麻豆影院

Researchers Andr茅s Montoya-Castillo and Julia Moriarty are named U.S. Department of Energy Early Career Researchers, receiving multiyear funding

Two 麻豆影院 researchers have been selected as U.S. Department of Energy Early Career Research Program scientists, a designation intended to support the next generation of U.S. STEM leaders.

Andr茅s Montoya-Castillo, an assistant professor in the Department of Chemistry, and Julia Moriarty, an assistant professor in the Department of Atmospheric and Oceanic Sciences and a fellow in the Institute of Arctic and Alpine Research, are among from across the United States whose research spans astrophysics and artificial intelligence to fusion-energy and quantum materials. The 93 scientists will share in $135 million in research funding for projects of up to five years.

鈥淪upporting America鈥檚 scientists and researchers early in their careers will ensure the United States remains at the forefront of scientific discovery,鈥� U.S. Secretary of Energy Jennifer M. Granholm states in the awards announcement. 鈥淭he funding 鈥� gives the recipients the resources to find the answers to some of the most complex questions as they establish themselves as experts in their fields.鈥澨�

Understanding how molecules dance

Montoya-Castillo鈥檚 research is guided, in part, by the need to know which molecules are 鈥済oing to be good candidates for some technological adventure,鈥� he says. 鈥淲e need to know how that molecule interacts with light.鈥�

One of the biggest challenges to understanding molecules is the fact that they don鈥檛 stop moving. Far from the static picture on a textbook page, molecules 鈥渁re always dancing, always jiggling about,鈥� Montoya-Castillo says. 鈥淲hen they jiggle about, sometimes photons or little particles of light that they wouldn鈥檛 have been able to absorb, now they can. Or the opposite could be true: They can鈥檛 absorb particles we thought they could, because they鈥檙e jiggling about, or can鈥檛 do it as well.鈥�

Knowing how molecules in liquids and solids absorb light has the potential to support the development of everything from more efficient solar cells to organic semiconductors and biological dyes. But knowing molecules means knowing how they dance, a longtime roadblock in designing materials that maximize energy conversion, say, or enhance quantum computing.

So, Montoya-Castillo and his research group will attack this problem with statistics. 鈥淥ne of deepest aspects of theoretical chemistry is saying, 鈥極K, we have a random-looking process. What kind of statistics does this random process follow?鈥� he says. 鈥淲e鈥檙e looking to bridge the randomness to establish a fully predictive simulation.鈥�

The researchers will initially apply their techniques to porphyrins, which are molecules prevalent everywhere on Earth and involved in everything from oxygen transport to energy transfer; they cause the red in blood and the green in plants. Montoya-Castillo notes that porphyrins are ideal for testing the techniques because they are highly tunable and are critical ingredients in natural and artificial energy conversion.

鈥淥ne of the questions we鈥檙e asking is, 鈥楬ow do we arrive at design principles to make the next generation of photo catalysts or energy conversion devices, the next generation of quantum computing or quantum sensing?鈥欌�� he says.

鈥淭o do this, we need to achieve two things. The first is realize when our wonderful theories and models are not sufficient to predict and explain the physics that one gets from experiment and generalize our approach. We are doing that by developing the theoretical framework required to predict the spectra of molecules whose constant jiggling makes it difficult to know when they will absorb photons.

鈥淭he second is to exploit the current models when they work to give us insight. And fast. To tackle this second challenge, we鈥檙e working on being able to exploit experimental data to parameterize the model automatically and use this as a starting point to predict how molecules interact with light. Then we鈥檒l be able to match our predictions to experiment, refine the model and our understanding, and speed up feedback loop of theory-experiment-design, which has traditionally been a very computationally complex and expensive procedure.

He adds that, 鈥淥ne of the final things we鈥檙e doing is developing a machine-learning framework to reduce this huge computational cost so we can really accelerate the pathway to tweaking these molecules to get some technological advances going for us.鈥�

Climate change and coastal flooding

For Moriarty, a coast oceanographer by training, the path to her DOE-supported research began with a practical observation: As storms become slower and wetter because of climate change, they are dumping a lot more rain on coastal areas. Couple that with sea level rise caused by climate change, and coastal urban centers are increasingly at risk for floods.

鈥淲hen urban areas flood, you can have sewage systems flood, water-treatment plants flood, nuclear power plants flood, because all these facilities have to be located near water,鈥� Moriarty says. 鈥淪o, the question is: when a flood causes polluted water to enter the local waterways, what鈥檚 that polluted water鈥檚 fate?鈥�

Not only can floods contaminate local waterways by spreading bacterial or even radioactive contaminants into them, but they can unleash a cascade of events in which excess nutrient levels can stimulate harmful algae blooms, reduce oxygen levels in the water and reduce water clarity and quality, sometimes leading to 鈥渄ead zones.鈥�

Moriarty鈥檚 research combines process-based and statistical machine-learning modeling to analyze how floods of coastal infrastructure affect pollutant and nutrient fluxes in local waterways, and their impact on biogeochemical processes. A significant aim is to better understand how extreme floods degrade water quality and which aspects of flooding are predictable and which are not.

鈥淚f something鈥檚 predictable, it鈥檚 a lot easier to plan for it,鈥� Moriarty says.

The research will use Baltimore, Maryland, as a case study, in collaboration with the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory. Using data from the climate model, as well as a new Baltimore hydrodynamic-biogeochemistry model, Moriarty and her research team aim to better understand how coastal urban flooding impacts local waterway biogeochemistry in different climate scenarios.

Further, the researchers want to use a combination of machine learning and sensitivity tests of the process-based model they develop to scale up what they learn from local observations in Baltimore to coastal-urban systems worldwide.

鈥淭he better we can understand and predict these events, the better we can plan for them,鈥� Moriarty says. 鈥淚t costs a lot less to mitigate risks in advance of events than to clean them up afterward.鈥�

Top image: Glenn Asakawa/CU 麻豆影院

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