Bulk Photovoltaics

One of our research focuses at The Rappe Group is the bulk photovoltaic effect (BPVE). In contrast to the traditional photovoltaic effect where a heterostructure is required, bulk photovoltaic effect (BPVE) or photogalvanic effect could generate a photocurrent in a homogeneous material. In the language of perturbation theory, BPVE is a second-order process which involves interaction of electrons with two photons. This is a promising area of study that differs from the “p-n junction” solar cell mechanism and could help create next-generation solar cells. 

Supported by:
DOE TCMP
NSF STC IMOD (relates to bulk PV)

Catalysis for Energy

The Rappe group uses first-principles modeling to discover and design catalysts for a number of technologies, specifically energy storage devices such as batteries and fuel cells. Through computation, the group tests catalytic activity of promising materials in various environments, searching for the most effective catalysts in terms of activation energy, cost, abundance, and safety. Through kinematic and thermodynamic modeling, the group also analyzes the effects of added substrates, solvents, and ligands on reaction energy. Recent work has focused on the catalytic activity of transition metal catalysts and their oxides. 

Supported by:
DOE Catalysis
NSF STC IMOD
NSF FMRG MXenes
NSF LEAP HI adhesion and contact for mechanical computing
VIEST single-atom catalysts (in collaboration with Tom Mallouk)
FE catalysis
topological catalysis
ai-GCMC method 

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Ferroelectrics

Ferroelectric materials have spontaneous electrical polarization, which refers to the separation of the negative and positive charges in a material. That characteristic can be reversed directionally with the application of an appropriate electric field. Ferroelectric material is composed of crystals, in which the structural units are tiny electric dipoles.  

When applied, ferroelectrics can be used for computing, because the material’s prior state can be used to store information similarly to a traditional hard disk. This could have benefits for computer memory and could make next-generation non-volatile memory devices.  Additionally, the group explores the use of these compounds in SONAR technology, electronics (including RAM and nanocapacitors), photovoltaics, and catalysis. 

Supported by:
ONR FE for sensing
CHARM FE for responsive materials
DOE 3DFEM FE for next-gen computing
Relaxor ferroelectrics
FE nanomaterials
FE catalysis
FE domain dynamics
BVMD method

Light-Matter Interactions

The interaction of light with matter is vital for applications in material processing and more. For example, the controlled generation of light from solid-state materials is fundamental in lasers. Furthermore, functional materials that respond to external stimuli can be combined into tunable nanophotonic devices, as well as other applications.  

Supported by:
NSF STC IMOD
CHARM FE for responsive materials
topological insulators

Mechanochemistry

Mechanical chemistry studies the use of mechanical phenomena to induce chemical reactions. Mechanochemistry eliminates the need for many solvents, which may help make many industries more environmentally friendly. This process can be an energy-saving, highly productive, and low-temperature process. Additionally, this field is rapidly advancing and may provide new avenues to make materials and molecules.  

Supported by:
NSF CCI CMCC mechanochemistry (relates to nanomaterials)
NSF LEAP HI adhesion and contact for mechanical computing

Nanomaterials

Nanomaterials describe materials in which the unit size is between 1 and 100 nm. Materials at this scale tend to have unique properties, including electronic and thermo-physical properties. Subsequently resulting from this field of study are MXenes, which are ceramics made from the bulk crystal MAX. MXenes have good conductivity and volumetric capacity. Creating 2D nanomaterials like MXenes could be vital in creating better microchips, batteries, and other devices.  

In addition, nanomaterials are created by using artificial intelligence, grand canonical Monte Carlo methods (Ai-GCMC). Ai-GCMC can run numerous computational scenarios at an atomic level, relying on repeated random sampling to obtain numerical results.  

Supported by:
NSF FMRG MXenes (relates to catalysis)
NSF CCI CMCC mechanochemistry (relates to nanomaterials)
FE nanomaterials

Next-Generation Computing

Next generation computing is vital for running accurate calculations, that can translate theoretical simulations into experimental, real-world, practices. By researching using methods like artificial intelligence, and researching topics like next-generation ferroelectrics for computing, as well as mechanical low-power computing elements, the Rappe Group aims to make computing more accurate and sustainable. 

Supported by:
3DFEM FE for next-gen computing
NSF LEAP HI adhesion and contact and surface chemistry for mechanical computing
NSF FMRG MXenes
OPIUM method

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