PNNL chemist Christopher Anderton recently named president-elect of the Imaging Mass Spectrometry Society (IMSS). In this new position, he will help lead the merge of IMSS with a European-based society, currently underway.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

Advanced proteomic analysis tools dig deep into human blood plasma to understand the molecular nature of advanced alcoholic disease.

A detailed snapshot of proteins by PNNL scientists marks a big step toward a diagnostic blood test for a deadly form of liver disease.

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.

Mowei Zhou, a chemist with the Environmental Molecular Sciences Laboratory, is speaking at the ACS spring conference on his latest protein discoveries for a plant that could transform biofuels production.

Development of a one-meter path length miniature ion mobility spectrometer based on multilevel structures for lossless ion manipulations.

PNNL scientists are protecting first responders and others by expanding what’s known about fentanyl, the driver of the opioid epidemic.

In adjoining Energy Sciences Center laboratories, researchers develop better energy storage devices by understanding the fundamental reactions that form interfaces.

Richard (Dick) Smith and Ljiljana (Lili) Paša-Tolić are experts in developing technology and techniques for mass-spectrometry-based multi-omics measurements.

Kristin Burnum-Johnson presented at two virtual events in June on proteome mapping.

PNNL is lending analytics expertise to a new first-of-its-kind electric-vehicle carsharing program being launched in Hood River, Oregon.

A webapp developed by PNNL in collaboration with the University of Washington to help drive efficiencies for urban delivery drivers is now in the prototype stage and ready for testing.

Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.

Researchers at PNNL are contributing artificial intelligence, machine learning, and app development expertise to a U of W project that will ease challenges with urban freight delivery. The project will provide delivery drivers with a tool