

Co-authors Wenhao Sun and Gerbrand Ceder at Berkeley Lab and Daniil Kitchaev of the Massachusetts Institute of Technology recently developed a theoretical model to predict which metastable phases a material will form during synthesis.

“The key insight is to consider influences other than temperature and pressure that can affect a material’s formation,” Sun said. “For example, at a very small scale, surface energy is important, and impurities that materials take up from the surrounding environment can stabilize some types of crystalline structures. We developed a theory to quantify how these factors govern the formation of metastable phases, and then worked with SLAC to design an experiment to test it.”

The experiment, conducted at SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL), focused on manganese oxide, a compound whose formation can involve a variety of metastable crystalline structures. Some of these metastable structures are useful for battery applications or catalysis.

SLAC/SSRL

Schematic representation of remnant metastability in a crystallization pathway. a Free-energy of three phases (supersaturated solution (gray), M (green), S (blue)) as a function of the surface-area-to-volume ratio, 1/R (R is a particle radius). The gray line corresponds to the free-energy of a supersaturated solution, green is a metastable phase M that is size-stabilized by a low surface energy (given by the slope), and blue is the bulk equilibrium phase S, with high surface energy. b Phase diagram in the 1/R axis created from the projection of lowest free-energy phases. c A multistage crystallization pathway (red arrow in a ) proceeds downhill in energy, but phase transformations are limited by nucleation. Crystal growth of M prior to the induction of S means M can grow into a size-regime where phase M is metastable. S will then nucleate, and quickly grow by consuming M via dissolution-reprecipitation. The characteristic length scale of size-driven phase transitions lies in the 2 nm–50 nm range. Nature Communications

“Although manganese oxide has been widely studied, we still don’t have a good understanding of how to make specific metastable phases of the material,” Toney said. “Figuring out why certain recipes favor certain metastable structures will help us predict recipes for synthesizing not just this material, but others as well.”

Theory vs. Experiment

Sun and Schelhas designed an experiment to carefully manipulate a single ingredient in a recipe for making manganese oxide and track its effect on the formation of metastable crystals.

SLAC scientists led by postdoctoral researcher Bor-Rong Chen used powerful X-ray beams at SSRL to observe the chemical reaction as it happened.

“It’s pretty simple,” Schelhas said. “We load up manganese salts and other reaction materials into a small glass capillary, seal it and heat it. Then we shoot X-rays through the capillary while the reaction is occurring and watch the signal that reflects off the crystals. That signal allows us to determine the atomic structure of each metastable phase as it forms.”

At first, the metastable phases identified by X-ray diffraction didn’t seem to match the theoretical predictions, Chen said.

“We worked with the theorists at Berkeley Lab to retool the model,” she said, “and arrived at some explanations for why certain metastable phases might be skipped in a reaction, or why they might persist longer than we anticipated.”

To continue developing their understanding of synthesis, the researchers plan to conduct experiments on more complicated materials.

“This work marks only the initial steps in a much longer journey towards a predictive theory of materials synthesis,” Sun said. “Our goal is to build a powerful toolkit to design recipes for making exactly the materials we want.”

The team also found that they could stop the reaction at the point where a metastable material has formed, which will make it possible to test those materials for desirable properties in future studies, Schelhas said.

“We’re starting to push science into a new space in terms of understanding how you go about synthesis,” she added. “Predictive models have the potential to profoundly alter the way that materials design is done. That could greatly speed up the adoption of more advanced materials in areas like photovoltaics, batteries, thermoelectrics and a whole host of other sustainable technologies.”

Other co-authors of the study are from the Colorado School of Mines and the DOE’s National Renewable Energy Laboratory.

SSRL is a DOE Office of Science user facility. Funding for this work came from the Center for Next Generation of Materials Design, an Energy Frontier Research Center led by DOE’s National Renewable Energy Laboratory and funded by the DOE Office of Science.

See the full article here .

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SLAC is a multi-program laboratory exploring frontier questions in photon science, astrophysics, particle physics and accelerator research. Located in Menlo Park, California, SLAC is operated by Stanford University for the DOE’s Office of Science.

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From MIT: “How to bend and stretch a diamond”

MIT News

April 19, 2018
David L. Chandler

(Stellar-Serbia/iStock) via Science Alert

This scanning electron microscope image shows ultrafine diamond needles (cone shapes rising from bottom) being pushed on by a diamond tip (dark shape at top). These images reveal that the diamond needles can bend as much as 9 percent and still return to their original shape. Courtesy of the researchers.

The brittle material can turn flexible when made into ultrafine needles, researchers find.

Diamond is well-known as the strongest of all natural materials, and with that strength comes another tightly linked property: brittleness. But now, an international team of researchers from MIT, Hong Kong, Singapore, and Korea has found that when grown in extremely tiny, needle-like shapes, diamond can bend and stretch, much like rubber, and snap back to its original shape.

