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

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

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

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

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.

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

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

The amount of radiation in a system influences how fast aggregated boehmite particles dissolve.

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.

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

Simulations show that pH influences boehmite nanoparticle structure and sorption ability.

A new simple and scalable synthesis produces nanoparticle assemblies that can perform catalytic hydrogen sensing at room temperature for the first time.

The buried interfaces between sections of a doped material can play a key role in the overall electronic properties of the system.

PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.

PNNL researchers created a model that helps materials scientists not only predict what is, but what could be.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

Researchers are studying the chemical reactions that occur when oxygen mixes into the surface of oxide materials.

Combining multiple experimental techniques allowed researchers to observe the early stages of oxidation in a model material.