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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2018

Dynamic Power Convertor Development for Radioisotope Power Systems at NASA Glenn Research Center

The Thermal Energy Conversion Branch at NASA Glenn Research Center is supporting the development of high-efficiency power convertors for use in radioisotope power systems (RPS). Significant progress was made toward such a system that utilized Stirling conversion during the 2001 to 2015 timeframe. Flight development of the Advanced Stirling Radioisotope Generator (ASRG) was canceled in 2013 by the U.S. Department of Energy (DOE) and NASA Headquarters primarily due to budget constraints, and the Advanced Stirling Convertor (ASC) technology contract was subsequently concluded in 2015. A new chapter of technology development has recently been initiated by the NASA RPS Program. This effort is considering all dynamic power convertor options, such as Stirling and Brayton cycles. Four convertor development contracts supporting this effort were awarded in 2017. The awarded contracts include two free-piston Stirling, one thermoacoustic Stirling, and one turbo-Brayton design. The technology development contracts each consist of up to three phases: design, fabrication, and test. As of May 2018, all contracts have completed the design phase, and each underwent a design review with an independent review board. Three of the contracts are planned to execute the phase 2 option for fabrication. Convertors manifesting from these development efforts will then undergo independent validation and verification (IV and V) at NASA facilities, which will consist of convertor performance and RPS viability demonstrations. Example tests include launch vibration simulation, performance mapping over the environmental temperature range, and static acceleration exposure. In parallel with this renewed development effort, Glenn is still demonstrating free-piston Stirling convertor technology using assets from previous projects. The Stirling Research Laboratory (SRL) is still operating several convertors from previous development projects, which have similarities and relevance to current contract designs. Four of these are flexure-bearing based and another six are gas bearing based. One of the flexure-bearing convertors has accumulated over 110,000 h of operation and holds the current record for maintenance-free heat-engine run time. Another flexure-bearing convertor was recently manually shut down after 105,620 h of operation, then disassembled and inspected. This inspection produced a wealth of information about the effects of this amount of run time on the technology's components. One of the engineering unit flexure-bearing convertors recently underwent a launch simulation vibration test, a static acceleration exposure up to 20 g, and was then placed on extended operation. Among the gas-bearing convertors, the longest running unit has accumulated over 70,000 h of operation. Four high-fidelity gas-bearing convertors from the ASRG project are still operating continuously for which the longest run time has reached 28,000 h.

Oriti, Salvatore M.↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
NASA NTRS2020

Reynolds-Averaged Navier-Stokes Computations of the NASA Juncture Flow Model Using FUN3D and OVERFLOW

Two Reynolds-averaged Navier-Stokes codes, FUN3D and OVERFLOW, are used to assess the capability of Spalart-Allmaras-based turbulence models to predict the flow over the NASA Juncture Flow model. Both free-air and in-tunnel simulations are performed. While the tunnel walls have some influence, it is found to be relatively minor in the juncture region of interest. Results from the two codes are found to be consistent with each other in attached flow regions, but results in the area of separation still show grid and code sensitivity, even on grids as large as 400 million unknowns. Nonetheless, it is possible to draw conclusions regarding the model capabilities. Without a quadratic constitutive relation, the linear model predicts a separation size that is too large. The inclusion of a nonlinear quadratic constitutive relation improves results significantly, but separation still occurs too far upstream. Prediction of turbulent normal stress differences play a key role in this flow. Although the nonlinear model makes better predictions in this regard, they could still be improved, particularly in the streamwise normal component near the wall.

C L Rumsey↗
NASA NTRS2020

Local Food Crop Production can Fulfil Demand for Less than One-Third of the Population

The distance between the origin and end-point of food supply chains, and the ‘localness’ of food systems, are key considerations of many narratives associated with sustainability. Yet, information on the minimum distance to food crops is still scarce at the global level. Using an optimization model based on ‘foodsheds’ (that is, self-sufficient areas with internal dependencies), we calculate the potential minimum distance between food production and consumption for six crop types around the world. We show that only 11–28% of the global population can fulfil their demand for specific crops within a 100-km radius, with substantial variation between different regions and crops. For 26–64% of the population, that distance is greater than 1,000 km. Even if transnational foodsheds were in place, large parts of the globe would still depend on trade to feed themselves. Although yield gap closure and food loss reductions could favour more local food systems, particularly in Africa and Asia, global supply chains would still be needed to ensure an adequate and stable food supply.