The surprising finding is being reported this week in the journal Science, in a paper by senior author Ming Dao, a principal research scientist in MIT’s Department of Materials Science and Engineering; MIT postdoc Daniel Bernoulli; senior author Subra Suresh, former MIT dean of engineering and now president of Singapore’s Nanyang Technological University; graduate students Amit Banerjee and Hongti Zhang at City University of Hong Kong; and seven others from CUHK and institutions in Ulsan, South Korea.

Experiment (left) and simulation (right) of a diamond nanoneedle being bent by the side surface of a diamond tip, showing ultralarge and reversible elastic deformation. No image credit.

The results, the researchers say, could open the door to a variety of diamond-based devices for applications such as sensing, data storage, actuation, biocompatible in vivo imaging, optoelectronics, and drug delivery. For example, diamond has been explored as a possible biocompatible carrier for delivering drugs into cancer cells.

The team showed that the narrow diamond needles, similar in shape to the rubber tips on the end of some toothbrushes but just a few hundred nanometers (billionths of a meter) across, could flex and stretch by as much as 9 percent without breaking, then return to their original configuration, Dao says.

Ordinary diamond in bulk form, Bernoulli says, has a limit of well below 1 percent stretch. “It was very surprising to see the amount of elastic deformation the nanoscale diamond could sustain,” he says.

“We developed a unique nanomechanical approach to precisely control and quantify the ultralarge elastic strain distributed in the nanodiamond samples,” says Yang Lu, senior co-author and associate professor of mechanical and biomedical engineering at CUHK. Putting crystalline materials such as diamond under ultralarge elastic strains, as happens when these pieces flex, can change their mechanical properties as well as thermal, optical, magnetic, electrical, electronic, and chemical reaction properties in significant ways, and could be used to design materials for specific applications through “elastic strain engineering,” the team says.

The team measured the bending of the diamond needles, which were grown through a chemical vapor deposition process and then etched to their final shape, by observing them in a scanning electron microscope while pressing down on the needles with a standard nanoindenter diamond tip (essentially the corner of a cube). Following the experimental tests using this system, the team did many detailed simulations to interpret the results and was able to determine precisely how much stress and strain the diamond needles could accommodate without breaking.

The researchers also developed a computer model of the nonlinear elastic deformation for the actual geometry of the diamond needle, and found that the maximum tensile strain of the nanoscale diamond was as high as 9 percent. The computer model also predicted that the corresponding maximum local stress was close to the known ideal tensile strength of diamond — i.e. the theoretical limit achievable by defect-free diamond.

When the entire diamond needle was made of one crystal, failure occurred at a tensile strain as high as 9 percent. Until this critical level was reached, the deformation could be completely reversed if the probe was retracted from the needle and the specimen was unloaded. If the tiny needle was made of many grains of diamond, the team showed that they could still achieve unusually large strains. However, the maximum strain achieved by the polycrystalline diamond needle was less than one-half that of the single crystalline diamond needle.

Yonggang Huang, a professor of civil and environmental engineering and mechanical engineering at Northwestern University, who was not involved in this research, agrees with the researchers’ assessment of the potential impact of this work. “The surprise finding of ultralarge elastic deformation in a hard and brittle material — diamond — opens up unprecedented possibilities for tuning its optical, optomechanical, magnetic, phononic, and catalytic properties through elastic strain engineering,” he says.

Huang adds “When elastic strains exceed 1 percent, significant material property changes are expected through quantum mechanical calculations. With controlled elastic strains between 0 to 9 percent in diamond, we expect to see some surprising property changes.”

The team also included Muk-Fung Yuen, Jiabin Liu, Jian Lu, Wenjun Zhang, and Yang Lu at the City University of Hong Kong; and Jichen Dong and Feng Ding at the Institute for Basic Science, in South Korea. The work was funded by the Research Grants Council of the Hong Kong Special Administrative Region, Singapore-MIT Alliance for Rresearch and Technology (SMART), Nanyang Technological University Singapore, and the National Natural Science Foundation of China.

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The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

From BNL: “New Capabilities at NSLS-II Set to Advance Materials Science”

Brookhaven Lab

The Hard X-ray Nanoprobe at Brookhaven Lab’s National Synchrotron Light Source II now offers a combination of world-leading spatial resolution and multimodal imaging.

Scientists at NSLS-II’s Hard X-ray Nanoprobe (HXN) spent 10 years developing advanced optics and overcoming many technical challenges in order to deliver world-leading spatial resolution and multimodal imaging at HXN.

By channeling the intensity of x-rays, synchrotron light sources can reveal the atomic structures of countless materials. Researchers from around the world come to the National Synchrotron Light Source II (NSLS-II)—a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Brookhaven National Laboratory—to study everything from proteins to fuel cells. NSLS-II’s ultra-bright x-rays and suite of state-of-the-art characterization tools make the facility one of the most advanced synchrotron light sources in the world. Now, NSLS-II has enhanced those capabilities even further.

