54 Search Results

Enhancing the catalytic stability of Fe–N–C catalysts for cathodic oxygen reduction in proton-exchange membrane fuel cells (PEMFCs) necessitates an in-depth understanding of their degradation mechanisms. Here, this study identifies key stressors affecting the stability of Fe–N–C catalysts, specifically acidic environment, oxygen (O₂), and reactive oxygen species (ROS). Through ex situ/operando experiments, we show that the oxidation of local carbon by acidic environment + O₂ + ROS, along with the demetalation of catalytic FeN_xC_y sites by O₂ or O₂ + ROS, is the primary factor responsible for the initial fast degradation of Fe–N–C catalysts. The demetalation of FeN_xC_y sites, influenced bymore » O₂, in particular by O₂ + ROS, leads to the subsequent gradual degradation of Fe–N–C. Notably, FeN₄C₁₂-type active sites are more susceptible to demetalation than FeN₄C₁₀-type sites in O₂ or O₂ + ROS. Our findings indicate that, besides constructing more stable FeN_xC_y sites, preventing local carbon oxidation and scavenging of ROS are all critical for maintaining the stability of Fe–N–C catalysts.« less

Anion exchange membrane water electrolyzers (AEMWEs) represent a promising technology for hydrogen production. The big advantage of the technology is that it allows for the use of platinum group metal-free (PGM-free) electrocatalysts at both electrodes, including catalysts for the hydrogen evolution reaction (HER) at the cathode. In addition to fulfilling the cost requirement, PGM-free HER catalysts need to meet the activity and durability targets of the AEMWEs. Here, in this work, we developed several carbon-supported, xerogel-derived nickel (Ni) HER electrocatalysts and evaluated the effect of various synthesis conditions, such as the type of carbon support, Ni-to-carbon ratio, and heat-treatment temperaturemore » and time, on their performance. Scanning transmission electron microscopy combined with energy-dispersive X-ray spectroscopy (STEM-EDS), X-ray diffraction spectroscopy (XRD), and X-ray photoelectron spectroscopy (XPS) revealed the formation of Ni nanoparticles with an oxygen-rich layer on the outside. Durability of the best-performing catalyst was assessed via a constant-current hold at 10 mA cm^–2 over 100 h. This catalyst was found to be more active and durable than the reference PGM-free material, a commercial Ni catalyst supported on a Vulcan XC-72. The catalyst was also tested in the cathode of a fully PGM-free AEMWE, allowing to reach 1.90 V (1.84 V HFR-free) at 1 A cm^–2 at 80 °C.« less

Anion-exchange-membrane water electrolyzers (AEMWE) for hydrogen production have attracted interest because cost-effective Ni- and Fe-based catalysts can be used for the oxygen evolution reaction (OER). Although NiFe oxide/hydroxide-based catalysts have been extensively studied, the role of Fe and its chemical state during OER are not well understood, with inconsistent findings across different studies. In this work, we combined in situ ⁵⁷Fe Mössbauer (MS) and X-ray absorption spectroscopy (XAS) to investigate the chemical states of Fe and Ni and elucidate their synergy during the OER. A NiFe (8 : 1 molar ratio) aerogel catalyst with high surface area, nano crystallinity, andmore » high performance in AEMWE was used. We show that both Fe and Ni are oxidized during anodic polarization, and the potential for the change of oxidation states correlates well with the onset of the OER. In situ MS shows that 80–90% of Fe³⁺ becomes tetravalent at OER potentials and remains so even after the potential is lowered below OER onset. Analysis of in situ XAS results suggests full Fe incorporation into Ni hydroxide. At OER potentials, lattice contraction indicates high oxidation states for both Ni and Fe. Upon returning to lower potentials, a portion of the Fe remains in its more oxidized form which corroborates the in situ MS findings. Results from this work affirm the importance of high-valent Ni and Fe in promoting the OER. Ni and Fe exhibit synergy during OER and the aerogel's unique nanomorphology leads to high OER activity.« less

The atomic dispersion of nickel in Ni-N-C catalysts is key for the selective generation of carbon monoxide through the electrochemical carbon dioxide reduction reaction (CO₂RR). Herein, the study reports a highly selective, atomically dispersed Ni_1.0%-N-C catalyst with reduced Ni loading compared to previous reports. Extensive materials characterization fails to detect Ni crystalline phases, reveals the highest concentration of atomically dispersed Ni metal, and confirms the presence of the proposed Ni-N_x active site at this reduced loading. The catalyst shows excellent activity and selectivity toward CO generation, with a faradaic efficiency for CO generation (FE_CO) of 97% and partial current densitymore » for CO (j_co) of -9.0 mA cm^-2 at -0.9 V in an electrochemical H-type cell. CO₂RR activity and selectivity are also studied by rotating disk electrode (RDE) measurements where transport limitations can be suppressed. It is expected that the utility of these Ni-N-C catalysts will lie with tandem CO₂RR reaction schemes to multi-carbon (C₂₊) products.« less

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Further, our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstratemore » that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.« less

Materials degradation is a major factor that limits the wider adoption of renewable and clean energy technologies. This is particularly true for the Pt group metal-free (PGM-free) atomically dispersed metal-nitrogen-carbon (M-N-C) catalysts. Here, while many experimental studies have investigated and reported the phenomenological aspects of M-N-C degradation, only a few modeling studies have considered degradation mechanisms at the atomic level. Understanding the mechanisms responsible for activity loss occurring in atomically dispersed M-N-C’s is crucial towards rationally designing active, durable, and less expensive Earth-abundant catalysts. Towards this end, we have surveyed recent literature concerning the modeling of corrosion mechanisms that impactmore » M-N-C catalysts (Fe–N–C, in particular) and offer our own perspectives on the future direction of this field.« less

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. Here, we review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed formore » the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.« less

Anion exchange membrane water electrolyzers (AEMWEs) can generate hydrogen with a pure water feed using noble metal-free catalysts. Here, the development of highly active and stable catalysts for oxygen evolution reaction (OER) is required for improving performance of AEMWEs systems. Ni-Fe (oxy)hydroxides show high OER catalytic activity in alkaline media, but typically have low surface area. In this work, we investigate a series of Ni-Fe oxides with high surface area and disordered morphology, obtained using an aerogel synthesis method. We evaluate the impact of different synthesis variables on the OER activity and demonstrate that heat treatment at high temperatures generatesmore » more ordered structure, resulting in a decrease in OER activity. Advanced characterization reveals that maintaining highly disordered and porous structure of the aerogel is essential to achieving high OER activity, as it enables the formation of highly OER-active lamellar structures of the catalyst.« less

The durability of platinum group metal-free (PGM-free) electrocatalysts is a major barrier to their usage in polymer electrolyte fuel cell cathodes. C and N removal from active sites may play an important role in the catalyst’s ability to maintain high activity. While C degradation mechanisms are kinetically controlled, previous studies have focused on thermodynamic descriptors. In this work, we develop a temperature-dependent kinetic descriptor of C and N stability using an electron beam-damage model. Our approach considers the electron beam energy threshold (EBET) describing the knock-on displacement of C and N atoms as a stability descriptor for atomic structures. Themore » stability of different sites is calculated to be different showing this approach can discriminate between similar sites with varied configurations. Additionally, we provide important insight regarding TEM beam damage of proposed active sites. We calculate 60 keV electrons can damage some proposed active site structures even at room temperature.« less