Operando Scanning Small-/Wide-Angle X-ray Scattering for Polymer Electrolyte Fuel Cells: Investigation of Catalyst Layer Saturation and Membrane Hydration- Capabilities and Challenges.

Polymer electrolyte fuel cells are an essential technology for future local emission-free mobility. One of the critical challenges for thriving commercialization is water management in the cells. We propose small- and wide-angle X-ray scattering as a suitable diagnostic tool to quantify the liquid saturation in the catalyst layer and determine the hydration of the ion-conducting membrane in real operating conditions. The challenges that may occur in operando data collection are described in detail─separation of the anode and cathode, cell alignment to the beam, X-ray radiation damage, and the possibility of membrane swelling. A synergistic development of experimental setup, data acquisition, and data interpretation circumvents the major challenges and leads to practical and reliable insights.

On the water transport mechanism through the microporous layers of operando polymer electrolyte fuel cells probed directly by X-ray tomographic microscopy.

Product water transport via the microporous layer (MPL) and gas diffusion layer (GDL) substrate during polymer electrolyte fuel cell (PEFC) operation was directly and quantitatively observed by X-ray tomographic microscopy (XTM). The liquid water distribution in two types of MPLs with different pore size distributions (PSDs) was characterized as a function of the inlet gas relative humidity (RH) and current density under humid operating conditions at 45 °C. During the first minute of PEFC operation, liquid water mainly accumulated at the catalyst layer (CL)/MPL interface and in the GDL substrate close to the flow fields. Furthermore, under all tested conditions, saturation in the MPL was low (<25%), whereas under the rib, the saturation in the GDL was up to ca. 70%. Based on these XTM results, it is confirmed that in the high porosity MPLs, vapor transport was non-negligible even at high humidity conditions. Therefore, on top of the widely discussed MPL pore size and its distribution, it is proposed that the lower thermal conductivity from the high porosity of MPLs can also be a main cause of promoted vapor transport, reducing water saturation near the CL.

Quantification of PEFC Catalyst Layer Saturation via In Silico, Ex Situ, and In Situ Small-Angle X-ray Scattering.

The complex nature of liquid water saturation of polymer electrolyte fuel cell (PEFC) catalyst layers (CLs) greatly affects the device performance. To investigate this problem, we present a method to quantify the presence of liquid water in a PEFC CL using small-angle X-ray scattering (SAXS). This method leverages the differences in electron densities between the solid catalyst matrix and the liquid water filled pores of the CL under both dry and wet conditions. This approach is validated using ex situ wetting experiments, which aid the study of the transient saturation of a CL in a flow cell configuration in situ. The azimuthally integrated scattering data are fitted using 3D morphology models of the CL under dry conditions. Different wetting scenarios are realized in silico, and the corresponding SAXS data are numerically simulated by a direct 3D Fourier transformation. The simulated SAXS profiles of the different wetting scenarios are used to interpret the measured SAXS data which allows the derivation of the most probable wetting mechanism within a flow cell electrode.

Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy.

Time-resolved X-ray tomographic microscopy is an invaluable technique to investigate dynamic processes in 3D for extended time periods. Because of the limited signal-to-noise ratio caused by the short exposure times and sparse angular sampling frequency, obtaining quantitative information through post-processing remains challenging and requires intensive manual labor. This severely limits the accessible experimental parameter space and so, prevents fully exploiting the capabilities of the dedicated time-resolved X-ray tomographic stations. Though automatic approaches, often exploiting iterative reconstruction methods, are currently being developed, the required computational costs typically remain high. Here, we propose a highly efficient reconstruction and classification pipeline (SIRT-FBP-MS-D-DIFF) that combines an algebraic filter approximation and machine learning to significantly reduce the computational time. The dynamic features are reconstructed by standard filtered back-projection with an algebraic filter to approximate iterative reconstruction quality in a computationally efficient manner. The raw reconstructions are post-processed with a trained convolutional neural network to extract the dynamic features from the low signal-to-noise ratio reconstructions in a fully automatic manner. The capabilities of the proposed pipeline are demonstrated on three different dynamic fuel cell datasets, one exploited for training and two for testing without network retraining. The proposed approach enables automatic processing of several hundreds of datasets in a single day on a single GPU node readily available at most institutions, so extending the possibilities in future dynamic X-ray tomographic investigations.

