The work is led by Dr. Mengying Liu from Washington and Lee University in collaboration with researchers at Texas A&M University. The team investigated formation of cracks in initially flawless, crack-free samples of a nickel-base alloy (Inconel 725), which is primarily known for its strength and corrosion resistance. There are currently several working hypotheses that attempt to explain hydrogen embrittlement. The results of this study show that one of the more well-known hypotheses — hydrogen enhanced localized plasticity (HELP) — is not applicable in the case of this alloy.

Plasticity, or irreversible deformation, is not uniform throughout a material, but is instead localized to certain points. HELP hypothesizes that cracks initiate at the points with the highest localized plasticity.

“As far as I know, ours is the first study that actually looks in real time to see where cracks initiate — and isn’t at locations of highest localized plasticity,” said co-author Dr. Michael J. Demkowicz, a professor in the Department of Materials Science and Engineering at Texas A&M University and Liu’s PhD advisor. “Our study tracks both the localized plasticity and the crack initiation locations in real time.”

Tracking crack initiation in real time is crucial. When examining a sample after a crack has appeared, the hydrogen has already escaped from the material, making it impossible to understand the mechanism that led to the damage.

“Hydrogen easily escapes from metals, so you can’t figure out what it does to embrittle a metal by examining specimens after they’ve been tested. You have to look while you’re testing,” said Demkowicz.

This study helps to lay the groundwork for better predictions of hydrogen embrittlement. In the future, hydrogen may replace fossil fuels as a clean energy source. If this change occurs, all of the infrastructure currently used to store and use fossil fuels would become susceptible to hydrogen embrittlement. Predicting embrittlement is crucial for preventing unexpected failures, making a future hydrogen economy possible.

The experiments for this study, as well as the preliminary data analysis, were conducted at Texas A&M, with Liu providing further data analysis and manuscript preparation at Washington and Lee. This paper is co-authored by Liu, Demkowicz and Texas A&M doctoral student Lai Jiang.

University of Illinois Urbana-Champaign chemical and biomolecular engineering professor Simon Rogers and graduate student Krutarth Kamani specialize in determining how soft materials yield to stress and have shown how solid and liquid physical states can exist together in the same material. This area is of high interest due to its importance to industrial, environmental and biomedical applications.

Along the way, the team identified a communication breakdown among the scientists who work in this area, causing a bottleneck between a theoretical understanding of soft material behavior and real-world applications.

When soft materials — natural or synthetic — deform under pressure, they eventually reach a critical point where they either return to their original form or undergo permanent deformation, like stretching or breaking a piece of elastic. This process is known as yielding. A gradual yielding transition is termed ductile behavior, while an abrupt one is referred to as brittle behavior, the researchers said.

“At a recent conference, we realized that all of us who study soft materials from all over Europe and North America couldn’t agree what the connection is between brittle and ductile behavior nor how to define it.”

In the study, published in the Proceedings of the National Academy of Sciences, instead of viewing soft material behavior as one or the other — brittle or ductile — Rogers’ team considers a spectrum of yielding behaviors. This allowed the team to build a continuum model, which led to them uncovering the brittility factor. This factor is critical in determining how and why soft materials fail.

Essentially, brittility affects how a material deforms permanently under stress. The team’s model indicates that the higher the brittility factor, the less a soft material will deform permanently before yielding.

As in the team’s past studies, the model was developed and tested using data from numerous experiments that subjected various soft materials to stress while measuring the individual strain responses using a device called a rheometer.

“We didn’t expect this study to explain as much as it does,” said Rogers, who is also an affiliate at the Beckman Institute for Advanced Science and Technology at the U. of. I. “What we ended up with was a way to bring a whole bunch of soft material behaviors together under the same physics umbrella. Previously, they’d been studied independently or maybe all been applied simultaneously, but never thought of as being physically or mathematically connected.”

This finding will allow researchers to explain precisely why some materials are more resistant to rapid yielding than others, a question that has eluded researchers for decades.

“This single parameter amazingly connects so many puzzling observations researchers have come across over the years,” Kamani said.

“This work marks the point at which we are approaching the crest of the hill in understanding soft materials behavior,” Rogers said. “We’ve always felt like each step takes us higher, but with no end in sight. Now we can see the top of the hill, and we are closer to the top and free to move forward in whatever direction we would like.”

