Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.
The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.
Two new publications provide emergency response agencies with critical insights into commercially available unmanned ground vehicles used for hazardous materials response.
To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.
PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.
CESER and PNNL convened a three-day summit with more than 100 state officials, cybersecurity experts, and industry leaders across 35 states to advance energy security planning, cyber risk assessment, and fortify protections against attacks.
This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.
The Wildfire Mitigation Plan Database was built to support electric utilities, state governments, policymakers, and regulators in understanding and improving wildfire risk and resilience strategies.
Ampcera has an exclusive licensing agreement with PNNL to commercially develop and license a new battery material for applications such as vehicles and personal electronics.
The ARPA-E Energy Innovation Summit brings together researchers, industry leaders, entrepreneurs, and investors to showcase the latest technologies shaping tomorrow’s energy landscape. This year, eight projects led by PNNL were featured.
The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.
EZBattery Model allows energy storage researchers to more quickly and easily identify the best performing battery designs without the need for extensive physical prototyping or computationally expensive simulations.
PNNL will analyze current and projected transportation fuel dynamics, supply chain risks, and risk comparators with relevant sectors, such as transportation electrification.
Global experts gathered at PNNL for the 9th International Conference on Sodium Batteries, sharing advancements in sodium battery research and development.
The Sodium-ion Alliance for Grid Energy Storage, led by PNNL, is focused on demonstrating high-performance, low-cost, safe sodium-ion batteries tested for real-world grid applications.
