To help overcome these obstacles, the National Institute of Standards and Technology (NIST) and the nonprofit World Technology Evaluation Center (WTEC) advocate that nanoparticle researchers, manufacturers and administrators “connect the dots” by considering their shared challenges broadly and tackling them collectively rather than individually. This includes transferring knowledge across disciplines, coordinating actions between organizations and sharing resources to facilitate solutions.

The recommendations are presented in a new paper in the journal ACS Applied Nano Materials.

“We looked at the big picture of nanoparticle manufacturing to identify problems that are common for different materials, processes and applications,” said NIST physical scientist Samuel Stavis, lead author of the paper. “Solving these problems could advance the entire enterprise.”

The new paper provides a framework to better understand these issues. It is the culmination of a study initiated by a workshop organized by NIST that focused on the fundamental challenge of reducing or mitigating heterogeneity, the inadvertent variations in nanoparticle size, shape and other characteristics that occur during their manufacture.

“Heterogeneity can have significant consequences in nanoparticle manufacturing,” said NIST chemical engineer and co-author Jeffrey Fagan.

In their paper, the authors noted that the most profitable innovations in nanoparticle manufacturing minimize heterogeneity during the early stages of the operation, reducing the need for subsequent processing. This decreases waste, simplifies characterization and improves the integration of nanoparticles into products, all of which save money.

The authors illustrated the point by comparing the production of gold nanoparticles and carbon nanotubes. For gold, they stated, the initial synthesis costs can be high, but the similarity of the nanoparticles produced requires less purification and characterization. Therefore, they can be made into a variety of products, such as sensors, at relatively low costs.

In contrast, the more heterogeneous carbon nanotubes are less expensive to synthesize but require more processing to yield those with desired properties. The added costs during manufacturing currently make nanotubes only practical for high-value applications such as digital logic devices.

“Although these nanoparticles and their end products are very different, the stakeholders in their manufacture can learn much from each other’s best practices,” said NIST materials scientist and co-author J. Alexander Liddle. “By sharing knowledge, they might be able to improve both seemingly disparate operations.”

Finding ways like this to connect the dots, the authors said, is critically important for new ventures seeking to transfer nanoparticle technologies from laboratory to market.

“Nanoparticle manufacturing can become so costly that funding expires before the end product can be commercialized,” said WTEC nanotechnology consultant and co-author Michael Stopa. “In our paper, we outlined several opportunities for improving the odds that new ventures will survive their journeys through this technology transfer ‘valley of death.'”

Finally, the authors considered how manufacturing challenges and innovations are affecting the ever-growing number of applications for nanoparticles, including those in the areas of electronics, energy, health care and materials.

All of these measures depend on science delivering high-quality seismic hazard models. And yet, current models depend on a list of uncertain assumptions, with predictions that are difficult to test in the real world due to the long intervals between big earthquakes.

Now, a team of researchers from Columbia University’s Lamont-Doherty Earth Observatory, University of Southern California, University of California at Riverside and the U.S. Geological Survey has come up with a physics-based model that marks a turning point in earthquake forecasting. Their results appear in the new issue of Science Advances.

“Whether a big earthquake happens next week or 10 years from now, engineers need to build for the long run,” says the study’s lead author, Bruce Shaw, a geophysicist at Lamont-Doherty. “We now have a physical model that tells us what the long-term hazards are.”

Simulating nearly 500,000 years of California earthquakes on a supercomputer, researchers were able to match hazard estimates from the state’s leading statistical model based on a hundred years of instrumental data. The mutually validating results add support for California’s current hazard projections, which help to set insurance rates and building design standards across the state. The results also suggest a growing role for physics-based models in forecasting earthquake hazard and evaluating competing models in California and other earthquake prone regions.

The earthquake simulator used in the study, RSQSim, simplifies California’s statistical model by eliminating many of the assumptions that go into estimating the likelihood of an earthquake of a certain size hitting a specific region. The researchers, in fact, were surprised when the simulator, programmed with relatively basic physics, was able to reproduce estimates from a model that has improved steadily for decades. “This shows our simulator is ready for prime time,” says Shaw.

Seismologists can now use RSQSim to test the statistical model’s region-specific predictions. Accurate hazard estimates are especially important to government regulators in high-risk cities like Los Angeles and San Francisco, who write and revise building codes based on the latest science. In a state with a severe housing shortage, regulators are under pressure to make buildings strong enough to withstand heavy shaking while keeping construction costs down. A second tool to confirm hazard estimates gives the numbers added credibility.

“If you can get similar results with different techniques, that builds confidence you’re doing something right,” says study coauthor Tom Jordan, a geophysicist at USC.

A hallmark of the simulator is its use of rate and state-dependent friction to approximate how real-world faults break and transfer stress to other faults, sometimes setting off even bigger quakes. Developed at UC Riverside more than a decade ago, and refined further in the current study, RSQSim is the first physics-based model to replicate California’s most recent rupture forecast, UCERF3. When results from both models were fed into California’s statistical ground-shaking model, they came up with similar hazard profiles.

John Vidale, director of the Southern California Earthquake Center, which helped fund the study, says the new model has created a realistic 500,000-year history of earthquakes along California’s faults for researchers to explore. Vidale predicted the model would improve as computing power grows and more physics are added to the software. “Details such as earthquakes in unexpected places, the evolution of earthquake faults over geological time, and the viscous flow deep under the tectonic plates are not yet built in,” he said.