Crop production↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry Descent and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry, Descent, and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗
NASA NTRSDate not supplied

Artificial Intelligence Enhancements to Imagery for Space Operations

Philosophy classes still ponder the question asked by Dr. George Berkely, an Anglican Bishop and philosopher in the 1600’s-- “If a tree falls in a forest and no one is around to hear it, does it make a sound?” With that in mind, I ask the following—If a still image or motion imagery from a space mission cannot be found during a search, does it exist? Since the beginning of spaceflight, imagery has been a key form of data collected. Whether for mere curiosity (what does Earth look like from Space?), or for operational reasons (did the solar panel deploy?), or for engineering purposes (what was that object that floated away from the spacecraft?), imagery has been included in space missions. To be useful, though, the image or motion imagery must be accessible and accessed when needed. During the analog era, that typically meant captions and numbers associated with the physical media. With “born digital” imagery, it is possible to add metadata to the image data file. This metadata might include the date and time of capture, mission, camera, exposure data, and similar data fields. Many modern cameras embed some basic metadata into the image file at the moment of capture. The reality, though, is even with today’s born-digital enhancements with embedded metadata at the time of capture, reviewing and cataloging still and motion imagery is very labor intensive. Humans review the imagery for sensitive content (privacy concerns, imagery containing proprietary data/subject matter), and to identify imagery containing crew members or imagery that should be reviewed for engineering or scientific reasons. All this review and manual data entry is very time-consuming. Many improvements in Artificial Intelligence (AI), Machine Learning, and processing power now make it possible to identify persons, objects, motion, color, audio with sensitive content, and other details after or while the imagery is captured.

Rodney Grubbs↗
NASA NTRSDate not supplied

Coupling of Dust Mineralogy to Radiation in NASA GISS ModelE: Impact Upon Dust Optical Properties and Direct Radiative Effect

Estimates of dust direct radiative effect (DRE) are still largely uncertain mainly due to the poor constraining of regional variations of dust absorption, whose known connection with mineral composition is still largely neglected by most Earth System Models. The inaccurate knowledge of soil mineralogy and insufficient measurements of dust physical and optical properties still present the main challenges for a proper representation of the dust-radiation interaction in climate models. We recently upgraded the coupling of dust minerals to shortwave radiation in the NASA GISS ModelE. In particular, we extended the interaction to online-calculated minerals and implemented two different mixing configurations: i) external mixing of three main mineral components and ii) internal mixing of all minerals, allowing a dynamic fraction of iron oxides. We used a semi-empirical approach for the optical calculations, mostly based on aerosol chamber measurements from natural soils, aiming to reduce the influence of uncertain assumptions on the optical properties and mixing state of individual minerals. In this contribution, we present the results of our first calculations of dust optical properties and DREs based on different mixing configurations of interactive minerals. We show the changes to dust optical depth and single scattering albedo, with respect to the control case using homogeneous dust composition, and the consequent global and regional variations in dust DREs at the top of atmosphere and at surface. We also compare the model optical properties with AERONET data, filtered to identify dust scenes. We find that accounting for the diverse properties of dust minerals modifies the pattern of regional dust absorption and therefore its impact upon climate. We also show that the mineralogy effect is sensitive to the assumed mixing state of minerals.