Operando Liquid Pressure Determination in Polymer Electrolyte Fuel Cells.

Extending the operating range of fuel cells to higher current densities is limited by the ability of the cell to remove the water produced by the electrochemical reaction, avoiding flooding of the gas diffusion layers. It is therefore of great interest to understand the complex and dynamic mechanisms of water cluster formation in an operando fuel cell setting as this can elucidate necessary changes to the gas diffusion layer properties with the goal of minimizing the number, size, and instability of the water clusters formed. In this study, we investigate the cluster formation process using X-ray tomographic microscopy at 1 Hz frequency combined with interfacial curvature analysis and volume-of-fluid simulations to assess the pressure evolution in the water phase. This made it possible to observe the increase in capillary pressure when the advancing water front had to overcome a throat between two neighboring pores and the nuanced interactions of volume and pressure evolution during the droplet formation and its feeding path instability. A 2 kPa higher breakthrough pressure compared to static ex situ capillary pressure versus saturation evaluations was observed, which suggests a rethinking of the dynamic liquid water invasion process in polymer electrolyte fuel cell gas diffusion layers.

A Method for Spatial Quantification of Water in Microporous Layers of Polymer Electrolyte Fuel Cells by X-ray Tomographic Microscopy.

A microporous layer (MPL) is typically added to the gas diffusion layer of polymer electrolyte fuel cells (PEFCs) to promote cell performance and water management. The transport mechanism of the water through the MPL is, however, not well understood due to its small pores (20-500 nm). Here, we demonstrate that polychromatic X-ray tomographic microscopy (XTM) can be used to determine the porosity and the spatial distribution of water in nanoporous materials and can quantitatively map the liquid water saturation of MPLs. The presented technique requires no a priori knowledge of the composition of the MPL and has a field of view on the millimeter scale, which is orders of magnitude larger than conventional electron microscopy techniques for nanoscale materials. The available field of view is compatible with existing operando cells for X-ray tomography, paving the way for the analysis of water transport in MPLs during operation.

Unveiling water dynamics in fuel cells from time-resolved tomographic microscopy data.

X-ray dynamic tomographic microscopy offers new opportunities in the volumetric investigation of dynamic processes. Due to data complexity and their sheer amount, extraction of comprehensive quantitative information remains challenging due to the intensive manual interaction required. Particularly for dynamic investigations, these intensive manual requirements significantly extend the total data post-processing time, limiting possible dynamic analysis realistically to a few samples and time steps, hindering full exploitation of the new capabilities offered at dedicated time-resolved X-ray tomographic stations. In this paper, a fully automatized iterative tomographic reconstruction pipeline (rSIRT-PWC-DIFF) designed to reconstruct and segment dynamic processes within a static matrix is presented. The proposed algorithm includes automatic dynamic feature separation through difference sinograms, a virtual sinogram step for interior tomography datasets, time-regularization extended to small sub-regions for increased robustness and an automatic stopping criterion. We demonstrate the advantages of our approach on dynamic fuel cell data, for which the current data post-processing pipeline heavily relies on manual labor. The proposed approach reduces the post-processing time by at least a factor of 4 on limited computational resources. Full independence from manual interaction additionally allows straightforward up-scaling to efficiently process larger data, extensively boosting the possibilities in future dynamic X-ray tomographic investigations.

Coating Distribution Analysis on Gas Diffusion Layers for Polymer Electrolyte Fuel Cells by Neutron and X-ray High-Resolution Tomography.

