Hydropower — DNA in a drop
Researchers at Oak Ridge National Laboratory are using a novel approach in determining environmental impacts to aquatic species near hydropower facilities, potentially leading to smarter facility designs that can support electrical grid reliability.
By collecting surface water samples, field researchers can analyze tiny pieces of environmental DNA, or eDNA, that fish and other organisms shed into the water, compare it to a genetic database of known species, and determine which organisms are living in the water. Environmental impact studies using eDNA are a fraction of the cost of conventional surveys, which can be disruptive because they involve capturing or seeing organisms in their habitats.
“From a drop of water, we can now better understand changes in the ecosystem, more accurately monitor and protect previously undetected endangered species, and respond with sound science,” said ORNL’s Brenda Pracheil.
Next, researchers hope to determine species sex and reproductive status through genetic marking. — Mimi McHale
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Caption: From left, ORNL’s Brenda Pracheil, Kristine Moody and Trent Jett collect water samples at Melton Hill Lake using a sophisticated instrument that collects DNA in the water to determine fish species and number of fish in the water, which could prove useful for monitoring hydropower impacts. Credit: Carlos Jones/ORNL, U.S Dept. of Energy
Caption: ORNL’s Brenda Pracheil, left, and Kristine Moody collect water samples at Melton Hill Lake using a sophisticated instrument that collects DNA in the water to determine fish species and number of fish in the water, which could prove useful for monitoring hydropower impacts. Credit: Carlos Jones/ ORNL, U.S Dept. of Energy
Manufacturing — Printing in the wind
Oak Ridge National Laboratory researchers recently used large-scale additive manufacturing with metal to produce a full-strength steel component for a wind turbine, proving the technique as a viable alternative to conventional welding approaches.
Wind energy adoption in the United States depends largely on industry’s ability to manufacture large, complex structures. Additive manufacturing, or 3D printing, could reduce cost and increase production efficiency compared with traditional methods.
In a demonstration, polymer and metal-based 3D printing processes were deployed to manufacture the turbine’s skeleton node, which serves as a load-bearing joint between structural beams. The research team compared performance and total production cost and determined that while near-term costs for 3D printing remained similar to welding, long-term projected future costs proved favorable.
“Metal additive allows for advanced designs and lightweight components that aren’t feasible with traditional processes,” ORNL’s Brian Post said. “This technology could enable the rapid printing of large-scale recyclable metallic wind turbine structures.”
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Caption: Oak Ridge National Laboratory researchers used big area additive manufacturing with metal to 3D print a steel component for a wind turbine, proving the technique as a viable alternative to conventional fabrication methods. Credit: ORNL, U.S. Dept. of Energy
Climate — Drier air
A new analysis from Oak Ridge National Laboratory shows that intensified aridity, or drier atmospheric conditions, is caused by human-driven increases in greenhouse gas emissions. The findings point to an opportunity to address and potentially reverse the trend by reducing emissions.
Scientists examined the underlying causes for these long-term changes in global aridity using a multipronged approach and a wealth of observational data from 1965 to 2014.
The research team projects that large-scale dryness will continue to increase through the end of this century. Their model results, which are constrained by historical observations, project more aridification than original Earth system models.
“We used rigorous detection and attribution methods to disentangle the human fingerprints from natural factors,” said ORNL’s Jiafu Mao. “Natural variability alone cannot explain increasing aridification at long timescales. We can say with high statistical confidence that anthropogenic drivers, primarily greenhouse gas emissions, are causing these changes in climate.”
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Caption: A new analysis shows projected changes in annual aridity for the years 2071-2100 compared to 1985-2014. Brown shadings indicate drier conditions. Black dots indicate statistical significance at the 90% confidence level. Credit: Jiafu Mao/ORNL, U.S. Dept. of Energy
AI — Revealing hidden biology
Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity.
Their process leverages deep learning techniques to study cell movements, guided by simple physics rules similar to video-game play. “We observed new features of an unknown migration mechanism, called sequential rosettes, that were validated by biomarker experiments,” said Dali Wang of Oak Ridge National Laboratory who led the research.
“We used hierarchical deep reinforcement learning and convolutional neural networks to study the movement of the nuclei and then investigated the migrating cell within the simulated biological environment to discover what’s unknown in the system,” he said.
ORNL, the Sloan Kettering Institute and the University of Tennessee will continue developing their deep learning method to better understand other biological unknowns.
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Caption: A new process developed by Oak Ridge National Laboratory leverages deep learning techniques to study cell movements in a simulated environment, guided by simple physics rules similar to video-game play. Credit: MSKCC and UTK
Energy storage — Calculating better batteries
Scientists can speed the design of energy-dense solid-state batteries using a new tool created by Oak Ridge National Laboratory.
The Solid-State Battery Performance Analyzer and Calculator, or SolidPAC, can help researchers who have developed a promising new material but are unsure how to design a successful cell, said ORNL’s Ilias Belharouak. “It builds practicality into the search for better batteries,” he said.
Researchers exploring materials and architecture for safer, more efficient battery designs can access the publicly available SolidPAC tool to accelerate their work. Based on user input, the system analyzes factors such as materials chemistry, thickness and electron flows, feeding back design specifications and an energy density estimate, as outlined in a recent paper.
“SolidPAC will help researchers, industry and even educated laypersons tinker with different compositions and determine energy density,” said ORNL’s Marm Dixit. “The result is a toolkit that lets users configure battery designs for specific uses.”
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Caption: Oak Ridge National Laboratory has developed the SolidPAC tool to help researchers design energy-dense, long-lived and safe solid-state batteries. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy
Nature Machine Intelligence
Method of Research
Subject of Research
Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement
Article Publication Date