dust direct radiative effect↗
NASA NTRSDate not supplied

Preliminary Results Cycling GEOS-JEDI with GSI-based Background Errors

The first phase of transitioning the NASA GMAO GEOS atmospheric data assimilation capabilities to JEDI involves the replacement of the Grid-point Statistical Interpolation (GSI) with a corresponding JEDI analysis. This includes taking JEDI's Unified Observation Operator (UFO), its underlying dependencies, and the JEDI solver that enables a hybrid 4DEnVar strategy similar to what is used in the current GEOS-GSI system. Variational analysis involves at least two main components associated with the observation and background cost function terms. The first is directly related to the UFO, which is being carefully validated in a joint collaboration between GMAO and NCEP to demonstrate consistency with corresponding observations usage in GSI. The second component is the background term, which in a hybrid system involves the ability to set up both a climatologically-based term and an ensemble-based term. JEDI provides the means to implement both terms through its BUMP component. Use of BUMP would require a complete re-tune of both climatological and ensemble, which is a non-trivial exercise we would prefer to avoid. As an alternative, the work here studies the results of interfacing the GSI-background error capability (GSIBEC) into JEDI through SABER. With this, the exact same background error covariance formulation used in GSI can be employed in JEDI without need for re-tuning. This brief summary covers the work done to interface GSIBEC into JEDI and shows preliminary results where the background error covariances of the control (GEOS-GSI) and experiment (GEOS-JEDI) are identical in corresponding cycling experiments. The cycling exercise is obviously preliminary and so much can be expected from GEOS-JEDI when compared to GEOS-GSI. There is still a number of features that need closer attention and although in some cases in principle ready to cycle have been intentionally either turned off or not fully exercised (e.g., VarBC is applied but not cycled). Other features are still pending implementation, one such example is the implementation of the Tangent Linear Normal Mode Constraint. Still, results are quite encouraging as hopefully the discussion here illustrates.

Ricardo Todling↗
DOE OSTI2026

Developing a Digital Twin for SRF Cavity Assembly at Fermilab

When assembling Superconducting Radio Frequency (SRF) Cavities, maintaining an environment devoid of particulates like dust and other small particles is essential. If a single spec of dust enters the cavity a significant degradation of performance can occur. To avoid a cavity failure Fermilab assembles the SRF cavities within a ISO-4 (Class 10) environment. This environment though is still susceptible to foreign contaminants when technicians enter and new components are added to the cleanroom. To reduce the risk even more Fermilab has introduced a cobot manipulator into the cleanroom environment to speed up the assembly time which will reduce the time that the technicians operate in the cleanroom. But, this still leaves the potential of contaminants to enter the cleanroom if new components need to be tested within the cleanroom. This project aims to lay the groundwork to develop a Digital Twin environment of the cleanroom to aid in manufacturing processes and testing. In its simplest form, a digital twin is a bidirectional link between a physical system and its digital counterpart or twin. NVIDIA Isaac Sim is used as the digital twin foundation for the digital representation of the cleanroom, specifically for the UR16e assembly area. A simulated UR16e was used to validate the performance of Isaac Sim as a testing environment by comparing the tool center points (TCP) positional data between the simulated and digital representation of the UR16e. Due to a new vision based robotic assembly process being introduced to the cleanroom a digital representation of the physical camera was tested and validated to ensure that it will produce close to the same outcome as the physical environment. The TCP comparison results showed a peak translational error of approximately 0.1mm and rotational errors of up to 8 between the simulated and digital UR16es. While the camera validation performed with high repeatability across multiple runs, it still requires minor tuning before it can accurately replicate a physical camera.

Imburgia, Joseph [Northern Illinois U.]↗
DOE OSTI2022

Development of Cr, Se, U, Sb, and Te isotopes as indicators of redox reactions, contaminant fate, and contaminant transport in aqueous systems: A review

Cr, Se, U, Sb, and Te are toxic, redox-active elements that are more mobile and environmentally problematic in their oxidized forms, and less mobile and bioavailable in their reduced forms. This chapter reviews the development of Cr, Se, U, Sb, and Te isotope ratio measurements as new indicators of redox reactions and contaminant migration. Reliable analytical methods exist, but are still evolving. Understanding of isotopic fractionation induced by various (bio)geochemical processes has been explored in dozens of publications, yet is far from complete: Reduction reactions, the major driver of isotopic variation, have been relatively well studied. However, the magnitude of fractionation is variable and the systematics of that variation are still being explored. Isotopic fractionation induced by oxidation reactions is not well understood. Non-redox reactions, which involve smaller changes in bonding of these elements, tend to induce less isotopic fractionation, but can nonetheless cause significant isotopic shifts. Field applications of Cr, Se, U isotope ratios have demonstrated that they are useful as indicators of reduction in natural systems. A few studies suggest they are also useful as indicators of oxidation and contaminant sources. The physical and chemical complexity of groundwater systems hinders accurate quantitative interpretation of Cr, Se, U isotope data using simple models. Numerical models have been developed that capture the behavior of complex, coupled systems and enable the most effective extraction of information from field data sets.

