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Developing Advanced Materials for a Sustainable Energy Future

UMass Lowell researcher Fanglin Che and her group use computers at the MGHPCC in their work centered around AI-guided multi-scale and multi-physics simulations of catalysis and material science.

The usability and costly storage issues of renewable electricity from solar or wind energy become major challenges on a global scale due to the daily and seasonal variability of sunlight or wind and the geographic inequality of energy needs. A promising solution to address the above challenges lies in electrified modular chemical processes, which provide a sustainable approach to store intermittent energy chemically.

Theoretically determining and quantifying the roles of electrified interfacial structure and field-dipole interactions on controlling the activity and selectivity of chemical processes and then integrating these roles to establish deep collaborations between physics-informed, interpretable machine learning and electrified interfacial chemical processes is crucial for rationally designing catalysts for these electrified modular systems for energy storage and sustainable chemical production.

Research in the Che Group has focused on several systems that take advantage of process intensification by converting abundant resources, e.g., carbon dioxide, natural gas, and nitrogen, into valuable chemicals. The systems include: (1) organic-inorganic interface and its impact on carbon reactive capture and selective conversion; (2) field-dipole interaction effects on ammonia decomposition and synthesis for hydrogen utilization, storage, and production.

Copper makes a great catalyst for turning carbon dioxide into useful chemicals, but it has some limitations. To improve its catalyzing qualities, researchers have added tiny amounts of platinum or similar metals to copper. This combination helps the copper convert CO2 into chemicals instead of making hydrogen, which is an unwanted side reaction. The researchers found that introducing small clusters of platinum or palladium onto copper surfaces can effectively produce chemicals like methane and ethylene. This new approach gives more options for using copper in CO2 reduction.

Fanglin Che, Ph.D.
Assistant Professor, UMass Lowell

Research projects

A Future of Unmanned Aerial Vehicles
Yale Budget Lab
Volcanic Eruptions Impact on Stratospheric Chemistry & Ozone
The Rhode Island Coastal Hazards Analysis, Modeling, and Prediction System
Towards a Whole Brain Cellular Atlas
Tornado Path Detection
The Kempner Institute – Unlocking Intelligence
The Institute for Experiential AI
Taming the Energy Appetite of AI Models
Surface Behavior
Studying Highly Efficient Biological Solar Energy Systems
Software for Unreliable Quantum Computers
Simulating Large Biomolecular Assemblies
SEQer – Sequence Evaluation in Realtime
Revolutionizing Materials Design with Computational Modeling
Remote Sensing of Earth Systems
QuEra at the MGHPCC
Quantum Computing in Renewable Energy Development
Pulling Back the Quantum Curtain on ‘Weyl Fermions’
New Insights on Binary Black Holes
NeuraChip
Network Attached FPGAs in the OCT
Monte Carlo eXtreme (MCX) – a Physically-Accurate Photon Simulator
Modeling Hydrogels and Elastomers
Modeling Breast Cancer Spread
Measuring Neutrino Mass
Investigating Mantle Flow Through Analyses of Earthquake Wave Propagation
Impact of Marine Heatwaves on Coral Diversity
IceCube: Hunting Neutrinos
Genome Forecasting
Global Consequences of Warming-Induced Arctic River Changes
Fuzzing the Linux Kernel
Exact Gravitational Lensing by Rotating Black Holes
Evolution of Viral Infectious Disease
Evaluating Health Benefits of Stricter US Air Quality Standards
Ephemeral Stream Water Contributions to US Drainage Networks
Energy Transport and Ultrafast Spectroscopy Lab
Electron Heating in Kinetic-Alfvén-Wave Turbulence
Discovering Evolution’s Master Switches
Dexterous Robotic Hands
Developing Advanced Materials for a Sustainable Energy Future
Detecting Protein Concentrations in Assays
Denser Environments Cultivate Larger Galaxies
Deciphering Alzheimer’s Disease
Dancing Frog Genomes
Cyber-Physical Communication Network Security
Avoiding Smash Hits
Analyzing the Gut Microbiome
Adaptive Deep Learning Systems Towards Edge Intelligence
Accelerating Rendering Power
ACAS X: A Family of Next-Generation Collision Avoidance Systems
Neurocognition at the Wu Tsai Institute, Yale
Computational Modeling of Biological Systems
Computational Molecular Ecology
Social Capital and Economic Mobility
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Outreach & Education Projects

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