Scientists at NSLS-II’s Hard X-ray Nanoprobe (HXN) beamline, an experimental station designed to offer world-leading resolution for x-ray imaging, have demonstrated the beamline’s ability to observe materials down to 10 nanometers—about one ten-thousandth the diameter of a human hair. This exceptionally high spatial resolution will enable scientists to “see” single molecules. Moreover, HXN can now combine its high spatial resolution with multimodal scanning—the ability to simultaneously capture multiple images of different material properties. The achievement is described in the Mar. 19 issue of Nano Futures.

“It took many years of hard work and collaboration to develop an x-ray microscopy beamline with such high spatial resolution,” said Hanfei Yan, the lead author of the paper and a scientist at HXN. “In order to realize this ambitious goal, we needed to address many technical challenges, such as reducing environmental vibrations, developing effective characterization methods, and perfecting the optics.”

A key component for the success of this project was developing a special focusing optic called a multilayer Laue lens (MLL)—a one-dimensional artificial crystal that is engineered to bend x-rays toward a single point.

A close-up view of the Hard X-ray Nanoprobe—beamline 3-ID at NSLS-II.

“Precisely developing the MLL optics to satisfy the requirements for real scientific applications took nearly 10 years,” said Nathalie Bouet, who leads the lab at NSLS-II where the MLLs were fabricated. “Now, we are proud to deliver these lenses for user science.”

Combining multimodal and high resolution imaging is unique, and makes NSLS-II the first facility to offer this capability in the hard x-ray energy range to visiting scientists. The achievement will present a broad range of applications. In their recent paper, scientists at NSLS-II worked with the University of Connecticut and Clemson University to study a ceramic-based membrane for energy conversion application. Using the new capabilities at HXN, the group was able to image an emerging material phase that dictates the membrane’s performance.

“We are also collaborating with researchers from industry to academia to investigate strain in nanoelectronics, local defects in self-assembled 3D superlattices, and the chemical composition variations of nanocatalysts,” Yan said. “The achievement opens up exciting opportunities in many areas of science.”

As the new capabilities are put to use, there is an ongoing effort at HXN to continue improving the beamline’s spatial resolution and adding new capabilities.

“Our ultimate goal is to achieve single digit resolution in 3D for imaging the elemental, chemical, and structural makeup of materials in real-time,” Yan said.

Scientific Paper: Multimodal hard x-ray imaging with resolution approaching 10 nm for studies in material science [IOP Science – Nano Futures]

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One of ten national laboratories overseen and primarily funded by the Office of Science of the U.S. Department of Energy (DOE), Brookhaven National Laboratory conducts research in the physical, biomedical, and environmental sciences, as well as in energy technologies and national security. Brookhaven Lab also builds and operates major scientific facilities available to university, industry and government researchers. The Laboratory’s almost 3,000 scientists, engineers, and support staff are joined each year by more than 5,000 visiting researchers from around the world. Brookhaven is operated and managed for DOE’s Office of Science by Brookhaven Science Associates, a limited-liability company founded by Stony Brook University, the largest academic user of Laboratory facilities, and Battelle, a nonprofit, applied science and technology organization.

From SLAC: “Scientists Use Machine Learning to Speed Discovery of Metallic Glass”

SLAC Lab

April 13, 2018
Glennda Chui

Fang Ren, who developed algorithms to analyze data on the fly while a postdoctoral scholar at SLAC, at a Stanford Synchrotron Radiation Lightsource beamline where the system has been put to use. (Dawn Harmer/SLAC National Accelerator Laboratory)

SLAC and its collaborators are transforming the way new materials are discovered. In a new report, they combine artificial intelligence and accelerated experiments to discover potential alternatives to steel in a fraction of the time.

Blend two or three metals together and you get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns.

But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass that’s amorphous, with its atoms arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today’s best steel, plus it stands up better to corrosion and wear.

Even though metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful.

Now a group led by scientists at the Department of Energy’s SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University has reported a shortcut for discovering and improving metallic glass – and, by extension, other elusive materials – at a fraction of the time and cost.

The research group took advantage of a system at SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning – a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data – with experiments that quickly make and screen hundreds of sample materials at a time.

SLAC/SSRL

This allowed the team to discover three new blends of ingredients that form metallic glass, and to do this 200 times faster than it could be done before, they reported today in Science Advances.

(Yvonne Tang/SLAC National Accelerator Laboratory)

“It typically takes a decade or two to get a material from discovery to commercial use,” said Northwestern Professor Chris Wolverton, an early pioneer in using computation and AI to predict new materials and a co-author of the paper. “This is a big step in trying to squeeze that time down. You could start out with nothing more than a list of properties you want in a material and, using AI, quickly narrow the huge field of potential materials to a few good candidates.”

The ultimate goal, he said, is to get to the point where a scientist could scan hundreds of sample materials, get almost immediate feedback from machine learning models and have another set of samples ready to test the next day – or even within the hour.

Over the past half century, scientists have investigated about 6,000 combinations of ingredients that form metallic glass, added paper co-author Apurva Mehta, a staff scientist at SSRL: “We were able to make and screen 20,000 in a single year.”