The National Science Foundation supported this research.

Although adaptive radar systems have been around since World War II, they’ve hit a fundamental performance wall in the past couple of decades. But with the help of modern AI approaches and lessons learned from computer vision, researchers at Duke University have broken through that wall, and they want to bring everyone else in the field along with them.

In a new paper published July 16 in the journal IET Radar, Sonar & Navigation, Duke engineers show that using convolutional neural networks (CNNs) — a type of AI that revolutionized computer vision — can greatly enhance modern adaptive radar systems. And in a move that parallels the impetus of the computer vision boom, they have released a large dataset of digital landscapes for other AI researchers to build on their work.

“Classical radar methods are very good, but they aren’t good enough to meet industry demands for products such as autonomous vehicles,” said Shyam Venkatasubramanian, a graduate research assistant working in the lab of Vahid Tarokh, the Rhodes Family Professor of Electrical and Computer Engineering at Duke. “We’re working to bring AI into the adaptive radar space to tackle problems like object detection, localization and tracking that industry needs solved.”

At its most basic level, radar is not difficult to understand. A pulse of high-frequency radio waves is broadcast, and an antenna gathers data from any waves that bounce back. As technology has advanced, however, so too have the concepts used by modern radar systems. With the ability to shape and direct signals, process multiple contacts at once, and filter out background noise, the technology has come a long way in the past century.

But radar has come just about as far as it can using these techniques alone. Adaptive radar systems still struggle to accurately localize and track moving objects, especially in complex environments like mountainous terrain.

To move adaptive radar into the age of AI, Venkatasubramanian and Tarokh were inspired by the history of computer vision. In 2010, researchers at Stanford University released an enormous image database consisting of over 14 million annotated images called ImageNet. Researchers around the world used ImageNet to test and compare new AI approaches that became industry standard.

In the new paper, Venkatasubramanian and his collaborators show that using the same AI approaches greatly improves the performance of current adaptive radar systems.

“Our research parallels the research of the earliest users of AI in computer vision and the creators of ImageNet, but within adaptive radar,” Venkatasubramanian said. “Our proposed AI takes as input processed radar data and outputs a prediction of the target’s location through a simple architecture that can be thought of as paralleling the predecessor of most modern computer vision architectures.”

While the group has yet to test their methods in the field, they benchmarked their AI’s performance on a modeling and simulation tool called RFView®, which gains its accuracy by incorporating the Earth’s topography and terrain into its modeling toolbox. Then, continuing in the footsteps of computer vision, they created 100 airborne radar scenarios based on landscapes from across the contiguous United States and released it as an open-source asset called “RASPNet.”

This is a valuable asset, as only a handful of teams have access to RFView®. The researchers, however, received special permission from the creators of RFView® to build the dataset — which contains more than 16 terabytes of data built over the course of several months — and make it publicly available.

“I am delighted that this groundbreaking work has been published, and particularly that the associated data is being made available in the RASPNet repository,” said Hugh Griffiths, Fellow Royal Academy of Engineering, Fellow IEEE, Fellow IET, OBE, and the THALES/Royal Academy Chair of RF Sensors at University College London, who was not involved with the work. “This will undoubtedly stimulate further work in this important area, and ensure that the results can readily be compared with each other.”

The scenarios included were handpicked by radar and machine learning experts and have a wide range of geographical complexity. On the easiest side for adaptive radar systems to handle is the Bonneville Salt Flats, while the hardest is Mount Rainier. Venkatasubramanian and his group hope that others will take their ideas and dataset and build even better AI approaches.

For example, in a previous paper, Venkatasubramanian showed that an AI tailored to a specific geographical location could achieve up to a seven-fold improvement in localizing objects over classical methods. If an AI could select a scenario on which it had already been trained that is similar to its current environment, it should substantially improve in performance.

“We think this will have a really big impact on the adaptive radar community,” Venkatasubramanian said. “As we move forward and continue adding capabilities to the dataset, we want to provide the community with everything it needs to push the field forward into using AI.”

This work was supported by the Air Force Office of Scientific Research (FA9550-21-1-0235, 20RYCORO51, 20RYCOR052).

