11 - 20 of 23 Results
A human-in-the-loop machine learning (hML) technology potentially enhances experimental workflows by integrating human expertise with AI automation.
The scanning transmission electron microscope (STEM) provides unprecedented spatial resolution and is critical for many applications, primarily for imaging matter at the atomic and nanoscales and obtaining spectroscopic information at similar length scales.
ORNL has developed bacterial strains that can utilize a common plastic co-monomer as a feedstock. This will help enable modern, petroleum-derived plastics to be converted into value-added chemicals.
Due to a genes unique nucleotide sequences acquired through horizontal gene transfer, the gene has a transcriptional repressor activity and innate enzymatic role.
We have developed bacterial strains that can convert sustainable feedstocks and waste feedstocks into chemical precursors for next generation plastics.
ORNL has identified a panel of novel nylon hydrolases with varied substrate and product selectivity.
In scientific research and industrial applications, selecting the most accurate model to describe a relationship between input parameters and target characteristics of experiments is crucial.
This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.
Genetic modification of microbes that are thermophiles—ones that grow at elevated temperatures—is extremely challenging. Tools developed for E. coli, a typical host for protein production, typically do not function at elevated temperatures.
The invention provides a gene and methods for maintaining meiotic chromosomal architecture