Just Getting Started

While other groups have used machine learning to come up with predictions about where different kinds of metallic glass can be found, Mehta said, “The unique thing we have done is to rapidly verify our predictions with experimental measurements and then repeatedly cycle the results back into the next round of machine learning and experiments.”

There’s plenty of room to make the process even speedier, he added, and eventually automate it to take people out of the loop altogether so scientists can concentrate on other aspects of their work that require human intuition and creativity. “This will have an impact not just on synchrotron users, but on the whole materials science and chemistry community,” Mehta said.

The team said the method will be useful in all kinds of experiments, especially in searches for materials like metallic glass and catalysts whose performance is strongly influenced by the way they’re manufactured, and those where scientists don’t have theories to guide their search. With machine learning, no previous understanding is needed. The algorithms make connections and draw conclusions on their own, and this can steer research in unexpected directions.

“One of the more exciting aspects of this is that we can make predictions so quickly and turn experiments around so rapidly that we can afford to investigate materials that don’t follow our normal rules of thumb about whether a material will form a glass or not,” said paper co-author Jason Hattrick-Simpers, a materials research engineer at NIST. “AI is going to shift the landscape of how materials science is done, and this is the first step.”

Strength in Numbers

The paper is the first scientific result associated with a DOE-funded pilot project where SLAC is working with a Silicon Valley AI company, Citrine Informatics, to transform the way new materials are discovered and make the tools for doing that available to scientists everywhere.

Founded by former graduate students from Northwestern and Stanford University, Citrine has created a materials science data platform where data that had been locked away in published papers, spreadsheets and lab notebooks is stored in a consistent format so it can be analyzed with AI specifically designed for materials.

“We want to take materials and chemical data and use them effectively to design new materials and optimize manufacturing,” said Greg Mulholland, founder and CEO of the company. “This is the power of artificial intelligence: As scientists generate more data, it learns alongside them, bringing hidden trends to the surface and allowing scientists to identify high-performance materials much faster and more effectively than relying on traditional, purely human-driven materials development.”

Until recently, thinking up, making and assessing new materials was painfully slow. For instance, the authors of the metallic glass paper calculated that even if you could cook up and examine five potential types of metallic glass a day, every day of the year, it would take more than a thousand years to plow through every possible combination of metals. When they do discover a metallic glass, researchers struggle to overcome problems that hold these materials back. Some have toxic or expensive ingredients, and all of them share glass’s brittle, shatter-prone nature.

Over the past decade, scientists at SSRL and elsewhere have developed ways to automate experiments so they can create and study more novel materials in less time. Today, some SSRL users can get a preliminary analysis of their data almost as soon as it comes out with AI software developed by SSRL in conjunction with Citrine and the CAMERA project at DOE’s Lawrence Berkeley National Laboratory.

“With these automated systems we can analyze more than 2,000 samples per day,” said Fang Ren, the paper’s lead author, who developed algorithms to analyze data on the fly and coordinated their integration into the system while a postdoctoral scholar at SLAC.

Experimenting with Data

In the metallic glass study, the research team investigated thousands of alloys that each contain three cheap, nontoxic metals.

They started with a trove of materials data dating back more than 50 years, including the results of 6,000 experiments that searched for metallic glass. The team combed through the data with advanced machine learning algorithms developed by Wolverton and graduate student Logan Ward at Northwestern.

Based on what the algorithms learned in this first round, the scientists crafted two sets of sample alloys using two different methods, allowing them to test how manufacturing methods affect whether an alloy morphs into a glass.

Both sets of alloys were scanned by an SSRL X-ray beam, the data fed into the Citrine database, and new machine learning results generated, which were used to prepare new samples that underwent another round of scanning and machine learning.

By the experiment’s third and final round, Mehta said, the group’s success rate for finding metallic glass had increased from one out of 300 or 400 samples tested to one out of two or three samples tested. The metallic glass samples they identified represented three different combinations of ingredients, two of which had never been used to make metallic glass before.

See the full article here .

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SLAC is a multi-program laboratory exploring frontier questions in photon science, astrophysics, particle physics and accelerator research. Located in Menlo Park, California, SLAC is operated by Stanford University for the DOE’s Office of Science.

From JHU HUB: “With new technique, researchers create metallic alloy nanoparticles with unprecedented chemical capabilities”

JHU HUB

4.4.18
Rachel Wallach

New, stable nanoparticles are expected to have useful applications in the chemical and energy industries. Image credit: Getty Images

Johns Hopkins researchers have teamed with colleagues from three other universities to combine up to eight different metals into single, uniformly mixed nanoparticles, creating new and stable nanoparticles with useful applications in the chemical and energy industries, the researchers said.

Metallic alloy nanoparticles—particles ranging from about a billionth to 100 billionths of a meter in size—are often used as catalysts in the production of industrial products such as fertilizers and plastics. Until now, only a small set of alloy nanoparticles have been available because of complications that arise when combining extremely different metals.