58 GEOSCIENCES↗
DOE OSTI2021

Thermal Stability and Lattice Strain Evolution of High-Nb-Containing TiAl Alloy under Low-Cycle-Fatigue Loading

The micromechanical behavior and the effect of temperature on the micromechanical mechanism of high-Nb-containing TiAl alloy during low-cycle fatigue still remain uncertain. Herein, in situ and ex situ synchrotron-based high-energy X-ray (HEXRD) experiment results reveal that the gamma and omega(o) phases suffer compressive lattice strains but the lattice strain in the alpha(2) phase evolves from tensile to compressive during low-cycle fatigue at 900 degrees C. In addition, the three phases suffer compressive lattice strains during cooling to room temperature, which could result in larger compressive lattice strains in gamma and omega(o) phases and the change of the lattice strain state in the alpha(2) phase. The peak-broadening results show gamma recrystallization is dominant in the interrupted low-cycle-fatigue samples, whereas inhomogeneous deformation occurs in the failed low-cycle-fatigue samples. The performed synchrotron diffraction experiments offer a deeper insight into the phase transformations and micromechanism of TiAl alloy during low-cycle fatigue.

cyclic stress-strain behavior↗
DOE OSTI2023

Extreme γ –Ray Radiation Tolerance of Spectrometer–Grade CsPbBr 3 Perovskite Detectors

The perovskite compound CsPbBr 3 has recently been discovered as a promising room-temperature semiconductor radiation detector, offering an inexpensive and easy-to-manufacture alternative to the current benchmark material Cd 1–x Zn x Te (CZT). The performance of CsPbBr 3 sensors is evaluated under harsh conditions, such as high radiation doses often found in industrial settings and extreme radiation in space. Results show minimal degradation in detector performance after exposure to 1 Mrad of Co-60 gamma radiation, with no significant change to energy resolution or hole mobility and lifetime. Additionally, many of the devices are still functional after being exposed to a 10 Mrad dose over 3 days, and those that do not survive can still be refabricated into working detectors. Notably, these results suggest that the failure mode in these devices is likely related to the interface between the electrode and material and their reaction, or the electrode itself and not the material itself. Overall, the study suggests that CsPbBr 3 has high potential as a reliable and efficient radiation detector in various applications, including those involving extreme fluxes and energies of gamma-ray radiation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Extreme Transverse Magnetoresistance in TiZn 16

Extreme magnetoresistance (XMR) is a phenomenon characterized by an increase in resistance by factors of 10 4 –10 7 % when a magnetic field is applied. This phenomenon is found in a number of semimetals such as WTe 2 , PtSn 4 , Cd 3 As 2 , and LaSb. The origin of XMR is still hotly debated, possibly with different materials having different (or multiple) explanations. Extreme transverse magnetoresistance of up to 8000% at 14 T and 1.8 K is measured in TiZn 16 , a semimetal with a multitude of bands crossing the Fermi energy, akin to PtSn 4 . The magnetoresistance is suppressed when the magnetic field is rotated to be parallel to the applied current, similar to PtSn 4 and PdSn 4 . The resistance of TiZn 16 follows Kohler's rule, but displays different behavior under an applied transverse field and under a longitudinal magnetic field, suggesting distinct electrical phases. Also present are Shubnikov-de Haas and de Haas-van Alphen oscillations with a transverse magnetic field up to 43 T, showing that despite an insulator-like temperature-resistance curve, charge carriers are still present. This positions TiZn 16 as an interesting addition to the investigation of XMR materials as a multi-band metal with complex Fermi surface geometries.

extreme magnetoresistance↗
DOE OSTI2020

Advanced electrolytes for fast-charging high-voltage lithium-ion batteries in wide-temperature range

LiNixMnyCo1-x-yO2 (NMC) cathode materials with Ni=0.8 have attracted great interest for application in high energy-density lithium (Li)-ion batteries (LIBs) because of their high specific capacities at high voltages. However, the practical application of Ni-rich NMC in LIBs under high charge voltages (e.g. 4.4 V and above) still faces big challenges due to the severe capacity fading, which is directly related to the instability of the cathode/electrolyte interface. In this work, we develop new localized high-concentration electrolytes (LHCEs) based on carbonate solvents, which are compatible with graphite (Gr) anode and have a high oxidation potential over 4.9 V vs. Li/Li+. The optimal LHCE enables the Gr||NMC811 cells with 2.8 mAh cm-2 cathode areal capacity loading to achieve an excellent cycling stability over 600 cycles with a capacity retention of 94.2 % under a charge cut-off voltage of 4.4 V at 25 ?C, good rate capabilities under fast charging or discharging up to 3C rate, and superior low-temperature discharge performance down to -30 ?C with a capacity retention of 85.6% at C/5 rate. The findings in this work shed light a very promising strategy to develop new electrolytes for practical high-energy LIBs with Ni-rich NMC cathodes.

