Water affects the intermediates present on the surface of a metal oxide catalyst during ketonization.

Led by PNNL, the new Cathode-Electrolyte Interphase Consortium has been launched, involving 10 national laboratories and 10 universities.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Variations in burn severity are a key control on the chemical constituents of dissolved organic matter delivered to streams within a single burn perimeter.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

A combined experimental and computational approach showed that the hydration shell of hydroxide has a four-coordinate binding.

Newly developed models can help rapidly optimize systems to meet the need for rapidly deployable commercial carbon capture.

Combining peptoids and carbon nanotube porins creates a new class of stable membranes with potential applications in separations science.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

A combined experimental and modeling study shows that electronic interactions direct how a metal-organic framework grows.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

Hydrogen bonding and proton transfer control the identity and reactivity of molecules in highly concentrated and alkaline aluminate solutions.

A new system can convert common plastics into valuable fuel molecules.

Within zeolite pores, rates of alcohol dehydration increase with ionic strength until reaching a maximum and then decreasing.

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.

Researchers combined crystallographic data and computational studies to investigate plutonium-ligand bonding within a hybrid material construct.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

In a water-containing system, the open circuit potential stabilizes positively charged species and lowers the energy barrier of the reaction.

Harnessing the sea for decarbonized energy is the focus of a new collaboration between PNNL and the University of Guam.

A new approach broadens the reach of quantum computers to previously challenging systems.