In the March 30 cover article of the journal Science, the researchers reported that their new technique made it possible to combine multiple metals, including those not usually considered capable of mixing.

“This method enables new combinations of metals that do not exist in nature and do not otherwise go together,” said Chao Wang, an assistant professor in the Department of Chemical and Biomolecular Engineering at Johns Hopkins and one of the study’s co-authors.

The new materials, known as high-entropy-alloy nanoparticles, have created unprecedented catalytic mechanisms and reaction pathways and are expected to improve energy efficiency in the manufacturing process and lower production costs.

The new method uses shock waves to heat the metals to extremely high temperatures—2,000 degrees Kelvin (more than 3,140 Fahrenheit) and higher—at exceptionally rapid rates, both heating and cooling them in the span of milliseconds. The metals are melted together to form small droplets of liquid solutions at the high temperatures and are then rapidly cooled to form homogeneous nanoparticles. Traditional methods, which rely on relatively slow and low-temperature heating and cooling techniques, often result in clumps of metal instead of separate particles.

Based on these new nanoparticles, Wang’s research group designed a five-metal nanoparticle that demonstrated superior catalytic performance for selective oxidation of ammonia to nitrogen oxide, a reaction used by the chemical industry to produce nitric acid, which is used in the large-scale production of fertilizers and other products.

In addition to nitric acid production, the researchers are exploring use of the nanoparticles in reactions like the removal of nitrogen oxide from vehicle exhaust. The work in Wang’s lab was part of a collaboration with colleagues from the University of Maryland, College Park; the University of Illinois at Chicago; and MIT.

See the full article here .

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About the Hub

We’ve been doing some thinking — quite a bit, actually — about all the things that go on at Johns Hopkins. Discovering the glue that holds the universe together, for example. Or unraveling the mysteries of Alzheimer’s disease. Or studying butterflies in flight to fine-tune the construction of aerial surveillance robots. Heady stuff, and a lot of it.

In fact, Johns Hopkins does so much, in so many places, that it’s hard to wrap your brain around it all. It’s too big, too disparate, too far-flung.

We created the Hub to be the news center for all this diverse, decentralized activity, a place where you can see what’s new, what’s important, what Johns Hopkins is up to that’s worth sharing. It’s where smart people (like you) can learn about all the smart stuff going on here.

At the Hub, you might read about cutting-edge cancer research or deep-trench diving vehicles or bionic arms. About the psychology of hoarders or the delicate work of restoring ancient manuscripts or the mad motor-skills brilliance of a guy who can solve a Rubik’s Cube in under eight seconds.

There’s no telling what you’ll find here because there’s no way of knowing what Johns Hopkins will do next. But when it happens, this is where you’ll find it.

The Johns Hopkins University opened in 1876, with the inauguration of its first president, Daniel Coit Gilman. “What are we aiming at?” Gilman asked in his installation address. “The encouragement of research … and the advancement of individual scholars, who by their excellence will advance the sciences they pursue, and the society where they dwell.”

The mission laid out by Gilman remains the university’s mission today, summed up in a simple but powerful restatement of Gilman’s own words: “Knowledge for the world.”

What Gilman created was a research university, dedicated to advancing both students’ knowledge and the state of human knowledge through research and scholarship. Gilman believed that teaching and research are interdependent, that success in one depends on success in the other. A modern university, he believed, must do both well. The realization of Gilman’s philosophy at Johns Hopkins, and at other institutions that later attracted Johns Hopkins-trained scholars, revolutionized higher education in America, leading to the research university system as it exists today.

From Cornell University: “Students work in the collaborative lab of Kin Fai Mak’

Cornell University

Students work in the collaborative lab of Kin Fai Mak

Their research is exploring new physical phenomena in atomically thin materials.

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Once called “the first American university” by educational historian Frederick Rudolph, Cornell University represents a distinctive mix of eminent scholarship and democratic ideals. Adding practical subjects to the classics and admitting qualified students regardless of nationality, race, social circumstance, gender, or religion was quite a departure when Cornell was founded in 1865.

Today’s Cornell reflects this heritage of egalitarian excellence. It is home to the nation’s first colleges devoted to hotel administration, industrial and labor relations, and veterinary medicine. Both a private university and the land-grant institution of New York State, Cornell University is the most educationally diverse member of the Ivy League.

On the Ithaca campus alone nearly 20,000 students representing every state and 120 countries choose from among 4,000 courses in 11 undergraduate, graduate, and professional schools. Many undergraduates participate in a wide range of interdisciplinary programs, play meaningful roles in original research, and study in Cornell programs in Washington, New York City, and the world over.

From UCLA: “UCLA researchers develop a new class of two-dimensional materials”

UCLA Newsroom

March 08, 2018
Matthew Chin

An artist’s concept of two kinds of monolayer atomic crystal molecular superlattices. On the left, molybdenum disulfide with layers of ammonium molecules; on the right, black phosphorus with layers of ammonium molecules. UCLA.