Coating load and distribution in gas diffusion layers (GDLs) for polymer electrolyte fuel cells (PEFCs) have a major influence on mass transport losses. To be able to optimize the coating distribution and get more accurate data about the influence of the coating on the PEFC performance, better characterization techniques are necessary. Common analysis techniques are limited to selected sections of the material, or they are not sensitive to small amounts of coating. We propose a new methodology to get a complete description of the coating distribution and the GDL structure by combining high-resolution X-ray tomography with high-resolution neutron tomography. Using an isotopic gadolinium staining method to enhance the neutron and X-ray absorption contrast, lower quantities of coating can be detected. The combination of both imaging techniques allows for a more detailed analysis of the coating distribution.

High-numerical-aperture macroscope optics for time-resolved experiments.

A novel high-quality custom-made macroscope optics, dedicated to high-resolution time-resolved X-ray tomographic microscopy at the TOMCAT beamline at the Swiss Light Source (Paul Scherrer Institut, Switzerland), is introduced. The macroscope offers 4× magnification, has a very high numerical aperture of 0.35 and it is modular and highly flexible. It can be mounted both in a horizontal and vertical configuration, enabling imaging of tall samples close to the scintillator, to avoid edge-enhancement artefacts. The macroscope performance was characterized and compared with two existing in-house imaging setups, one dedicated to high spatial and one to high temporal resolution. The novel macroscope shows superior performance for both imaging settings compared with the previous systems. For the time-resolved setup, the macroscope is 4 times more efficient than the previous system and, at the same time, the spatial resolution is also increased by a factor of 6. For the high-spatial-resolution setup, the macroscope is up to 8.5 times more efficient with a moderate spatial resolution improvement (factor of 1.5). This high efficiency, increased spatial resolution and very high image quality offered by the novel macroscope optics will make 10-20 Hz high-resolution tomographic studies routinely possible, unlocking unprecedented possibilities for the tomographic investigations of dynamic processes and radiation-sensitive samples.

Elucidating the Nuanced Effects of Thermal Pretreatment on Carbon Paper Electrodes for Vanadium Redox Flow Batteries.

Sluggish vanadium reaction rates on the porous carbon electrodes typically used in redox flow batteries have prompted research into pretreatment strategies, most notably thermal oxidation, to improve performance. While effective, these approaches have nuanced and complex effects on electrode characteristics hampering the development of explicit structure-function relations that enable quantitative correlation between specific properties and overall electrochemical performance. Here, we seek to resolve these relationships through rigorous analysis of thermally pretreated SGL 29AA carbon paper electrodes using a suite of electrochemical, microscopic, and spectroscopic techniques and culminating in full cell testing. We systematically vary pretreatment temperature, from 400 to 500 °C, while holding pretreatment time constant at 30 h, and evaluate changes in the physical, chemical, and electrochemical properties of the electrodes. We find that several different parameters contribute to observed performance, including hydrophilicity, microstructure, electrochemical surface area, and surface chemistry, and it is important to note that not all of these properties improve with increasing pretreatment temperature. Consequently, while the best overall performance is achieved with a 475 °C pretreatment, this enhancement is achieved from a balance, rather than a maximization, of critical properties. A deeper understanding of the role each property plays in battery performance is the first step toward developing targeted pretreatment strategies that may enable transformative performance improvements.

Direct observation of active material interactions in flowable electrodes using X-ray tomography.

