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2020 Spotlights

Francisco BarreraNeuromorphic computing is the use of the human brain as design inspiration for computer systems, and has been steadily gaining interest since the 1980’s. Its potential to improve both speed and energy efficiency in computing, and subsequently supercomputing, make neuromorphic computing a thriving area of interest. 

While not attempting to directly copy the human brain, neuromorphic computing draws inspiration from neurons and synapses to develop new means of computation and information transfer. Innovation is an important part of the field of neuromorphic computing, and Francisco Barrera, associate professor of Biochemistry & Cellular and Molecular Biology, is bringing a new approach to chip development. 

Historically Barrera’s work has focused on the plasma membrane, a protective barrier between individual cells and their external environments, which also regulate cell signaling or communication between cells. For this StART project, Barrera is using his expertise in collaboration with Pat Collier, staff researcher at ORNL.  

“The Collier laboratory is working to recreate how neurons work using a system called droplet interface bilayer,” said Barrera. In a droplet interface bilayer, DIB, system there are small membranes that closely mimic the membranes of neurons. When connected, these membranes have been shown to do some promising computing. 

Barrera’s team’s work could improve the function and connectivity of these membranes. They have designed a peptide that, when added into the membranes, canb change how currents move across the membranes, which is important for communication.  

“What I think is very exciting is that I believe we can make an important contribution, because we try to understand, at a very deep level, how our peptides interact with lipids in the membrane,” said Barrera. “This is the kind of basic knowledge that allows you to understand a system and predict how it will respond.” 

Barrera hopes the discoveries made with this StART project will ultimately lead to increased power and flexibility in chips for neuromorphic computing. 

Sindhu JagadammaCarbon is the foundation of all life on planet Earth and is a central component of climate, food production, and energy creation. Carbon cycling is the way carbon is recycled or moved around from the atmosphere, into organisms and soil, and back out again. Changes to each of these components have the ability to impact the carbon cycle, but the potential effects of soil composition are not well understood. Assistant Professor of Biosystems Engineering and Soil Science Sindhu Jagadamma hopes to improve that understanding. 

Plants pull carbon dioxide from the air and, through photosynthesis, convert it to plant biomass, which ultimately ends up in soil as soil carbon. Soil carbon is critical to sustainable food production, playing a vital role in soil, water and air quality. Securely storing carbon in soil is also important for reducing the concentration of carbon dioxide in the atmosphere.   

Soil composition plays an important role in soil carbon cycling. For example, manganese content in soil can impact carbon cycling by influencing photosynthesis and litter decomposition. Jagadamma’s StART project is focusing on the impact of manganese on the balance of carbon within agricultural soil systems. 

“It is really important to understand the different drivers of carbon cycling in soil in order to build healthy soils and promote sustainability,” said Jagadamma. “The role of manganese in influencing carbon decomposition is relatively unknown, especially in agricultural soils.” 

Jagadamma points out that nitrogen fertilizers may create more acidic soil, which increases manganese availability. While manganese is an essential nutrient for plants, excess manganese in soil can inhibit plant growth and lead to lower crop yields. However, a comprehensive study determining the link between manganese, carbon cycling, and the impact on crop lands has yet to be completed. 

Jagadamma’s ORNL collaborator, Staff Scientist in Environmental Sciences Elizabeth Herndon, has begun this work with laboratory and field experiments in forested ecosystems. The knowledge these experiments have generated is being used by Jagadamma’s team to extend the research into the agricultural field.  

“We are going to manipulate different levels of manganese in soil, grow plants, and see if the different levels of manganese are influencing plant growth and litter decomposition, and how it is ultimately going to influence the carbon cycle,” said Jagadamma.  

Her project, like so many others this year, was delayed due to COVID-19 precautions but she hopes to move into the experimental phase next spring. In the meantime, she and her team have focused on literature review and a lab-scale pilot study to assist in developing the most meaningful field experiment for the Spring. 

Jagadamma’s StART project will have obvious implications for agriculture. If soil manganese is altered by nitrogen fertilization and other human-induced changes, and if those altered manganese levels change soil carbon storage, cropland systems can be developed for better crop growth and carbon storage. This work will also have broader implications for global carbon cycling by helping to curb carbon dioxide levels in the atmosphere. 

Hugh MedalThe Materials Genome Initiative, MGI, was announced in 2011 as a multi-agency initiative intended to increase the speed of advanced materials development and production. Since that time the federal government has invested more than $250 million in new research and innovation infrastructures to help achieve that goal. Assistant Professor of Industrial and Systems Engineering Hugh Medal hopes his StART project will also contribute to the goals of the MGI. 

The Materials Project was announced as a key program of the MGI with the goal of providing open access to a registry of known and predicted materials. Since it’s inception, the Materials Project has amassed a database of hundreds of thousands of materials with their predicted properties, information that would normally require repeated experimentation to discover. 

However, knowing the material can exist is not the same thing as successfully creating it. While the simulations contributed to the Materials Project may be able to point toward potential new materials, figuring out how best to grow those materials is left to experimentation. 