A research team led by UCLA scientists and engineers has developed a method to make new kinds of artificial “superlattices” — materials comprised of alternating layers of ultra-thin “two-dimensional” sheets, which are only one or a few atoms thick. Unlike current state-of-the art superlattices, in which alternating layers have similar atomic structures, and thus similar electronic properties, these alternating layers can have radically different structures, properties and functions, something not previously available.

For example, while one layer of this new kind of superlattice can allow a fast flow of electrons through it, the other type of layer can act as an insulator. This design confines the electronic and optical properties to single active layers, and they do not interfere with other insulating layers.

Such superlattices can form the basis for improved and new classes of electronic and optoelectronic devices. Applications include superfast and ultra-efficient semiconductors for transistors in computers and smart devices, and advanced LEDs and lasers.

Compared with the conventional layer-by-layer assembly or growth approach currently used to create 2D superlattices, the new UCLA-led process to manufacture superlattices from 2D materials is much faster and more efficient. Most importantly, the new method easily yields superlattices with tens, hundreds or even thousands of alternating layers, which is not yet possible with other approaches.

This new class of superlattices alternates 2D atomic crystal sheets that are interspaced with molecules of varying shapes and sizes. In effect, this molecular layer becomes the second “sheet” because it is held in place by “van der Waals” forces, weak electrostatic forces to keep otherwise neutral molecules “attached” to each other. These new superlattices are called “monolayer atomic crystal molecular superlattices.”

The study, published in Nature, was led by Xiangfeng Duan, UCLA professor of chemistry and biochemistry, and Yu Huang, UCLA professor of materials science and engineering at the UCLA Samueli School of Engineering.

“Traditional semiconductor superlattices can usually only be made from materials with highly similar lattice symmetry, normally with rather similar electronic structures,” Huang said. “For the first time, we have created stable superlattice structures with radically different layers, yet nearly perfect atomic-molecular arrangements within each layer. This new class of superlattice structures has tailorable electronic properties for potential technological applications and further scientific studies.”

One current method to build a superlattice is to manually stack the ultrathin layers one on top of the other. But this is labor-intensive. In addition, since the flake-like sheets are fragile, it takes a long time to build because many sheets will break during the placement process. The other method is to grow one new layer on top of the other, using a process called “chemical vapor deposition.” But since that means different conditions, such as heat, pressure or chemical environments, are needed to grow each layer, the process could result in altering or breaking the layer underneath. This method is also labor-intensive with low yield rates.

The new method to create monolayer atomic crystal molecular superlattices uses a process called “electrochemical intercalation,” in which a negative voltage is applied. This injects negatively charged electrons into the 2D material. Then, this attracts positively charged ammonium molecules into the spaces between the atomic layers. Those ammonium molecules automatically assemble into new layers in the ordered crystal structure, creating a superlattice.

“Think of a two-dimensional material as a stack of playing cards,” Duan said. “Then imagine that we can cause a large pile of nearby plastic beads to insert themselves, in perfect order, sandwiching between each card. That’s the analogous idea, but with a crystal of 2D material and ammonium molecules.”

The researchers first demonstrated the new technique using black phosphorus as a base 2D atomic crystal material. Using the negative voltage, positively charged ammonium ions were attracted into the base material, and inserted themselves between the layered atomic phosphorous sheets.

Following that success, the team inserted different types of ammonium molecules with various sizes and symmetries into a series of 2D materials. They found that they could tailor the structures of the resulting monolayer atomic crystal molecular superlattices, which had a diverse range of desirable electronic and optical properties.

“The resulting materials could be useful for making faster transistors that consume less power, or for creating efficient light-emitting devices,” Duan said.

The lead author of the study is Chen Wang, a doctoral student advised by Huang and Duan, who are both members of the California NanoSystems Institute. Other study authors are UCLA graduate students and postdoctoral researchers in Duan or Huang’s research groups; researchers from Caltech; Hunan University, China; University of Science and Technology of China; and King Saud University, Saudi Arabia.

The research was supported by the National Science Foundation and the Office of Naval Research.

See the full article here .

Please help promote STEM in your local schools.

Stem Education Coalition

For nearly 100 years, UCLA has been a pioneer, persevering through impossibility, turning the futile into the attainable.

We doubt the critics, reject the status quo and see opportunity in dissatisfaction. Our campus, faculty and students are driven by optimism. It is not naïve; it is essential. And it has fueled every accomplishment, allowing us to redefine what’s possible, time after time.

This can-do perspective has brought us 12 Nobel Prizes, 12 Rhodes Scholarships, more NCAA titles than any university and more Olympic medals than most nations. Our faculty and alumni helped create the Internet and pioneered reverse osmosis. And more than 100 companies have been created based on technology developed at UCLA.

From PI: Women in STEM-“Celebrating International Women’s Day”

Is it not a shame that we need to have a special day to celebrate women when they are so already fantastic and exceptionally brilliant in the physical sciences?

