Newly formed atmospheric aerosol particles exert a considerable impact on global climate by affecting the Earth's radiation balance. Nucleation plays a pivotal role in the formation of these particles. Understanding how particles nucleate in a multi-component gas mixture has important implications not only for climate and weather but also wide-ranging technological applications including gas separations, pollution control, and nanotechnology. Atmospheric nucleation involves multi-scale processes ranging from proton transfer to molecular condensation and evaporation events and culminating in the rare formation of the critical nucleus. The goals of this project are (i) to develop computational algorithms and analysis tools for efficient investigations of multi-component gas-to-particle nucleation processes, (ii) to elucidate atmospherically relevant nucleation processes and to validate the rate predictions through strategically selected laboratory experiments measuring cluster size and mass distributions at the sub-3 nm scale, and (iii) to deploy a freely-available cyber-tool that transforms data to knowledge by enabling large-scale modelers and experimental researchers to harvest predicted atmospheric nucleation rates and learn about mechanisms, by providing a general framework to visualize and analyze the abundance of digital data generated by particle-based simulations for any type of gas-to-particles nucleation process, and by being an aid for teaching about nucleation.
The project impacts our understanding of atmospheric nucleation pathways and sheds light on how quantitative modeling of the nucleation kinetics affects global climate models and impacts the ability to influence atmospheric nucleation. Driven by the partnership of researchers from different fields, diverse academic institutions, and international collaborators, the education, training, and mentoring of undergraduate and graduate students is advanced in a unique way that broadens participation. Knowledge gained from this project infuses the excitement of discovery in courses and laboratories taught by the team members. Outreach activities to junior high schools and science museums allow a broader community to learn about atmospheric nucleation.
This is a Cyber-Enabled Discovery and Innovation Program award and is co-funded by the Division of Chemistry, the Division of Civil, Mechanical & Manufacturing Innovation, the Office of International Science & Engineering, and the Experimental Program to Stimulate Competitive Research.
Newly formed atmospheric aerosol particles exert a considerable impact on global climate by affecting the Earth’s radiation balance. Nucleation plays a pivotal role in the formation of these particles. Understanding how particles nucleate in a multi-component gas mixture has important implications not only for climate and weather but also wide-ranging technological applications including gas separations, pollution control, and nanotechnology. Atmospheric nucleation involves processes occurring on disparate time scales, and pre-critical clusters are characterized by emergent behavior (e.g., formation of double ions due to proton transfer) and self-organization due to differences in micro-solubility. Atmospheric nucleation shows distinct non-linear dependencies on the vapor-phase composition and the feedback between the different elementary processes does not allow for reductionism. Due to this complexity many fundamental questions about atmospheric nucleation pathways remain unanswered and quantitative modeling of the nucleation kinetics is not yet possible. This leads to substantial uncertainties in global climate models and impacts the ability to influence atmospheric nucleation. The goals of this research are (i) to develop computational algorithms and analysis tools for efficient investigations of multi-component gas-to-particle nucleation processes, (ii) to elucidate atmospherically relevant nucleation processes and to validate the rate predictions through strategically selected laboratory experiments, and (iii) to deploy a freely-available cyber-tool that transforms data to knowledge by enabling large-scale modelers and experimental researchers to harvest predicted atmospheric nucleation rates and learn about mechanisms, by providing a general framework to visualize and analyze the abundance of digital data generated by particle-based simulations for any type of gas-to-particles nucleation process, and by being an aid for teaching about nucleation. This collaborative team (including Kelly Anderson from Roanoke College, Jinzhu Gao from the University of the Pacidic, David Hanson from Augsburg College, and Peter McMurry, Ilja Siepmann, and Donald Truhlar from the University of Minnesota-Twin Cities) has spearheaded innovations in computational thinking by synergistically developing problem-driven and efficient Monte Carlo algorithms and highly accurate electronic structure methods interfaced with equilibrium and dynamics sampling. The team has developed and released a cyber-tool (pacificscience.com/CTIANP/index.php) for distributed large-scale data analysis and remote visualization facilitating knowledge generation and transfer. Coupled with emerging experimental tools for the measurements of cluster size and mass distributions at the sub-3 nm scale, the team proposed a model that predicts observed nucleation rates in the atmosphere to within a factor of ten, whereas earlier models often yielded disagreements by many orders of magnitude. Driven by the collaborative partnership, the education, training, and mentoring of three postdoctoral associates, three graduate students, more than ten undergraduate students, and two high school students has been advanced in a unique and successful way. The team has also participated in numerous outreach activities aimed at recruiting students into the STEM fields.
