Studying how microorganisms behave in space could facilitate farming in the final frontier and lead to better crops on Earth.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.

Evaluating the relative roles of soil water stress and vapor pressure deficit at limiting canopy conductance in coupled land-atmosphere simulations.

Silicone wristbands could be more convenient option for collecting exposure data.

Ancient seawater pockets offer a new source of clues to climate change in vanished oceans and our own.

Tailoring hydrogen-based energy storage solutions to specific real-world applications.

Organismal contribution and community interaction drive soil organic matter breakdown by microbes.

The world's current climate pledges won't limit global warming to 1.5 °C. We will overshoot. A new study shows that more ambitious climate pledges could minimize the overshoot.

PNNL gathered researchers from eight national laboratories plus the U.S. Department of Energy (DOE) to share ideas and build synergy at the Energy Equity and Environmental Justice Summit.

Direct visualization of metal atoms during shear deformation has broad applications from battery design to vehicle lightweighting.

Understanding why the westerly jets shift poleward in certain seasons and regions but equatorward in others under global warming.

A review article examines a decade of work on the atom-scale process of turning carbon dioxide into stable minerals.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

PNNL computational neuroscientist Rogene Eichler West honored with the International Society for Neuroregulation & Research Joseph and Ann Marie Horvat Distinguished Service Award.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new deep learning approach allows researchers to rapidly analyze three-dimensional representations of multi-component catalysts.

Where molybdenum dopants sit within catalytic material affects catalysis at a mechanistic level.

Using electrocatalysis offers a different path for reactions involving polar groups.