FALL 2026 PROJECTS
Effects of Aerosol Jet Printing Parameters on the Lifetime Performance of Additively-Manufactured Flexible Circuits
Biomedical devices such as implantable electronics and flexible sensors can be enhanced through the use of Aerosol Jet Printing (AJP) as a micro fabrication technique. A more affordable and customizable alternative to traditional methods, AJP allows for microgabrication outside of a clean room. However, there is limited research regarding its efficacy. Dr. Gbur, Mitchell Melander, a MS student, and their team aim to fill this gap by studying the qualitative and quantitative characteristics of AJP flexible sensors and the morphology of their conductive traces. Successful prints are studied through extensive mechanical and electrical testing to ensure that the ink’s adhesion to the substrate can withstand bending forces without impacting its electrical resistance. From their data collection, the team intends to develop processing maps that correlate to printing parameters in order to create a guide for future development of flexible sensors.
Janet Gbur
Materials Science & Engineering
Case Western Reserve University
Enhancing Battery Degradation Analysis and Thermal Runway Prediction
This project focuses on thermal runaway (TR), a major safety risk in lithium-ion batteries where self-heating can lead to fire, explosion, or gas release. While relatively rare in single cells, the risk increases significantly in systems with many cells, making TR a critical issue in modern electronics and vehicles.
Dr. Iyengar and his team are collecting data from experiments, incident reports, and related phenomena to identify early indicators of TR. They are applying ARIMA models for stationary voltage and temperature data, and nonlinear approaches such as splines and neural networks for highly nonstationary periods. A known PDE model for temperature is incorporated with noise to explore TR behavior in a stochastic setting. This work aims to improve prediction accuracy, generate simulation data, and provide insights into the mechanisms underlying thermal runaway.
Satish Iyengar
Department of Statistics
University of Pittsburgh
Multimodal Formulation
This project focuses on thermal runaway (TR), a major safety risk in lithium-ion batteries where self-heating can lead to fire, explosion, or gas release. While relatively rare in single cells, the risk increases significantly in systems with many cells, making TR a critical issue in modern electronics and vehicles.
Dr. Iyengar and his team are collecting data from experiments, incident reports, and related phenomena to identify early indicators of TR. They are applying ARIMA models for stationary voltage and temperature data, and nonlinear approaches such as splines and neural networks for highly nonstationary periods. A known PDE model for temperature is incorporated with noise to explore TR behavior in a stochastic setting. This work aims to improve prediction accuracy, generate simulation data, and provide insights into the mechanisms underlying thermal runaway.
John Kitchin
Carnegie Mellon University
Chemical Engineering
Quantitative Characterization of Chemical Interactions of Solutes with Defects for Predicting Intergranular Corrosion
Dr. Hyeji Im and their team are investigating the potential of laser additive manufacturing (AM) to develop molybdenum alloys as fuel cladding materials with enhanced corrosion resistance. This research has two main aims: (1) explore the potential of laser AM as a viable processing route to develop molybdenum alloy as a fuel cladding material, and (2) generate quantitation input data on thermodynamic and diffusional interactions between defects and solutes, which can serve as a guide for predicting intergranular corrosion.
By introducing silicon (Si) and boron (B) into Mo grain boundaries, the team aims to improve cohesion and mitigate impurity segregation, strengthening the material against intergranular failure. To further enhance corrosion resistance, the researchers are employing a novel laser exposure technique to introduce surface defects, promoting the rapid formation of protective oxide layers. Additionally, they are generating precise datasets on defect-solute interactions for machine learning models, which can predict intergranular corrosion and inform material design strategies. This research has the potential to extend the lifetime of nuclear materials and improve reactor resilience under extreme conditions.
Hyeji Im
Materials Science & Engineering
Case Western Reserve University
Good Vibrations: Developing a Standard for Time-Sonication Superposition to Accelerate Characterization of Aqueous Polymer Degradation
This project will use data science to predict long-term polymer degradation in aqueous environments based solely on experimental data obtained from short-duration tests, combined with chemical and mechanical information about the polymer. Experimental measurements will be conducted under sonication to accelerate polymer breakdown relative to natural conditions. The work will establish a data-driven framework for time–sonication superposition, analogous to the well-known time–temperature superposition principle, enabling reliable extrapolation of degradation behavior over extended timescales.
Stephanie A. Sydlik
Carnegie Mellon University
Department of Chemistry
Gerald J. Wang
Carnegie Mellon University
Civil & Environmental Engineering
Electrodeposition of Protective Coatings for Harsh Environment
The primary objectives of this project are to (1) develop the electrodeposition technique of uniform and durable coatings by combining experimental approach and machine learning frameworks, (2) explore an in-situ monitoring approach that allows for real-time adaptation of process parameters during the electrodeposition process, and (3) evaluate the effect of electrodeposited coatings on the lifetime of substrate materials in harsh environment (e.g., high temperature or oxidizing conditions).
Jung-kun Lee
University of Pittsburgh
Materials Science & Engineering
Development of Process Reliability Parameters for Dry Spray Coating Technique
This project develops process reliability parameters for powder aerosol deposition (PAD), a room-temperature, solvent-free dry spray technique that produces dense, highly adherent ceramic and metallic coatings without high-temperature sintering. The team is building a multi-dimensional experimental dataset paired with computational fluid dynamics (CFD) and finite element (FE) process models, targeting a mid-term goal of established coating processing-quality relationships and a long-term goal of ML-based inverse design for optimal process parameters.
Neamul Khansur
Case Western University
Materials Science & Engineering
Explainable AI based Electrical Grid Fire Resilience Assessment and Predictive Analytics
This project aims to develop an AI- and ML-driven model to predict and assess fire risk in electrical grids. By applying explainable AI (XAI), the research will identify hidden patterns and causal relationships between grid operational stressors early fire indicators under harsh environmental conditions, enabling transparent and interpretable fire resilience predictions.
