Fuel Cell Scientific Insights Series

Webinar / May 20, 2026

Fuel Cell Scientific Insights Series

Fraunhofer ISE presents its latest research findings to industry professionals, academics, and funding partners interested in fuel cells. Each session will offer an in-depth exploration of one of the publications, detailing not only the most recent research but also the methodologies and scientific expertise that support these results. This is a unique opportunity to explore Fraunhofer ISE's research and discover how it can be applied to your specific needs.

We look forward to exchanging ideas with you!

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Event details

Location

Online, via Microsoft Teams

Date

May 20, 2026

Organization

Fraunhofer Institute for Solar Energy Systems ISE

Upcoming Webinars

Welcome to our Upcoming Webinars section!

In this section, you will find a schedule of our upcoming webinars. We invite you to register and join us. If you have any questions or need more information, please do not hesitate to reach out.

PEMFC Performance Modeling

May 20, 2026 | 3:00 - 4:30 p.m. CEST

Online via Microsoft Teams

This month, we are excited to provide insights into our activities, specifically Modeling of PEM Fuel Cells. You will explore with Dr. Dietmar Gerteisen the methods and results of his recent publication, followed by an interactive session in the second half.

To the paper

Innovative Ionomer Materials

June 17, 2026 | 3:00 - 4:30 p.m. CEST

Online via Microsoft Teams

This month, we are excited to provide insights into our activities, specifically innovation in ionomer materials. You will explore with Dr. Faezeh Makhlooghi Azad and Mr. Linus Hirth the methods and results of their recent publication, followed by an interactive session in the second half.

To the paper

Past Webinars

Welcome to our Past Webinars section!

Here, you can revisit the insightful sessions that have already taken place. If you have any questions or would like to discuss the content further, feel free to contact the respective author directly.

Technoeconomic Analysis of CCM Production

Our eighth talk in the series provided insights into our activities, specifically the technoeconomic analysis of catalyst coated membrane production.

High manufacturing costs of proton exchange membrane (PEM) fuel cells remain a significant obstacle to their widespread adoption in transportation applications. To address this, roll-to-roll (R2R) processes are proposed to increase production throughput and reduce membrane electrode assembly (MEA) costs. Among R2R-compatible coating processes, slot-die coating (SDC) represents the state-of-the-art technology, while rotary screen printing (RSP) offers advantages in structured and intermittent printing, providing additional design freedom for catalyst layers (CL). This study examines the cost implications of both coating processes for a specific set of R2R machines to be implemented at Fraunhofer ISE. Technical boundary conditions and operational limitations are evaluated and integrated into a comprehensive continuous process cost of ownership model capturing expenses throughout the production chain, from catalyst ink raw materials to the final seven-layer MEA. A methodology is presented for determining consumable costs at various purchasing volumes, combining literature values with supplier quotations. By integrating performance data from experimentally produced MEAs with the cost model, an approach is developed to determine optimum platinum (Pt) loadings for cost per kW for given materials. The identified optimal Pt loadings of 0.44–0.54 mgPt cm−2 demonstrate that the cell performance is crucial for further cost reductions of fuel cells.

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To the paper

Imaging of Catalyst Layers

This seventh talk in the series provided insights into our collaboration activities, specifically imaging of catalyst layers.

This study establishes quantitative correlations between catalyst layer (CL) microstructure, electrochemical performance, and degradation in proton exchange membrane fuel cells (PEMFCs) as a function of ionomer-to-carbon (I/C) weight ratio and relative humidity (RH). Conventional cathode catalyst layers were analyzed using high-resolution SEM, epoxy-free TEM, and STEM-EDS mapping, combined with automated deep-learning and Python-based image quantification. Structural descriptors—including CL thickness, porosity, ionomer coverage, connectivity, and platinum (Pt) particle size and migration to the membrane —were quantified and correlated with in-situ performance and accelerated stress test data. Increasing I/C ratio enhanced ionomer connectivity and proton transport under dry conditions but also promoted Pt dissolution, migration, and particle growth, especially under humid operation. High RH amplified Pt ion mobility and Pt band formation in the membrane (PITM), leading to significant electrochemically active surface area (ECSA) loss. Principal component analysis (PCA) revealed two competing degradation modes: catalyst transport–driven degradation governed by ionomer connectivity and humidity, and ionomer-related structural densification dominating under dry conditions. The findings highlight a critical trade-off—enhanced ionomer connectivity improves short-term performance yet accelerates long-term degradation. This data-driven quantitative microscopy framework provides mechanistic insight and practical guidance for optimizing CL architecture and humidity management to achieve durable, high-performance PEMFC electrodes.