The research group of corresponding author Laure Kayser, assistant professor in the Department of Materials Science and Engineering in UD’s College of Engineering with a joint appointment in the Department of Chemistry and Biochemistry in the College of Arts and Sciences, regularly works with PEDOT:PSS, a polymer that has both electronic and ionic conductivity, and was interested in finding ways to synthesize this material from plastic waste.

After connecting with Argonne chemist David Kaphan during an event hosted by UD’s research office, the research teams at UD and Argonne began evaluating the hypothesis that PEDOT:PSS could be made by sulfonating polystyrene, a synthetic plastic found in many types of disposable containers and packing materials.

Sulfonation is a common chemical reaction where a hydrogen atom is replaced by sulfonic acid; the process is used to create a variety of products such as dyes, drugs and ion exchange resins. These reactions can either be “hard” (with higher conversion efficiency but that require caustic reagents) or “soft” (a less efficient method but one that uses milder materials).

In this paper, the researchers wanted to find something in the middle: “A reagent that is efficient enough to get really high degrees of functionalization but that doesn’t mess up your polymer chain,” Kayser explained.

The researchers first turned to a method described in a previous study for sulfonating small molecules, one that showed promising results in terms of efficiency and yield, using 1,3-Disulfonic acid imidazolium chloride ([Dsim]Cl). But adding functional groups onto a polymer is more challenging than for a small molecule, the researchers explained, because not only are unwanted byproducts harder to separate, any small errors in the polymer chain can change its overall properties.

To address this challenge, the researchers embarked on many months of trial and error to find the optimal conditions that minimized side reactions, said Kelsey Koutsoukos, a materials science doctoral candidate and second author of this paper.

“We screened different organic solvents, different molar ratios of the sulfonating agent, and evaluated different temperatures and times to see which conditions were the best for achieving high degrees of sulfonation,” he said.

The researchers were able to find reaction conditions that resulted in high polymer sulfonation, minimal defects and high efficiency, all while using a mild sulfonating agent. And because the researchers were able to use polystyrene, specifically waste Styrofoam, as a starting material, their method also represents an efficient way to convert plastic waste into PEDOT:PSS.

Once the researchers had PEDOT:PSS in hand, they were able to compare how their waste-derived polymer performed compared to commercially available PEDOT:PSS.

“In this paper, we looked at two devices — an organic electronic transistor and a solar cell,” said Chun-Yuan Lo, a chemistry doctoral candidate and the paper’s first author. “The performance of both types of conductive polymers was comparable, and shows that our method is a very eco-friendly approach for converting polystyrene waste into high-value electronic materials.”

Specific analyses conducted at UD included X-ray photoelectron spectroscopy (XPS) at the surface analysis facility, film thickness analysis at the UD Nanofabrication Facility, and solar cell evaluation at the Institute of Energy Conversion. Argonne’s advanced spectroscopy equipment, such as carbon NMR, was used for detailed polymer characterization. Additional support was provided by materials science and engineering professor Robert Opila for solar cell analysis and by David C. Martin, the Karl W. and Renate Böer Chaired Professor of Materials Science and Engineering, for the electronic device performance analyses.

One unexpected finding related to the chemistry, the researchers added, is the ability to use stoichiometric ratios during the reaction.

“Typically, for sulfonation of polystyrene, you have to use an excess of really harsh reagents. Here, being able to use a stoichiometric ratio means that we can minimize the amount of waste being generated,” Koutsoukos said.

This finding is something the Kayser group will be looking into further as a way to “fine-tune” the degree of sulfonation. So far, they’ve found that by varying the ratio of starting materials, they can change the degree of sulfonation on the polymer. Along with studying how this degree of sulfonation impacts the electrical properties of PEDOT:PSS, the team is interested in seeing how this fine-tuning capability can be used for other applications, such as fuel cells or water filtration devices, where the degree of sulfonation greatly impacts a material’s properties.

“For the electronic devices community, the key takeaway is that you can make electronic materials from trash, and they perform just as well as what you would purchase commercially,” Kayser said. “For the more traditional polymer scientists, the fact that you can very efficiently and precisely control the degree of sulfonation is going to be of interest to a lot of different communities and applications.”

The researchers also see great potential for how this research can contribute to ongoing global sustainability efforts by providing a new way to convert waste products into value-added materials.

“Many scientists and researchers are working hard on upcycling and recycling efforts, either by chemical or mechanical means, and our study provides another example of how we can address this challenge,” Lo said.

