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Ferrandon, Magali

Publications and source records attributed to Ferrandon, Magali.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Single-Atom Manganese-Based Catalysts for the Oxidative Dehydrogenation of Propane

Combinatorial screening of 150 supported metal oxide (manganese and additives) catalysts was carried out via a high-throughput synthesis platform and parallel reactors for the oxidative dehydrogenation (ODH) of propane to propylene. Specifically, an organomanganese (0.05-2.5 Mn atoms/nm 2 ) complex was grafted on metal oxide supports (Al 2 O 3 , SiO 2 , TiO 2 , and ZrO 2 ) premodified with either Lewis acid (Al, Ti, Zn, and Zr) or redox-active (Cu, Cr, Ga Ni, V) additives at various surface coverages (25, 50, and 75%). Catalysts were characterized by high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), Raman spectroscopy, and UV-vis spectroscopy. Catalysts 0.05 Mn/V(50%)/Al 2 O 3 and 0.05 Mn/Ni(50%)/ZrO 2 showed the highest combined propane conversion and propylene selectivities (31/41% and 15/85%), with excellent stability at 500 degrees C for 25 h. The presence of Ni in Mn/Ni/ZrO 2 resulted in a 6-fold increase in turnover frequency (TOF) over the Mn/ZrO 2 . HRTEM identified single Mn atoms after 500 degrees C heat treatment. For the Mn/Ni/ZrO 2 system, Mn was incorporated into the support lattice due to the similar ionic radius of Mn 2+ and Zr 4+ , which was also enhanced by the presence of Ni. For the Mn/V/Al 2 O 3 system, highly active MnO was prevalent as observed by Raman. Both V and Mn contributed to an increase in mutual dispersion, but both species remained on the surface. Finally, it is proposed that the highly dispersed atom and interactions between Mn with either Ni or V are responsible for the ODH performance and stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Iron redox behavior and oxygen reduction activity of Fe-N-C electrocatalysts in different electrolytes

The iron redox behavior and oxygen reduction reaction (ORR) activity of Fe-N-C ORR electrocatalysts synthesized by a variety of techniques were investigated as a function of the identity of the electrolyte anion (bisulfate/sulfate or perchlorate) at a constant pH. In situ X-ray absorption spectroscopy data support the assignment of the redox peaks in the voltammograms to the Fe 3+ /Fe 2+ redox couple. It was found that for a given Fe-N-C catalyst, there is a correlation between the Fe redox couple peak potential and the ORR activity in perchloric acid electrolyte, but not in sulfuric acid electrolyte. While a higher Fe redox couple potential (≥ 110 mV higher) was observed in perchloric acid electrolyte, a higher ORR activity was obtained in sulfuric acid electrolyte. Here, the higher ORR activity observed in sulfuric acid than perchloric acid was correlated with the higher peak current and larger faradaic charge for the Fe redox couple. A study of the Fe redox behavior using a cavity microelectrode, eliminating the impact of ionomer, showed that the interaction of H 2 SO 4 with Fe-N-C is stronger than that of HClO 4 and that Fe redox in both electrolytes is a reversible surface electrochemical reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Lithium-Ion Battery Materials as Tunable, “Redox Non-Innocent” Catalyst Supports

The development of general strategies for the electronic tuning of a catalyst’s active site is an ongoing challenge in heterogeneous catalysis. To this end, herein, we describe the application of Li-ion battery cathode and anode materials as redox non-innocent catalyst supports that can be continuously modulated as a function of lithium intercalation. A zero-valent nickel complex was oxidatively grafted onto the surface of lithium manganese oxide (Li x Mn 2 O 4 ) to yield isolated Ni2+ occupying the vacant interstitial octahedral site in the Li diffusion channel on the surface and subsurface of the spinel structure (Ni/Li x Mn 2 O 4 ). Additionally, the activity of Ni/Li x Mn 2 O 4 for olefin hydrogenation, as a representative probe reaction, was found to increase monotonically as a function of support reductive lithiation. Simulation of Ni/Li x Mn 2 O 4 reveals the dramatic impact of surface redox states on the viability of the homolytic oxidative addition mechanism for H 2 activation. Catalyst control through support lithiation was extended to an organotantalum complex on Li x TiO 2 , demonstrating the generality of this phenomenon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

25 ENERGY STORAGE↗