Metal nanoparticles have a wide range of applications, from medicine to catalysis, from energy to the environment. But the fundamentals of adsorption--the process allowing molecules to bind as a layer to a solid surface--in relation to the nanoparticle's characteristics were yet to be discovered.

New research from the University of Pittsburgh Swanson School of Engineering introduces the first universal adsorption model that accounts for detailed nanoparticle structural characteristics, metal composition and different adsorbates, making it possible to not only predict adsorption behavior on any metal nanoparticles but screen their stability, as well. {module In-article}

The research combines computational chemistry modeling with machine learning to fit a large number of data and accurately predict adsorption trends on nanoparticles that have not previously been seen. By connecting adsorption with the stability of nanoparticles, nanoparticles can now be optimized in terms of their synthetic accessibility and application property behavior. This improvement will significantly accelerate nanomaterials design and avoid trial and error experimentation in the lab.

"This model has the potential to impact diverse areas of nanotechnology with applications in catalysis, sensors, separations and even drug delivery," says Giannis (Yanni) Mpourmpakis, the Swanson School's Bicentennial Alumni Faculty Fellow and associate professor of chemical and petroleum engineering, whose CANELa lab conducted the research. "Our lab, as well as other groups, have performed prior computational studies that describe adsorption on metals, but this is the first universal model that accounts for nanoparticle size, shape, metal composition and type of adsorbate. It's also the first model that directly connects an application property, such as adsorption and catalysis, with the stability of the nanoparticles."

As hurricanes grow in power as the climate changes, accurately modeling the interactions between the atmosphere and the ocean grows increasingly important to prepare people to batten down or to evacuate. The many mechanisms of the atmosphere-ocean system -- known as air-sea flux -- make modeling extremely complicated, however.

Qi Shi, a postdoctoral researcher in the Great Lakes Research Center at Michigan Technological University, has created the first detailed analysis of ocean and atmospheric responses to currents, waves and wind. In the article "Coupling Ocean Currents and Waves with Wind Stress over the Gulf Stream"published in Remote Sensing this summer, Shi argues that current numerical models simply don't account for the impact of waves, currents and wind coupled together. This coupling is crucial because without it, models do not accurately represent marine atmospheric boundary layer processes.

"We quantified the impact of this coupling to improve the accuracy of air-sea fluxes, because without modeling currents, there is a constant bias in models," Shi said. "What causes that bias? Missing the full spectrum of feedback mechanisms." {module In-article}

Simply put: Better modeling gives weather forecasters and climate scientists a more accurate picture of what happens where atmosphere and ocean meet.

Feedback Mechanisms

Part of what makes modeling air-sea flux so complicated are the sheer number of feedback mechanisms in the system: To model waves, one must account for surface roughness and wind; to model sea surface temperature, one must account for air-sea temperature differences, water vapor, humidity, evaporation and more. Modeling wind and surface currents are equally complex. 

Numerical models solve equations that describe the atmosphere, ocean, and land surface to predict future weather and climate. Interactions among each model component, such as heat exchange between atmosphere and ocean, play an important role in driving both oceanic and atmospheric circulation.

Hurricanes and Climate

Hurricanes are fueled with heat and moisture from the ocean. Ocean currents and waves modify wind shear and surface roughness, which are key variables for calculating the air-sea heat and momentum fluxes. Using a high-resolution, three-way coupled ocean-wave-atmospheric modeling system, Shi determined the role of coupling ocean currents, waves and wind stress in reducing model bias in air-sea flux over the Gulf Stream.

Shi's work is the first detailed mechanism study in the current-wave-stress coupling process, which can be applied to increase the accuracy of forecasts for hurricane intensity and climate prediction as well as to better use satellite observations in the numerical models. 

"We provide evidence that observation of currents is important and has significant influence on models," Shi said.

Shi said she hopes to see the eventual launch of a satellite that observes ocean currents to validate ground observations.