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To the paper

Next Generation Catalysts

This sixth talk in the series provided insights into our collaboration activities, specifically next generation catalysts.

Recent studies have shown the importance of porous carbon supports in achieving high-performance proton exchange membrane fuel cells (PEMFCs). Porous carbon supports control the cascaded oxygen and proton mass transport through the intraparticle porosity inside the electrocatalyst and through the interparticle porosity throughout the electrode. Here, two monodisperse mesoporous (10 and 28 nm pore size) N-doped carbon (MPNC) nanosphere supports with large particle sizes (>100 nm) are presented as supports for the design of advanced Pt-based PEMFC cathode electrocatalysts. The morphology and structure of the MPNC nanosphere supports and derived catalysts enable a 3D bottom-up design of the catalyst layer in terms of intra- and interparticle porosities, thereby positively impacting the fuel cell performance. Characterization at rotating disc electrode (RDE) and PEMFC single-cell level reveals the strong potential of the MPNC as catalyst support materials, combining excellent oxygen transport properties and improved mass activities. The MPNC-based catalysts achieve 0.66 W cm−2 at 0.76 V in single-cell configuration, outperforming state-of-the-art reference catalyst material by 27%. The results confirm not only the advantages of mesoporous N-doped carbons as advanced catalyst supports but also highlight the underexplored potential of tailoring performance determining parameters of the carbon support such as pore size and particle size.

To the paper

Modeling the Morphology of Porous Carbon Supports of PEMFC

This fifth talk in the series provided insights into our modeling activities, specifically porous structure modeling.

We present a model of the cathode catalyst layer morphology before and after loading a porous catalyst support with Pt and ionomer. Support nanopores and catalyst particles within pores and on the support surface are described by size distributions, allowing for qualitative processes during the addition of a material phase to be dependent on the observed pore and particle size. A particular focus is put on the interplay of pore impregnation and blockage due to ionomer loading and the consequences for the Pt/ionomer interface, ionomer film thickness and protonic binding of particles within pores. We used the model to emulate six catalyst/support combinations from literature with different porosity, surface area and pore size distributions of the support as well as varying particle size distributions and ionomer/carbon ratios. Besides providing qualitatively and quantitatively accurate predictions, the model is able to explain why the protonically active catalyst surface area has been reported to not increase monotonically with ionomer addition for some supports, but rather decrease again when the optimum ionomer content is exceeded. The proposed model constitutes a fast translation from manufacturing parameters to catalyst layer morphology which can be incorporated into existing performance and degradation models in a straightforward way.

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To the first paper

To the second paper

To the third paper

In-Situ Characterization of Cathode Catalyst Degradation in PEM Fuel Cells

This fourth talk in the series provided insights into our characterization activities, specifically characterization of catalyst degradation.

The composition and morphology of the cathode catalyst layer (CCL) have a significant impact on the performance and stability of polymer electrolyte membrane fuel cells (PEMFC). Understanding the primary degradation mechanism of the CCL and its influencing factors is crucial for optimizing PEMFC performance and durability. Within this work, we present comprehensive in-situ characterization data focused on cathode catalyst degradation. The dataset consists of 36 unique durability tests with over 4000 testing hours, including variations in the cathode ionomer to carbon ratio, platinum on carbon ratio, ionomer equivalent weight, and carbon support type. The applied accelerated stress tests were conducted with different upper potential limits and relative humidities. Characterization techniques including IV-curves, limiting current measurements, electrochemical impedance spectroscopy, and cyclic voltammetry were employed to analyse changes in performance, charge and mass transfer, and electrochemically active surface area of the catalyst. The aim of the dataset is to improve the understanding of catalyst degradation by allowing comparisons across material variations and provide practical information for other researchers in the field.

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To the first paper

To the second paper

Understanding Contamination in the Hydrogen Feed

This third talk in the series provided insights into our characterization activities, specifically characterization of fuel contaminants.

