Robert Hettich: Decoding biological complexity with next-gen mass spectrometry
Scientists at the Department of Energy’s Oak Ridge National Laboratory recently demonstrated an autonomous robotic field monitoring, sampling and data-gathering system that could accelerate understanding of interactions among plants, soil and the environment.
A new Global Biomass Resource Assessment developed by ORNL scientists gathered data from 55 countries resulting in a first-of-its kind compilation of current and future sustainable biomass supply estimates around the world.
A study led by the Department of Energy’s Oak Ridge National Laboratory details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
A first-ever dataset bridging molecular information about the poplar tree microbiome to ecosystem-level processes has been released by a team of DOE scientists led by ORNL. The project aims to inform research regarding how natural systems function, their vulnerability to a changing climate and ultimately how plants might be engineered for better performance as sources of bioenergy and natural carbon storage.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.
A research team from Oak Ridge National Laboratory has identified and improved the usability of data that can help accelerate innovation for the growing bioeconomy.