The researchers plan to use the model to learn more about aftershocks, and how they unfold on California’s faults, and to study other fault systems globally. They are also working on incorporating the simulator into a physics-based ground-motion model, called CyberShake, to see if it can reproduce shaking estimates from the current statistical model.

“As we improve the physics in our simulations and computers become more powerful, we will better understand where and when the really destructive earthquakes are likely to strike,” says study coauthor Kevin Milner, a researcher at USC.

Story Source:

Materials provided by Columbia University. Original written by Kim Martineau. Note: Content may be edited for style and length.

Researchers at the University of Wisconsin-Madison have built a robot, named Minnie, to serve as a reading buddy to middle school kids, and Minnie’s new friends grew more excited about books and more attached to the robot over two weeks of reading together.

“After one interaction, the kids were generally telling us that, sure, it was nice to have someone to read with,” says Joseph Michaelis, a UW-Madison graduate student studying educational psychology. “But by the end of two weeks, they’re talking about how the robot was funny and silly and afraid, and how they’d come home looking forward to seeing it again.”

Michaelis and computer sciences professor Bilge Mutlu published their work with Minnie on Wednesday (Aug. 22) in the journal Science Robotics.

Research shows that social learning — pairing up with a peer to complete math problems or read a chapter in a textbook — is a powerful way to help students develop skills and interests, according to Michaelis. It improves students’ comprehension by distributing the cognitive workload and reinforcing understanding through dialogue.

“Most interesting to me is that we know social learning strengthens interest and motivation,” says Michaelis, who taught high school science before returning to graduate school. “A lot of kids who don’t like reading, in particular, point out that it’s an isolated activity, and people just sort of accept that isolation. But it can be demotivating and harder to learn and understand in that situation.”

Michaelis and Mutlu believe companion robots will soon be a fixture in homes, and they wondered if those robots could serve as social learning companions for kids.

They designed a two-week reading program including 25 books representing a range of reading skill and story complexity, and programmed Minnie to be an interested listener. The children in the study read aloud to the robot, which could track their progress in the book and react to the story — every few pages or so, especially during important moments in the plot — with one of hundreds of preprogrammed comments.

“The goal is to try to make it as genuinely conversational as possible. If you were reading a book to me, and I was surprised, I’d say something like, “Wow, I didn’t see that coming!” Michaelis says. “When a scary part of the book happens, the robot says, ‘Oh, wow, I’m really scared.’ It reacts like it would if it had a real personality.”

With simple, oversized black eyes on a relatively featureless white globe of a head, Minnie can react and cajole and summarize and appear thoughtful. It even starts by following an initial introductory read by recommending (via an algorithm) a good book from its library of titles, which include the familiar Harry Potter and Goosebumps series and genre classics like “A Wrinkle in Time.”

“That match is crucial. If you’re trying to make a social connection with someone, and they say, ‘You’d love this book,’ and they’re totally wrong? That ruins that credibility,” Michaelis says. “Most kids said the robot did a good job suggesting books to them.”

The connection grew from there. The number of children who told researchers the robot has a personality or emotions increased more than fourfold over the two weeks readers spent with Minnie. The number reporting they were motivated to read also spiked — and surpassed a control group following a paper-based version of the reading program. And kids who read with Minnie said they felt like they understood and remembered more about the shared books.

Mutlu, who studies human-robot interaction, said the illusion of personality in a robot is so fragile, it often breaks down in a matter of minutes.

“We’re getting more used to interacting with things that talk to us now, with Siri and Alexa and others. When they are behaving in a way that kind of reveals their technical flaws or limitations, you realize immediately you’re not having an actual conversation,” Mutlu says. “The biggest shock in our study is that two weeks later, the kids are still relating to the character — rather than saying, ‘This is stupid. I’m not talking to your robot anymore.'”

He attributes that success to the care taken in developing Minnie’s speech repertoire, and also to a sweet spot robots enjoy among the age group in the study — a group that may be emotionally sophisticated enough to make connections, but not wary or snarky enough to push the envelope.

“The children are a match to the robot’s capabilities, and vice versa,” Mutlu says. “This robot supports an engaging reading activity, but it’s not a social companion to the extent that you could have an open conversation with it. If you had a much more capable robot, that picture might change.”

Not that other family members were left out. Minnie’s homestays got to be comfy visits, with the robot and humans sprawled on the floor or propped up on beds with pillows.

“I had families send me pictures of themselves dressing up the robot. I’d get the box back, and it would have sorts of extra books in it that were just from the kids’ shelves, just thrown in there. All kinds of random paper, markers, crayons,” Michaelis says. “Your experiments don’t get to live like that — to get that authenticity — in a lab.”

Michaelis and Mutlu, whose work is supported by the National Science Foundation, see Minnie helping to spur otherwise reluctant students on in all sorts of academic tasks, and have already begun testing a version of the robot that shares in science studies. They hope to try out periods of interaction even longer than two weeks — an eternity for a robot interaction, but a blink of an eye in the development of a child.

They expect social learning functions could be part of a companion robot shared by a whole family, but acknowledge that future as fraught with design concerns over how to craft an engaging personality for users at different ages, protect family members’ privacy and maintain the hard-won trust of a child that will also see the robot having private interactions with parents.

“This idea is in its infancy. But now we know if you really, carefully design this, it can actually sustain interaction and heighten kids’ emotional experience with reading,” Mutlu says. “That’s a huge achievement.”

This work was supported by a grant from the National Science Foundation (1149970).