Janna Maranas Research Group




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Research Topics

This section discusses the practical applications of our research areas.

> Solid polymer electrolytes for Li-ion batteries
> Biology and bio-molecular engineering
> Fuel cells
> Coarse-grained modeling


Solid polymer electrolytes for Li-ion batteries
Susan Fullerton, Kokonad Sinha, Kan-Ju Lin, Wenjin Shi

Engineering a light, flexible and environmentally-friendly lithium-ion battery

We use neutron scattering to investigate solid polymer electrolytes for rechargeable lithium-ion batteries. The problem with solid polymer electrolytes is that room temperature conductivity is too low to power a portable device. [For more information on solid polymer electrolytes visit the student pages]. By understanding the molecular-level mechanism driving conductivity, we can modify the electrolyte to increase conductivity to a practical level.

Solid polymer electrolytes in Li-ion batteries

Neutron scattering is a tool that allows us to investigate both the mobility of the polymer and structures within the electrolyte on time scales (picoseconds – nanoseconds) and length scales (angstroms – nanometers) relevant to this problem. Polymer mobility is thought to be important because it likely drives lithium-ion mobility - the mobility of the lithium-ion determines the conductivity of the electrolyte. The structure of the electrolyte is important because the presence of structures such as channels could possibly provide a pathway to speed-up lithium ion mobility.

Polymer mobility can be measured directly using neutron scattering because the hydrogen atoms in the polymer scatter neutrons much more strongly than any other element. The only species in the electrolyte that contains hydrogen is the polymer, therefore we are able to directly measure its mobility in the presence of other species. We use DCS and HFBS to measure polymer mobility.

The structure of the electrolyte is measured using SANS based on the neutron scattering contrast between the polymer and the other species in the system. The other species can include lithium salts, lithium ions, or in some cases, an oxide nanoparticle additive.

We are going to use Molecular Dynamics (MD) simulation to study how lithium ion transports in solid state electrolytes. MD simulation provides an evolution in time of the positions of all atoms. Although the mobility of Li+ may be directly measured using the dielectric and Li-NMR techniques, this does not reveal the exact path these ions take. Do they move continuously from one ion cluster to another? Do they leave an anion only to revisit it shortly thereafter? How long do Li+ ions coordinate with a given anion or ion cluster? All these questions are readily addressed by simulation, but not by any other method.

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Biology and bio-molecular engineering
Maria Antonieta Sanchez Farran, Ryan Bradley, Ana Vila Verde, Victoria Garcia Sakai

Alzheimer’s disease (AD) is the most common neurodegenerative disease. According to the statistics released by the Alzheimer’s Association in 2008 there are 5.2 millions of Americans suffering this disease, and it is now the sixth leading cause of death in the United States according to the Centers for Disease Control and Prevention (Source).

Since it was first defined by Dr. Alois Alzheimer in 1907, research efforts have focused on determining the origin of neuronal loss and presence of abnormal protein aggregates in the brain of AD patients. Currently, the most accepted hypothesis in this field is that aggregates containing amyloid beta protein are toxic to the cell. However, the mechanisms by which these aggregates are formed and how they trigger cell death are not yet fully understood, and remain a crucial step toward developing effective therapies against Alzheimer’s.

In this context, molecular dynamics simulations is a valuable tool for gaining insight into the molecular details that could explain the factors that influence the aggregation process, and is one of our research topics in the group.

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Fuel Cells
Erin Boland

(Coming soon from Erin Boland)

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Coarse-grained modeling
Praveen Depa, Chunxia Chen

(Coming soon)

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Department of Chemical Engineering, The Pennsylvania State University

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