The complete list of co-authors includes Chun-Yuan Lo, Kelsey Koutsoukos, Dan My Nguyen, Yuhang Wu, David Angel Trujillo, Tulaja Shrestha, Ethan Mackey, Vidhika Damani, Robert Opila, David Martin, and Laure Kayser from the University of Delaware and Tabitha Miller, Uddhav Kanbur, and David Kaphan from Argonne National Laboratory.

Her latest research examines the health records and health care use of youth, individuals under age 21, who likely have been involved or whose families have been involved in the justice system. The challenge was identifying youth who have been impacted by mass incarceration, as most health care systems don’t routinely ask about incarceration. Families may not disclose that information due to stigma, fear of child protective services involvement, or judgment.

“There are few, if any, large community-level studies about the health of youth affected by incarceration, or their family’s incarceration, using medical records,” explains Boch, an assistant professor at the University of Cincinnati College of Nursing. “Despite a lot of youth and families affected by incarceration, gaps remain in understanding its prevalence and consequences. There are numerous reasons for this, some include a lack of provider awareness, lack of curriculum in provider training, lack of funding for this research and lack of routine sensitive screening for exposure.”

Boch and her research team searched the electronic medical records for justice-related keywords such as “prison,” “jail,” “sentenced,” “probation,” “parole,” and others, to determine the impact of incarceration. The researchers used data from Cincinnati Children’s Hospital collected over an 11-year period.

Their study, published in Academic Pediatrics, found that of the more than 1.7 million records reviewed, 38,263 (or 2.2%) of youth seen between January 2009 and December 2020 likely had a parent incarcerated or faced some type of confinement as a juvenile. This small percentage was also responsible for a disproportionate number of physical and mental health diagnoses and health care visits at Cincinnati Children’s. They were compared against a socio demographically matched sample without a justice keyword and the total sample population of youth.

Nearly 63.3% of all behavioral health inpatient admissions, 23.7% of all hospitalization inpatient days and 45.5% of all foster care visits were attributed to the 2.2% of youth who had documented probable personal or family justice system involvement. The findings complement another study led by Boch, published in 2021 using data from Nationwide Children’s Hospital in Columbus, Ohio.

Youth with a justice keyword in their record had 1.5 to 16.2 times the prevalence of various physical and mental health disorder groupings studied compared to matched youth who didn’t have a justice keyword but do have similar socioeconomic backgrounds. They also had 428.2 more physical health diagnoses and 269.2 more mental health diagnoses per 100 youth than the matched youth.

According to the study, youth with a justice keyword made up a large proportion of all of those who were diagnosed with health disorders or conditions at Cincinnati Children’s from 2009-2020. This includes 42.9% of all schizophrenia spectrum and other psychotic disorders, 42.1% of all bipolar and related disorders, 38.3% of all suicide and self-injury disorders, 24.5% of all trauma and stress related disorders, 44.9% of all shaken baby syndrome cases, 13.9% of all infectious diseases, 12.5% of speech language disorders and 12.8% of all youth pregnancies.

Nationally, about 7% of U.S. youth have had a parent incarcerated. Findings at Cincinnati Children’s and Nationwide Children’s Hospital in Columbus grossly underestimate the number of youth affected by incarceration or confinement, says Boch.

“Our data reflects families who disclosed and health providers who documented,” says Boch. “Families who refrain from disclosing or whose information is not documented were not represented which is a key limitation. This study is an attempt to uncover the size of the impact of mass incarceration on youth health in Cincinnati. Our health care systems and correctional systems clearly overlap and impact the lives of children.

“Replication of these findings in other communities would strengthen the growing justification for decarceration efforts and other reforms, especially if we want all U.S. children and families to thrive,” says Boch. “We will continue to have health care disparities and lead the world with poor health outcomes if we continue to lead in incarceration.”

Other co-authors of the study include Joshua Lambert, PhD, University of Cincinnati; Christopher Wilderman, PhD, Duke University; and Judith Dexheimer, PhD; Robert Kahn, MD; and Sarah Beal, PhD, all of the University of Cincinnati and Cincinnati Children’s.