Understanding electrical percolation and charging mechanisms in electrochemically active biphasic flowable electrodes is critical for enabling scalable deionization (desalination) and energy storage. Flowable electrodes are dynamic material systems which store charge (remove ions) and have the ability to flow. This flow process can induce structural changes in the underlying material arrangement and result in transient and non-uniform material properties. Carbon-based suspensions are opaque, multi-phase, and three dimensional, and thus prior characterization of the structural properties has been limited to indirect methods (electrochemical and rheology). Herein, a range of mixed electronic and ionically conducting suspensions are evaluated to determine their static structure, function, and properties, utilizing synchrotron radiation X-ray tomographic microscopy (SRXTM). The high brilliance of the synchrotron light enables deconvolution of the liquid and solid phases. Reconstruction of the solid phase reveals agglomeration cluster volumes between 10 µm3 and 103µm3 (1 pL) for low loaded samples (5 wt% carbon). The largest agglomeration cluster in the low loaded sample (5 wt%) occupied only 3% of the reconstructed volume whereas samples loaded with 10 wt% activated carbon demonstrated electrically connected clusters that occupied 22% of the imaged region. The highly loaded samples (20 wt%) demonstrated clusters of the order of a microliter, which accounted for 63-85% of the imaged region. These results demonstrate a capability for discerning the structural properties of biphasic systems utilizing SRXTM techniques, and show that discontinuity in the carbon particle networks induces decreased material utilization in low-loaded flowable electrodes.

Quantifying microstructural dynamics and electrochemical activity of graphite and silicon-graphite lithium ion battery anodes.

Despite numerous studies presenting advances in tomographic imaging and analysis of lithium ion batteries, graphite-based anodes have received little attention. Weak X-ray attenuation of graphite and, as a result, poor contrast between graphite and the other carbon-based components in an electrode pore space renders data analysis challenging. Here we demonstrate operando tomography of weakly attenuating electrodes during electrochemical (de)lithiation. We use propagation-based phase contrast tomography to facilitate the differentiation between weakly attenuating materials and apply digital volume correlation to capture the dynamics of the electrodes during operation. After validating that we can quantify the local electrochemical activity and microstructural changes throughout graphite electrodes, we apply our technique to graphite-silicon composite electrodes. We show that microstructural changes that occur during (de)lithiation of a pure graphite electrode are of the same order of magnitude as spatial inhomogeneities within it, while strain in composite electrodes is locally pronounced and introduces significant microstructural changes.

Combining operando synchrotron X-ray tomographic microscopy and scanning X-ray diffraction to study lithium ion batteries.

We present an operando study of a lithium ion battery combining scanning X-ray diffraction (SXRD) and synchrotron radiation X-ray tomographic microscopy (SRXTM) simultaneously for the first time. This combination of techniques facilitates the investigation of dynamic processes in lithium ion batteries containing amorphous and/or weakly attenuating active materials. While amorphous materials pose a challenge for diffraction techniques, weakly attenuating material systems pose a challenge for attenuation-contrast tomography. Furthermore, combining SXRD and SRXTM can be used to correlate processes occurring at the atomic level in the crystal lattices of the active materials with those at the scale of electrode microstructure. To demonstrate the benefits of this approach, we investigate a silicon powder electrode in lithium metal half-cell configuration. Combining SXRD and SRXTM, we are able to (i) quantify the dissolution of the metallic lithium electrode and the expansion of the silicon electrode, (ii) better understand the formation of the Li15Si4 phase, and (iii) non-invasively probe kinetic limitations within the silicon electrode. A simple model based on the 1D diffusion equation allows us to qualitatively understand the observed kinetics and demonstrates why high-capacity electrodes are more prone to inhomogeneous lithiation reactions.

Polymer electrolyte fuel cell performance degradation at different synchrotron beam intensities.

The degradation of cell performance of polymer electrolyte fuel cells under monochromatic X-ray irradiation at 13.5 keV was studied in galvanostatic and potentiostatic operation modes in a through-plane imaging direction over a range of two orders of magnitude beam intensity at the TOMCAT beamline of the Swiss Light Source. The performance degradation was found to be a function of X-ray dose and independent of beam intensity, whereas the degradation rate correlates with beam intensity. The cell performance was more sensitive to X-ray irradiation at higher temperature and gas feed humidity. High-frequency resistance measurements and the analysis of product water allow conclusions to be drawn on the dominating degradation processes, namely change of hydrophobicity of the electrode and sulfate contamination of the electrocatalyst.