Sustainable energy, green fuels (e.g. H2, NH3, e-fuels), CO2 reduction and utilization, and energy materials; high pressure combustion, plasma assisted combustion and material synthesis, H2/NH3 production, and thermal chemical and electrochemical energy storage materials, in situ laser diagnostics, and machine learning modeling of interfacial plasma catalysis.

Igor Kaganovich is a Principal Research Physicist, is an expert in theoretical plasma physics. He has an extensive publication record with 200 publications on plasma theory, plasma-surface interactions, plasma-based synthesis and processing of nanomaterials, cross-field discharges, and physics of plasma thrusters. His professional interests include plasma physics with applications to nuclear fusion (heavy ion fusion), gas discharge modeling,  plasma processing, nanomaterial synthesis, kinetic theory of plasmas and gases, hydrodynamics, quantum mechanics, nonlinear phenomena and pattern formation. He was elected a fellow of the American Physical Society in 2007. Among many honors, Dr. Kaganovich, along with PPPL physicist Yevgeny Raitses, received PPPL’s Kaul Foundation Prize for Excellence in Plasma Physics Research and Technology Development in 2019. He is also PPPL Distinguished Research Fellow since 2022. He was the recipient of the Alexander von Humboldt Fellowship in 1996.

Organic thin films and organic electronics for photovoltaics: organic/organic heterojunctions that constitute the core of organic photovoltaic cells; interfaces between organic films and conducting oxides or polymer-functionalized surfaces that constitute the high and low work function anode and cathode of these solar cells; hybrid organic/inorganic semiconductor solar cells; metal halide perovskites and their two-dimensional analogs; chemical doping that increases carrier mobility for improved charge collection

The politics of climate change at various scales, including analysis of individual attitudes,  national policies, and international institutions. How characteristics of individuals, states, and multilateral institutions affect climate policy outcomes.

Nonlinear dynamics tools and multiphase flow modeling are used to understand the interplay of reaction and transport in PEM fuel cells. Multiscale modeling of complex systems with applications to chemical reactions, transport processes, and agent-based models of behavioral dynamics and urban growth

Kingsbury’s research aims to accelerate the development of electrochemical technologies for clean water and clean energy production by advancing fundamental understanding of ion-selective materials such as membranes and electrodes. These materials preferentially absorb or transport charged particles like Lithium or Sodium ions and are found in numerous environmental technologies including water desalination systems, fuel cells, electrolyzers, and flow batteries. We seek to develop a molecular-level understanding of the factors that make these materials selective to certain ions, and use that knowledge to engineer higher-performing materials. Our multi-scale research approach integrates electrochemical and thermodynamic materials characterization methods, first principles simulations, and device testing to understand how materials behave under a wide variety of conditions.

Surface and interfacial processes; plasma-materials interactions in fusion reactors; plasma-enhanced catalysis; photoelectrocatalysis for water splitting and carbon dioxide reduction; alloy catalysis; chemistry of the battery solid electrolyte interphase

Politics and policy making that impact environmental outcomes in poor countries, especially India.

Egemen Kolemen is a Professor at Princeton University’s Mechanical & Aerospace Engineering jointly appointed with the Andlinger Center for Energy and the Environment and the Princeton Plasma Physics Laboratory (PPPL). He is the director of the Program in Sustainable Energy, recipient of the David J. Rose Excellence in Fusion Engineering Award and the American Nuclear Society’s Technical Accomplishment Award, and an ITER Scientist Fellow. His research combines engineering and physics analysis to enable economically feasible fusion reactors. He currently leads research on machine learning, real-time diagnostics and control at KSTAR, NSTX-U, and DIII-D. He directs liquid metal divertor and low temperature diagnostics labs. On the theoretical side, his group develops software for stellarator optimization and economical analysis of fusion reactors.