TY - JOUR
T1 - Machine learning for guiding high-temperature PEM fuel cells with greater power density
AU - Briceno-Mena, Luis A.
AU - Venugopalan, Gokul
AU - Romagnoli, José A.
AU - Arges, Christopher G.
N1 - Funding Information:
This material is based on work supported by the US Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office (AMO) Award Number DE-EE0009101. This report was prepared as an account of work sponsored by an agency of the US government. Neither the US government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the US government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the US government or any agency thereof. L.A.B.-M. thanks the support received from Universidad de Costa Rica. IDE fabrication and ellipsometry were performed at Louisiana State University's Nanofabrication Facility (NFF) located in the Center for Advanced Microstructures and Devices (CAMD). Conceptualization, L.A.B. G.V. C.G.A. and J.A.R.; Methodology, L.A.B. G.V. C.G.A. and J.A.R.; Investigation, L.A.B. and G.V.; Data Curation, L.A.B. and J.A.R.; Visualization, L.A.B. and J.A.R.; Experiments, G.V.; Writing – Original Draft, L.A.B. G.V. C.G.A. and J.A.R.; Writing – Review & Editing, L.A.B. G.V. C.G.A. and J.A.R.; Funding Acquisition, C.G.A. and J.A.R.; Resources, C.G.A. and J.R.; Supervision, C.G.A. and J.A.R. C.G. Arges is a co-founder of a startup company, Ionomer Solutions LLC, that is in the process of licensing HT-PEM materials (US Patent Application # 62/656,538) developed at Louisiana State University with plans for commercialization.
Funding Information:
This material is based on work supported by the US Department of Energy 's Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office (AMO) Award Number DE-EE0009101. This report was prepared as an account of work sponsored by an agency of the US government. Neither the US government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the US government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the US government or any agency thereof. L.A.B.-M. thanks the support received from Universidad de Costa Rica. IDE fabrication and ellipsometry were performed at Louisiana State University's Nanofabrication Facility (NFF) located in the Center for Advanced Microstructures and Devices (CAMD).
Publisher Copyright:
© 2020 The Authors
PY - 2021/2/12
Y1 - 2021/2/12
N2 - Renewable energy and energy efficiency are crucial for achieving global sustainability goals. In this context, there is need for the development of new materials that realize high-performing and low-cost power sources. At the same time, advances in computational power, simulation, and Machine Learning enable researchers to explore large amounts of data, providing inspiration and tools for the design of new systems. In this work, we combined experiments with modeling and data analysis tools to build a framework for the study and development of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). The framework used Machine Learning tools (e.g., support vector regression, dimension reduction, and clustering) that seamlessly linked materials characteristics with fuel cell performance. This allowed for the accelerated discovery of material properties and fuel cell operating parameters that achieve greater power density while co-currently addressing costs.
AB - Renewable energy and energy efficiency are crucial for achieving global sustainability goals. In this context, there is need for the development of new materials that realize high-performing and low-cost power sources. At the same time, advances in computational power, simulation, and Machine Learning enable researchers to explore large amounts of data, providing inspiration and tools for the design of new systems. In this work, we combined experiments with modeling and data analysis tools to build a framework for the study and development of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). The framework used Machine Learning tools (e.g., support vector regression, dimension reduction, and clustering) that seamlessly linked materials characteristics with fuel cell performance. This allowed for the accelerated discovery of material properties and fuel cell operating parameters that achieve greater power density while co-currently addressing costs.
UR - http://www.scopus.com/inward/record.url?scp=85099643551&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099643551&partnerID=8YFLogxK
U2 - 10.1016/j.patter.2020.100187
DO - 10.1016/j.patter.2020.100187
M3 - Article
C2 - 33659908
AN - SCOPUS:85099643551
VL - 2
JO - Patterns
JF - Patterns
SN - 2666-3899
IS - 2
M1 - 100187
ER -
