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Romagnoli, José A.

Publications and source records attributed to Romagnoli, José A..

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deconvoluting charge-transfer, mass transfer, and ohmic resistances in phosphonic acid–sulfonic acid ionomer binders used in electrochemical hydrogen pumps

Ion-pair high-temperature polymer electrolyte membranes (HT-PEMs) paired with phosphonic acid ionomer electrode binders have substantially improved the performance of HT-PEM electrochemical hydrogen pumps (EHPs) and fuel cells. Here, blending poly(pentafluorstyrene-co-tetrafluorostyrene phosphonic acid) (PTFSPA) with Nafion™, and using this blend as an electrode binder, improved proton conductivity in the electrode layer resulting in a 2 W cm –2 peak power density of fuel cells at 240 °C (a HT-PEM fuel cell record). However, much is unknown about how phosphonic acid ionomers blended with perfluorosulfonic acid materials affect electrode kinetics and gas transport in porous electrodes. In this work, we studied the proton conductivity, electrode kinetics, and gas transport resistances of 3 types of phosphonic acid ionomers, poly(vinyl phosphonic acid), poly(vinyl benzyl phosphonic acid), and PTFSPA by themselves and when blended with Aquivion® (a perfluorosulfonic acid material).

08 HYDROGEN↗
DOE OSTI2022

PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. Furthermore, while robust and better suited for extrapolation, knowledge-based modeling has limitations as it is time-consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling, on the other hand, is easier to implement but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are combined by implementing a few-shot learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFCs was used to generate simulated data (887,735 points) and used to pretrain a neural network source model tuned via a genetic algorithm-based AutoML. Then, experimental datasets from HT-PEMFCs with different materials and operating conditions (~50 points each) were used to train six target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE < 10%).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