Check out this blog post-
https://sciencesprings.wordpress.com/2018/03/08/from-the-conversation-women-in-stem-perish-not-publish-new-study-quantifies-the-lack-of-female-authors-in-scientific-journals/

“”I have done a couple of STEM events, but there have never been this many girls. There are so many here. It is really empowering. Go girls in STEM!” Eama, Grade 12

Today’s Inspiring Future Women in Science conference was a success. Mona Nemar, Canada’s Chief Science Advisor, gave opening remarks encouraging the students in attendance to take advantage of the opportunity to learn from the speakers to come.

“The days of women being held back or being excluded from science are over. Now, more than ever women are entering, remaining in, and revolutionizing the science fields. Today is a shining example of that.”
-Mona Nemar, Chief Science Advisor, Government of Canada

Mona, read my above post on women getting not published.

The speakers and panelists, who included a chemist, engineer, astronomer, ecologist, and surgeon, talked about the challenges and triumphs that a career in STEM brings. Students were then treated to a speed mentoring session where they were able to ask questions and interact with women from a broad number of STEM careers. Read more about how this conference is inspiring young women here.

“This conference showed me there are so many things you can do going into [a career in STEM], so now I feel more inspired, and I feel more confident and not scared to go into science.” Lealan, Age 16

Programs like Perimeter’s “Inspiring Future Women in Science” conference are helping young women see their own potential and reach out for careers in STEM. And more talented female scientists today, means a brighter future tomorrow.

Thank you for being part of the equation.

From MIT: “Automating materials design”

MIT News

February 2, 2018 [Just showed up in social media.]
Larry Hardesty

New software identified five different families of microstructures, each defined by a shared “skeleton” (blue), that optimally traded off three mechanical properties. Courtesy of the researchers.

With new approach, researchers specify desired properties of a material, and a computer system generates a structure accordingly.

For decades, materials scientists have taken inspiration from the natural world. They’ll identify a biological material that has some desirable trait — such as the toughness of bones or conch shells — and reverse-engineer it. Then, once they’ve determined the material’s “microstructure,” they’ll try to approximate it in human-made materials.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new system that puts the design of microstructures on a much more secure empirical footing. With their system, designers numerically specify the properties they want their materials to have, and the system generates a microstructure that matches the specification.

The researchers have reported their results in Science Advances. In their paper, they describe using the system to produce microstructures with optimal trade-offs between three different mechanical properties. But according to associate professor of electrical engineering and computer science Wojciech Matusik, whose group developed the new system, the researchers’ approach could be adapted to any combination of properties.

“We did it for relatively simple mechanical properties, but you can apply it to more complex mechanical properties, or you could apply it to combinations of thermal, mechanical, optical, and electromagnetic properties,” Matusik says. “Basically, this is a completely automated process for discovering optimal structure families for metamaterials.”

Joining Matusik on the paper are first author Desai Chen, a graduate student in electrical engineering and computer science; and Mélina Skouras and Bo Zhu, both postdocs in Matusik’s group.

Finding the formula

The new work builds on research reported last summer, in which the same quartet of researchers generated computer models of microstructures and used simulation software to score them according to measurements of three or four mechanical properties. Each score defines a point in a three- or four-dimensional space, and through a combination of sampling and local exploration, the researchers constructed a cloud of points, each of which corresponded to a specific microstructure.

Once the cloud was dense enough, the researchers computed a bounding surface that contained it. Points near the surface represented optimal trade-offs between the mechanical properties; for those points, it was impossible to increase the score on one property without lowering the score on another.

No image caption or credit.

That’s where the new paper picks up. First, the researchers used some standard measures to evaluate the geometric similarities of the microstructures corresponding to the points along the boundaries. On the basis of those measures, the researchers’ software clusters together microstructures with similar geometries.

For every cluster, the software extracts a “skeleton” — a rudimentary shape that all the microstructures share. Then it tries to reproduce each of the microstructures by making fine adjustments to the skeleton and constructing boxes around each of its segments. Both of these operations — modifying the skeleton and determining the size, locations, and orientations of the boxes — are controlled by a manageable number of variables. Essentially, the researchers’ system deduces a mathematical formula for reconstructing each of the microstructures in a cluster.

Next, the researchers use machine-learning techniques to determine correlations between specific values for the variables in the formulae and the measured properties of the resulting microstructures. This gives the system a rigorous way to translate back and forth between microstructures and their properties.

On automatic

Every step in this process, Matusik emphasizes, is completely automated, including the measurement of similarities, the clustering, the skeleton extraction, the formula derivation, and the correlation of geometries and properties. As such, the approach would apply as well to any collection of microstructures evaluated according to any criteria.

By the same token, Matusik explains, the MIT researchers’ system could be used in conjunction with existing approaches to materials design. Besides taking inspiration from biological materials, he says, researchers will also attempt to design microstructures by hand. But either approach could be used as the starting point for the sort of principled exploration of design possibilities that the researchers’ system affords.

“You can throw this into the bucket for your sampler,” Matusik says. “So we guarantee that we are at least as good as anything else that has been done before.”

