The Simple Building Calculator, developed at PNNL, meets a need for a quick, interactive, and economic method to evaluate energy use—and potential savings from efficiency measures—in simple commercial buildings.

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

Advancing the science of radiation, especially among students at minority-serving institutions, is the goal of one of the Department of Energy’s newest consortia.

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

PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.

Across the United States, water moving between the river and riverbed sediments does not overcome localized processes that govern organic matter chemistry.

PNNL experts in nuclear forensics helped lead laboratories around the world through a rigorous test involving nuclear materials.

Eight Office of Science Graduate Student Research join PNNL.

New research simulates power demand, supply, and cost in a hotter, drier climate.

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.

Pacific Northwest National Laboratory researchers developed a patented, nearly non-destructive approach, known as liquid secondary ion mass spectrometry, to analyze nuclear samples.

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.