2022 Technical Papers

2022 Technical Papers


A Hierarchical Machine Learning Model to Predict the Modulus of Bi-Soft Segment Polyurethanes 

Calvin Gang, Joseph Pugar, Newell Washburn
Carnegie Mellon University


Advanced Analysis Techniques for Root Cause Polyurethane Failure

Matt Sennett
LANXESS Corporation


Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning

Joseph A. Pugar, Christopher M. Childs, Christine Huang, Karl W. Haider, and Newell R. Washburn
Carnegie Mellon University and Covestro LLC


Mass balanced raw materials for sustainable cast elastomers

Mike Lorenzo
Covestro LLC


The National Institute for Materials Advancement (NIMA) – Sustainable Polyurethanes and Workforce Development

Tim Dawsey, PhD
Pittsburg State University


Development Of High Performance Bio-Based Polyurethane Systems

George Brereton, Ronald M. Emanuel Jr., Gerald King, Dr. Zhenya Zhu
LANXESS Corporation