In the new paper, the researchers do report one aspect of their analysis that was not automated: the identification of the physical mechanisms that determine the microstructures’ properties. Once they had the skeletons of several different families of microstructures, they could determine how those skeletons would respond to physical forces applied at different angles and locations.

But even this analysis is subject to automation, Chen says. The simulation software that determines the microstructures’ properties can also identify the structural elements that deform most under physical pressure, a good indication that they play an important functional role.

The work was supported by the U.S. Defense Advanced Research Projects Agency’s Simplifying Complexity in Scientific Discovery program.

See the full article here .

Please help promote STEM in your local schools.

Stem Education Coalition

The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

From Ames Lab: “Seeing the future of new energy materials”

Ames Laboratory

Using advanced NMR spectroscopy methods to guide materials design.

(l-r) Jason Goh, Takeshi Kobayashi, Linlin Wang, Wenyu Huang, Amrit Venkatesh, Aaron Rossini, Frederic Perras, Marek Pruski, Mike Hanrahan and Zhuoran Wang.

How do small defects in the surface of solar cell material affect its ability to absorb and convert sunlight to electricity? How does the molecular structure of a porous material determine its ability to separate gases from one another? Understanding the structure and function of materials at the atomic scale is one of the frontiers of energy science.

“Many new materials have been developed in the past decade to address needs for energy conversion and storage,” said Aaron Rossini, a scientist at the U.S. Department of Energy’s Ames Laboratory, and a professor of chemistry at Iowa State University. “However, there is still a lot we don’t know about how these materials function. We want to change that and bring new information to the table that will be used to optimize these materials.”

Ames Laboratory has recently received new funding to study such materials by developing and applying new techniques in solid-state nuclear magnetic resonance (NMR) spectroscopy. “NMR has a long and distinguished history at Ames Laboratory, in terms of both expertise and facilities, and this new research project is its latest chapter,” said Ames Laboratory scientist Marek Pruski, “Understanding the structure of materials is fundamentally important to many research groups here, and we will be collaborating with them at a new level to expand their insights.”

Most people associate NMR with magnetic resonance imaging (MRI), which is used as a diagnostic tool in medicine. Nuclear magnetic resonance probes the nuclei of atoms as they absorb and re-emit radio waves when they are placed in a magnetic field. Those nuclei resonate at measurable radio-frequencies that precisely depend on the local structure of material, the element being studied, and the strength of the magnetic field.

In late 2014, the spectroscopy experts at Ames Laboratory took their NMR capabilities a quantum leap forward with the acquisition of the first commercial DNP-NMR spectrometer used for materials research in North America. “DNP” stands for “Dynamic Nuclear Polarization,” a method which uses microwaves to excite unpaired electrons in radicals and transfer their high spin polarization to the nuclei in the sample being analyzed. It’s an ‘extra-oomph’ version of conventional NMR technology, offering drastically higher sensitivity and faster data acquisition—and it has already provided game-changing insight into the physical, chemical, and electronic properties of materials. For example, with DNP-enhanced NMR it is possible to measure the distances in between atoms with precision of a trillionth of a meter or measure two-dimensional correlation spectra between rare nuclei, such as carbon-13.

“We‘ve had a ball here for the last two and a half years, publishing research findings at the rate of a journal paper per month since the DNP-NMR became operational,” said Pruski. “That’s really a very high pace for high-impact science.”

“It’s a perfect tool for this type of investigations. The properties of energy materials are governed by the structure of their surfaces and the interfaces, and DNP-NMR is especially well-adapted and sensitive to exploring these.”

Ames Laboratory will pair these rapidly expanding capabilities in DNP-NMR with a technique called ultrafast magic-angle spinning (UFMAS), which relies on spinning the sample at extremely high frequencies (> 6 million RPM). UFMAS greatly improves NMR experiments by allowing signals from hydrogen to be well resolved in most solids.

Theoretical physicists will be joining the efforts of the experimentalists, developing models that computationally verify or explain their results. Conversely, NMR experiments will guide the development of improved theoretical models.

“Our work could have far-reaching impact on a lot of fields, in electronics, lighting, solar cells, nanoparticle design, materials with a variety of energy applications,” said Rossini. “If we are able to explain how structure and function are related, we can help direct intelligent materials design.”

See the full article here .

Please help promote STEM in your local schools.

Stem Education Coalition

Ames Laboratory is a government-owned, contractor-operated research facility of the U.S. Department of Energy that is run by Iowa State University.

For more than 60 years, the Ames Laboratory has sought solutions to energy-related problems through the exploration of chemical, engineering, materials, mathematical and physical sciences. Established in the 1940s with the successful development of the most efficient process to produce high-quality uranium metal for atomic energy, the Lab now pursues a broad range of scientific priorities.

Ames Laboratory is a U.S. Department of Energy Office of Science national laboratory operated by Iowa State University. Ames Laboratory creates innovative materials, technologies and energy solutions. We use our expertise, unique capabilities and interdisciplinary collaborations to solve global problems.

Ames Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.