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Synthesis, Stability, and Magnetic Properties of Antiperovskite Co 3 PdN

Experimental synthesis and characterization of theoretically predicted compounds are important steps in the materials discovery pipeline. Here, we report on the synthesis of Co 3 PdN, which was recently predicted to be a stable magnetic antiperovskite. The Co 3 PdN thin films were grown by reactive sputtering and were confirmed to form in an antiperovskite crystal structure. The thermal stability of the compound is demonstrated up to 600 K by in situ X-ray diffraction, though the phase persists at slightly higher temperatures (700 K) in an air-free magnetometer. Both ab initio calculations and magnetization measurements find Co 3 PdN to be ferromagnetic with an experimentally determined Curie temperature of T C = 560 ± 5 K. The saturation magnetization of 1.2 μ B /Co found in the experiment is slightly lower than the 1.7 μ B /Co value expected by theory. A narrow magnetic hysteresis loop with a coercive field of 100 Oe at low temperature suggests that Co 3 PdN might be useful in electronic applications requiring fast switching of the magnetization vector. While prior prediction of Co 3 PdN showed a gapped electronic band structure for each spin channel, we show that this was due to incomplete sampling of Brillouin zone paths and that band crossings exist along R-X|M and X|M-R paths. The metallic nature of Co 3 PdN is further confirmed by temperature-dependent transport measurements, which also show a considerable anomalous Hall effect. Altogether, this work represents an appreciable step toward understanding the synthesis, structure, stability, and properties of a new magnetic material.

14 SOLAR ENERGY↗

Materials Data on PdN by Materials Project

PdN is Boron Nitride structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is two-dimensional and consists of two PdN sheets oriented in the (0, 0, 1) direction. Pd2+ is bonded in a trigonal planar geometry to three equivalent N2- atoms. All Pd–N bond lengths are 1.95 Å. N2- is bonded in a trigonal planar geometry to three equivalent Pd2+ atoms.

36 MATERIALS SCIENCE↗

Materials Data on PdN by Materials Project

PdN is Zincblende, Sphalerite structured and crystallizes in the cubic F-43m space group. The structure is three-dimensional. Pd2+ is bonded to four equivalent N2- atoms to form corner-sharing PdN4 tetrahedra. All Pd–N bond lengths are 2.06 Å. N2- is bonded to four equivalent Pd2+ atoms to form corner-sharing NPd4 tetrahedra.

36 MATERIALS SCIENCE↗

Materials Data on PdN by Materials Project

PdN is Molybdenum Carbide MAX Phase-like structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Pd2+ is bonded to six equivalent N2- atoms to form a mixture of face, edge, and corner-sharing PdN6 octahedra. The corner-sharing octahedral tilt angles are 46°. All Pd–N bond lengths are 2.22 Å. N2- is bonded to six equivalent Pd2+ atoms to form a mixture of distorted edge and corner-sharing NPd6 pentagonal pyramids.

36 MATERIALS SCIENCE↗

Materials Data on PdN by Materials Project

PdN is Tetraauricupride structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Pd2+ is bonded in a body-centered cubic geometry to eight equivalent N2- atoms. All Pd–N bond lengths are 2.41 Å. N2- is bonded in a body-centered cubic geometry to eight equivalent Pd2+ atoms.

36 MATERIALS SCIENCE↗

Materials Data on PdN by Materials Project

PdN is Tungsten Carbide structured and crystallizes in the hexagonal P-6m2 space group. The structure is three-dimensional. Pd2+ is bonded to six equivalent N2- atoms to form a mixture of distorted edge, face, and corner-sharing PdN6 pentagonal pyramids. All Pd–N bond lengths are 2.26 Å. N2- is bonded to six equivalent Pd2+ atoms to form a mixture of distorted edge, face, and corner-sharing NPd6 pentagonal pyramids.

36 MATERIALS SCIENCE↗

Materials Data on PdN by Materials Project

PdN is Wurtzite structured and crystallizes in the hexagonal P6_3mc space group. The structure is three-dimensional. Pd2+ is bonded to four equivalent N2- atoms to form corner-sharing PdN4 tetrahedra. There are three shorter (2.04 Å) and one longer (2.11 Å) Pd–N bond lengths. N2- is bonded to four equivalent Pd2+ atoms to form corner-sharing NPd4 tetrahedra.

36 MATERIALS SCIENCE↗

A Stochastic Charging Station Deployment Model for Electrified Taxi Fleets in Coupled Urban Transportation and Power Distribution Networks

Metropolitansworldwide are increasingly adopting electric taxis (ET) to address concerns about transportation-related emissions. However, the widespread deployment of electric taxis presents challenges in terms of increased electricity demand and changing demand profiles. This transition impacts both the urban transportation network (TN) and the electricity power distribution network (PDN), highlighting the interdependence between these two systems. Here, in this paper, we propose a two-stage stochastic programming planning model that aims to optimize both the TN and PDN, enabling efficient deployment of charging stations and grid upgrades. Our model seeks to strike a balance between meeting ET drivers' charging preferences, minimizing the costs associated with infrastructure deployment and grid expansion, and harmonizing the coordination between the TN and PDN. Additionally, we explore the potential benefits of utilizing an autonomous ET fleet to enhance overall system performance.

33 ADVANCED PROPULSION SYSTEMS↗

Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling

Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pd n (n = 1-21) clusters supported on CeO 2 (111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dynamic behavior of molecular Pd-acetate trimers and dimers in heterogeneous vinyl acetate synthesis

Vinyl acetate monomer (VAM) is a crucial intermediate in the production of various polymers. While molecular Pd-acetate trimers and dimers, such as Pd 3 (OAc) 6 and K 2 Pd 2 (OAc) 6 , are known to form on potassium acetate (KOAc)-promoted PdAu catalysts during heterogeneous VAM synthesis, their mechanistic role remains unclear. Here, we study the dynamics of different Pd-acetate species by utilizing in situ and operando crystallographic and spectroscopic characterizations combined with computational modeling on monometallic Pd model catalysts. The promoter-free catalyst expectedly shows low catalytic activity and VAM selectivity, corresponding to the complete reduction of Pdn(OAc) 2n species to form Pd 0 and PdC x nanoparticles. Conversely, noticeable quantities of K n Pd 2 (OAc) n+4 species remain on the KOAc-promoted catalyst, leading to smaller nanoparticle formation with 10 times the activity and double the selectivity for VAM. This study reveals that molecular Pd-acetate trimers and dimers are significant indicators of catalytic performance and highlights their structurally dynamic nature in heterogeneous vinyl acetate chemistry.

Jacobs, Hunter P. [Rice Univ., Houston, TX (United↗