Zhang, Xianhui↗
DOE OSTI2021

Recent progress in understanding solid electrolyte interphase on lithium metal anode

Lithium (Li) metal batteries (LMBs) are among the most promising candidates of next-generation high-energy-density rechargeable batteries. Solid electrolyte interphase (SEI) on Li metal anode plays a significant role which influences the Li deposition morphology and the cycle life of LMBs. Although SEI is the most important part, a thorough understanding of SEI is inadequate. In this review, we focus on the progresses of understanding on structures, properties and influencing factors of SEI as well as efficient strategies of tailoring SEI. First, the compositions, models and recent progresses on characterizing atomic structure of SEI are summarized. Second, the properties of SEI, including electronic conduction, ionic conduction, stability and mechanical properties are elucidated. Structures and properties of SEI are greatly influenced by multiple factors such as solvent, salt, additive, solvation structure, impurity, current density, temperature, pressure and capacity utilization. Thus, interactions between these factors and SEI are comprehensively discussed. Correlations of SEI with Li deposition morphology, rate capability and cycle life are further summarized. Moreover, efficient strategies of tailoring SEI with desired properties, including in-situ SEI and ex-situ SEI are also reviewed. Despite the significant progresses that have been achieved in the researches of SEI, better understanding of SEI is still highly demand. Finally, future directions especially in-operando techniques, multi-modality approaches for characterization of SEI and artificial intelligence assisted understanding of correlation between electrolyte components and SEI properties are proposed.

Wu, Haiping↗
DOE OSTI2022

Direct Characterization of Type-I Band Alignment in 2D Ruddlesden–Popper Perovskites

2D Ruddlesden–Popper halide perovskites have attracted considerable attention due to their desirable optoelectronic properties, high chemical and structural tunability, and improved environmental stability. However, the understanding of their structure–properties relationships is still limited. In particular, the energy level positions and band alignments at interfaces involving these materials, which are important features to control in the context of any applications, are still under debate. Here, the electronic structure of high-purity films of BA 2 MA n-1 Pb n I 3n+1 for n = 1–5 (where BA stands for butylammonium and MA for methylammonium) is investigated, using optical absorption, ultraviolet, and inverse photoemission spectroscopies, and density functional theory calculations. Further, this study determines the ionization energy and electron affinity of each compound and demonstrates a type-I band alignment for the BA 2 MA n-1 Pb n I 3n+1 series. This study further describes the evolution of the exciton binding energy as a function of the thickness of the inorganic layers.

2D Ruddlesden–Popper perovskites↗
DOE OSTI2023

Migration and Precipitation of Platinum in Anion‐Exchange Membrane Fuel Cells

Abstract Despite the recent progress in increasing the power generation of Anion‐exchange membrane fuel cells (AEMFCs), their durability is still far lower than that of Proton exchange membrane fuel cells (PEMFCs). Using the complementary techniques of X‐ray micro‐computed tomography (CT), Scanning Electron Microscopy (SEM) and Energy Dispersive X‐ray (EDX) spectroscopy, we have identified Pt ion migration as an important factor to explain the decay in performance of AEMFCs. In alkaline media Pt +2 ions are easily formed which then either undergo dissolution into the carbon support or migrate to the membrane. In contrast to PEMFCs, where hydrogen cross over reduces the ions forming a vertical “Pt line” within the membrane, the ions in the AEM are trapped by charged groups within the membrane, leading to disintegration of the membrane and failure. Diffusion of the metal components is still observed when the Pt/C of the cathode is substituted with a FeCo−N−C catalyst, but in this case the Fe and Co ions are not trapped within the membrane, but rather migrate into the anode, thereby increasing the stability of the membrane.

Raut, Aniket↗