Platinum on graphitized low surface area carbon (Pt/C) is coated with a silicon oxide thin film and is employed as anode catalyst to manipulate the tolerance of proton exchange membrane fuel cells toward carbon monoxide and hydrogen sulfide contamination. The SiO2 coating, prepared by successive hydrolysis of 3-aminopropyl-triethoxisilane and tetraethoxysilane, forms clusters in proximity to Pt in sizes comparable to the catalyst particles, leaving most of the carbon surfaces free. The performance with and without CO is investigated in situ at relative humidities (RH) of 100%, 70%, and 40%. When operated with neat hydrogen, SiO2-Pt/C shows marginally better performance owing to an improved protonic conduction due to the water retaining hydrophilic SiO2. Upon operation with CO-contaminated fuel, the SiO2-Pt/C performs worse than that of Pt/C particularly at high RH. CO stripping measurements reveal an increase in CO oxidation potential for the SiO2-Pt/C, suggesting an increased CO coverage and consequently higher anode overpotentials during operation with CO-contaminated fuel. Upon operation with H2S in the fuel, the SiO2 coating extends the lifetime until the cell voltage broke down, which is attributed to the enhanced water retention due to SiO2 and the solubility of sulfuric species.

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To the paper: CV Analysis of Low Loaded Electrodes

To the paper: CO and H2S-Tolerance of Low Loaded Anode Electrodes

To the paper: Effect of SiO2-Coating versus CO and H2S Contamination

Screen Printing of Catalyst Layers for PEM Fuel Cells

This second talk in the series provided insights into our production activities, specifically screen printing.

Current state-of-the-art coating techniques for PEM fuel cell electrode manufacturing such as slot-die coating use closed ink reservoirs, allowing low boiling point solvents as the dispersion matrix for solid components of the catalyst ink. Applying such low boiling point inks to printing methods that expose catalyst inks to air, like flatbed screen printing, results in an instable and nonscalable production process due to the successive evaporation of these solvents. Within this study, a total of 12 different solvents are examined for process stability and electrochemical performance when applied with flatbed screen printing. Ink characteristics, such as contact angle, rheology, and sedimentation experiments, are quantified to reveal the most suitable set of solvents, enabling the use of open-reservoir printing methods like flatbed screen printing. Additionally, electrochemical in situ characterization of catalyst-coated membranes showed that 1,2-propanediol and 1-heptanol are solvents that combine process stability with high fuel cell performance.

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To the main paper: Optimizing the Catalyst Ink

To the paper: Challenges of Screen Printing

To the paper: Upscaling to Short Stack

Optimizing Catalyst Layer Composition of PEM Fuel Cell via Machine Learning

The inaugural talk in the series provided insights into our activities in machine learning.

The catalyst layer (CL) is a pivotal component of Proton Exchange Membrane (PEM) fuel cells, exerting a significant impact on both performance and durability. Its ink composition can be succinctly characterized by platinum (Pt) loading, Pt/carbon ratio, and ionomer/carbon ratio. The amount of each substance within the CL must be meticulously balanced to achieve optimal operation. In this work, we apply an Artificial Neural Network (ANN) model to forecast the performance and durability of a PEM fuel cell based on its cathode CL composition. The model is trained and validated based on experimental data measured at our laboratories, which consist of data from 49 fuel cells, detailing their cathode CL composition, operating conditions, accelerated stress test conditions, polarization curves and ECSA measurements throughout their lifespan. The presented ANN model demonstrates exceptional reliability in predicting PEM fuel cell behavior for both beginning and end of life. This allows for a deeper understanding of the influence of each input on performance and durability. Furthermore, the model can be effectively applied to optimize the CL composition. This paper demonstrates the immense potential of AI, combined with a high-quality database, to advance fuel cell research.

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To the paper

More information

Business Area

Hydrogen Technologies

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Research Topic

Fuel Cell

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Webinar

Webinar / May 20, 2026

Fuel Cell Scientific Insights Series

Upcoming Webinar on May 20!

Event details

Location

Organization

Upcoming Webinars

PEMFC Performance Modeling

Dr. Dietmar Gerteisen

Innovative Ionomer Materials

Linus Hirth

Past Webinars

Technoeconomic Analysis of CCM Production

Jakob Hog

Imaging of Catalyst Layers

Patrick Schneider

Next Generation Catalysts

Kläre Christmann

Modeling the Morphology of Porous Carbon Supports of PEMFC

Anne-Christine Scherzer

In-Situ Characterization of Cathode Catalyst Degradation in PEM Fuel Cells

Patrick Schneider

Understanding Contamination in the Hydrogen Feed

Dr. Sebastian Prass

Screen Printing of Catalyst Layers for PEM Fuel Cells

Linda Ney

Optimizing Catalyst Layer Composition of PEM Fuel Cell via Machine Learning

Dr. Yuze Hou

More information

Hydrogen Technologies

Fuel Cell