“Making a material is a lot more complicated than just putting components together,” said Medal. “Think about steel. It’s not just a matter of adding different elements from the periodic table. It requires applying a lot of different processing actions in order to get the material to its final state, or phase.” 

The question of how best to develop these predicted materials is a large one in material science. Medal is attempting to make inroads of this problem with his StART project. His team in collaborating with Haixuan Xu, associate professor of materials science and engineering and former Science Alliance JDRD awardee, to create a simulation to predict how to grow these materials. 

“We’re working together to come up with a technique that can tell us how, given a predicted material that’s really interesting, what processing do we need to apply over time to be able to grow that material,” said Medal. 

Leveraging Xu’s expertise in modeling the kinetic behavior of materials and Medal’s work with machine learning, the team hopes to develop a tool to serve as a guide for experimentalists as they work toward creating predicted materials. 

“Our hope is that our tool that will simplify the process. Rather than having experimentalists sift through a large number of combinations of processing actions, we want our tool to point toward the processes that would most likely be successful,” said Medal. 

Constance BaileyOn average, the U.S. Food and Drug Administration, FDA, approves 20 new drugs per year for public use. Each of those drugs has been on a decade long journey of research and development that may have cost as much as $2.6 billion. The JDRD work of Constance Bailey, assistant professor of chemistry, could help reduce the time, and subsequently costs, of drug development. 

Creating pharmaceuticals is a complex process that typically begins in a lab. The process of constructing drugs relies heavily on understanding how certain molecules exist in three-dimensional space, or stereochemistry. 

“Stereochemistry is really important for developing safe pharmaceutical intermediates, the building blocks of complex molecules. If you can’t control the stereochemistry then the drugs don’t fit together correctly,” said Bailey.  

Molecules have what Bailey described as handedness. Putting two molecules together is similar to a handshake; one molecule acts as the right hand and one acts as the left hand allowing them to fit together correctly. If both molecules are the right hand, the handshake does not work correctly. Bailey’s JDRD team is investigating how to control the handedness of particular molecules.  

“Enzymes are really naturally good at doing this, so what we’re trying to do is figure out how to selectively harness the enzymes in a fairly easy and trivial way to make one mirror image, or one hand, into the other,” said Bailey. 

The ultimate goal of Bailey’s work is to discover a method to allow scientists to determine in advance what changes may be needed in a molecule in the context of an experiment. While the pharmaceutical industry may be one of the most immediately relevant areas that could benefit from Bailey’s work, it is far from the only one. 

“There are actually a lot of broad applications beyond just the pharmaceutical applications, for example in materials. In a sense, the work we’re doing is kind of a fundamental science problem,” said Bailey. “How do you selectively create three-dimensional structures that could have applications in all different areas of chemistry?” 

Bailey’s team adapted to the university wide changes due to COVID-19 by having online meetings with their collaborator, Omar Demerdesh, Liane B. Russell Distinguished Fellow in the biosciences division at ORNL. While her lab was closed, Bailey and her students used their time digging deep into the literature and formulating an updated strategy for moving forward.  

Zhenbo WangIn 2009, the Google Self-Driving Car Project made its debut, ushering in an age of interest and research in automated vehicles. Connected and automated vehicles, CAVs, have continued to capture the attention of researchers as they attempt to address some of the fundamental problems with connective vehicle technology. Zhenbo Wang, assistant professor of mechanical, aerospace and biomedical engineering is tackling one of these very problems: intersections. 

CAVs use a variety of technologies to communicate with other connected devices around them. This may include other cars, roadside assistance services, or even traffic signals. These communications could provide information that allow drivers and vehicles to adjust for improved efficiency, such as changes in acceleration to adapt to an approaching intersection. 

“What I’m doing with this project is trying to better control ground vehicles based on traffic signal changes,” said Wang. “The traffic signal will broadcast information to oncoming vehicles and we want to know how we can optimize the motion of the vehicle to, for example, minimize fuel consumption.” 

In additional to improved fuel efficiency, CAVs have major implications for road safety as well. According to the Tennessee Department of Safety & Homeland Security, there were more than 1,100 traffic fatalities in the state, a little more than ten percent of which occurred in the greater Knoxville area.  

Some of the primary causes of these accidents include distracted driving, driving under the influence, and speeding. In other words, human error is a major cause of fatal traffic accidents. CAVs may decrease fatal traffic accidents by using connections with other devices to reduce risk such as by communicating to oncoming vehicles that a car is about to run a red light.  

Wang’s StART team hopes to develop a control strategy for vehicles using traffic signal phase and timing data to make real time speed adjustments in response to information received. These adjustments will contribute to better fuel efficiency and a host of other benefits. 

Wang is working in collaboration with Tim LaClair, research and development engineer at ORNL, whose team has developed expertise in modeling, simulation, and control of CAVs. Their long-term goal is to develop a framework for controlling CAVs that works effectively with the large amounts of data generated by the traffic network. 

“Connected vehicles have the potential to revolutionize transportation,” said Wang. “To realize that potential we need to develop algorithms to control these vehicles in real time to reduce congestion, maximize fuel economy, and increase safety.”