The research study of Cincinnati youth was supported by Boch’s awards, including the Agency for Healthcare Research and Quality and Patient Centered Outcomes Research Institute (AHRQ/PCORI) K12 PEDSnet Scholars Learning Health Systems Career Development Program, internal funding from the University of Cincinnati College of Nursing Dean’s New Investigator Award, internal funding from the Cincinnati Children’s Hospital Medical Center James M. Anderson Center for Health Systems Excellence, and the NIH/NIMHD Loan Repayment Award for Clinician Scientists from Disadvantaged Backgrounds.

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“The way A-beta behaves in a variety of environments, including the human brain, is elusive,” said Brian Sun, an electrical systems and engineering alumnus of Washington University in St. Louis who is now an MD/PhD student in the School of Medicine.

“There’s an understanding of growth and decay that isn’t fully fleshed out,” he added.

That’s going to change, thanks to research recently published by Sun with colleagues in Associate Professor Matthew Lew’s lab at the Preston M. Green Department of Electrical and Systems Engineering (ESE) in WashU’s McKelvey School of Engineering. .

In first of its kind work, Sun and colleagues were able to make measurements of amyloid fibril beta-sheet assemblies, the underlying girders of the protein conglomeration, while they were changing. Previous high-resolution microscopy studies have only gotten static shots.

“We wanted to look specifically at dynamics of the underlying structure of A-beta that could be responsible for the changes we’re seeing, not just changes in the overall shape,” Sun said.

Lew uses Lego as an analogy, noting that current imaging technology shows you the full Lego building but not any look at how each individual brick is organized.

“The individual proteins are always changing in response to their environment,” Lew said. “It is like having certain Lego bricks causing other bricks to change their shape. The changing architecture of the proteins and the assembled aggregates together leads to the complexity of neurogenerative disease.”

The Lew lab has developed a new type of imaging tech that allows them to see the orientation and other minute details in nanostructures of biological systems that were previously invisible. Their technique — single-molecule orientation-localization microscopy (SMOLM) — uses the flashes of light from chemical probes to visualize the sheets of peptides underlying Aβ42, one kind of A-beta peptide.

Using SMOLM lets them look at individual orientation of the underlying beta-sheets to see the relationship between their organization and how that relates to the overall structure of the amyloid protein.

Multiple ways to remodel

Aβ42 is constantly changing, and step one is to try to find a method to the madness, a model or pattern of action to predict the protein’s behavior.

Now that the Lew lab can make these measurements, they made some intuitive observations and found some surprises hidden in the amyloid-beta architecture.

As can be expected, stable Aβ42 structures tend to retain stable underlying beta-sheetsgrowing structures have underlying beta-sheets that become more defined and rigid as the growth continues. Decaying structures exhibit increasingly disordered and less rigid beta-sheets. But they also found more than one way that Aβ42 can renovate.

“There are multiple different ways for Aβ42 structures to remain stable, or grow and decay,” Sun said.

The researchers also discovered that Aβ42 can grow and decay in ways that defy expectations. For example, Aβ42 can grow and decay in ways that preserve the underlying structure; sometimes there’s growth where the peptides just pile on, but the underlying beta-sheet orientations don’t change. In other cases, Aβ42 undergoes “stable decay,” where the opposite happens i.e. peptides leave, but beta-sheet structure remains. Finally, Aβ42’s beta-sheets sometimes reorganize and change orientations without immediate accompanying changes to the overall shape. These nano-structural reorganizations can predispose to future large-scale remodeling.

“Because SMOLM can track Aβ42’s underlying organization and not just its shape, we can see different kinds of subtypes of remodeling that aren’t visible to diffraction-limited, non-orientation imaging modalities,” Sun said.

If it all sounds a bit vague, keep in mind this is the first pass at even looking at these constantly shifting nanoscale structures. There were no previous works to compare notes, which makes it all the more notable that Sun crafted this work while juggling COVID-19 lockdown restrictions and his undergrad course load at WashU, which he completed in three years. It paves the way for him and others to start really getting a handle on amyloid architecture.

He’ll likely end up chasing more of these questions during the graduate phase of his MD/PhD training, where he plans to design nanoscale imaging systems and sensors that could reveal hidden mechanisms of difficult-to-treat diseases.

Sun credits WashU ESE department and the Lew lab for the rigorous training that made this study and academic trajectory possible, as well as WashU’s MSTP for supporting his continued research post-graduation. “I’m really glad I went through this journey,” he said.

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM124858.