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At least 73 records · Page 4

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling

La 2 ⁢O 3 ⁢Mn 2 ⁢Se 2 : A correlated insulating layered d-wave altermagnet

Altermagnets represent a new class of magnetic phases without net magnetization, invariant under a combination of rotation and time reversal. Unlike conventional collinear antiferromagnets (AFM), altermagnets could lead to new correlated states and important material properties deriving from their nonrelativistic spin-split band structure. Indeed, they serve as the magnetic analogue of unconventional superconductors and can yield spin-polarized electrical currents in the absence of external magnetic fields, making them promising candidates for next-generation spintronics. Here, we report altermagnetism in the correlated insulator, magnetically ordered tetragonal oxychalcogenide, L⁢a 2 ⁢O 3⁢ M⁢n 2 ⁢S⁢e 2 . Symmetry analysis reveals a 𝑑 𝑥 2 −𝑦 2 -wave-like spin-momentum locking arising from the M⁢n 2 ⁢O Lieb lattice, supported by density functional theory (DFT) calculations. Magnetic measurements confirm the AFM transition below ∼166K while neutron pair distribution function analysis reveals a 2D short-range magnetic order that persists above the Néel temperature. Single crystals are grown and characterized using x-ray diffraction, optical and electron microscopy, and micro-Raman spectroscopy to confirm the crystal structure, stoichiometry, and uniformity. Furthermore, our findings establish L⁢a 2⁢ O 3 ⁢M⁢n 2⁢ S⁢e 2 as a model altermagnetic system realized on a Lieb lattice.

36 MATERIALS SCIENCE

First-principles investigation of cerium and neodymium diffusion in BCC chromium and vanadium via vacancy-mediated transport

Lanthanide transport plays a crucial role in the performance and longevity of metallic nuclear fuels. This study examines the diffusion behavior of Ce and Nd—two major fission products—in body-centered cubic (BCC) Cr and V, which are potential liner or coating materials for mitigating fuel-cladding chemical interactions (FCCI). Using density functional theory (DFT) calculations and self-consistent mean-field (SCMF) analysis, the vacancy-mediated diffusion coefficients are evaluated. Our findings reveal that Ce and Nd act as oversized solutes and are strongly bound to vacancies in BCC Cr and V, with diffusivities in Cr significantly lower than in V and in hexagonal closed-packed (HCP) Zr, as investigated in our previous work. The activation energies for Ce and Nd diffusion are 3.39 and 3.32 eV, respectively, in BCC Cr, and 2.56 and 2.33 eV, respectively, in BCC V. Analysis of vacancy drag and partial diffusion coefficient ratios indicates a strong tendency for lanthanide enrichment at vacancy sinks in BCC Cr, and to a lesser extent in BCC V, with this effect persisting up to the melting point in Cr and remaining substantial for Nd in V at high temperatures. Under irradiation, the increase in vacancy concentration is expected to enhance lanthanide transport, potentially accelerating interactions at liner-cladding interfaces. Although BCC Cr exhibits relatively low lanthanide diffusivities under equilibrium conditions, the expected segregation tendencies under irradiation suggest that Zr liners may be a more favorable option. Further investigations using rate theory, cluster dynamics, and phase-field modeling are required to quantitatively assess the performance of these materials in reactor environments.

36 - MATERIALS SCIENCE

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery

Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Lastly, we demonstrate that the cis model may be combined with Δ-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Interactive multiscale modeling to bridge atomic properties and electrochemical performance in Li-CO 2 battery design

Li-CO 2 batteries are promising energy storage systems due to their high theoretical energy density and CO 2 fixation capability, relying on reversible Li 2 CO 3 /C formation during discharge/charge cycles. Here, we present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO 2 battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb 0.67 Bi 1.33 Te 3 catalyst. DFT and AIMD determined the electrical conductivities of Sb 0.67 Bi 1.33 Te 3 and Li 2 CO 3 using the Kubo–Greenwood formalism and studied the CO 2 reduction mechanism on the cathode catalyst. MD simulations calculated the CO 2 diffusion coefficient, Li + transference number, ionic conductivity, and Li + solvation structure. The FEA model, parameterized with atomistic simulation data, reproduced the available experimental voltage–capacity profile at 1 mA/cm 2 and revealed spatio-temporal variations in Li 2 CO 3 /C deposition, porosity, and CO 2 concentration dependence on discharge rates in the cathode. Accordingly, Li 2 CO 3 can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm 2 and 1 mA/cm 2 , respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm 2 to 6200 mAh/g at 1 mA/cm 2 , due to pore clogging from excessive discharge product deposition that limits CO 2 transport to the cathode interior. Therefore, the performance of Li-CO 2 batteries can be improved by enhancing CO 2 transport, regulating Li 2 CO 3 deposition, and optimizing cathode architecture.

Battery performance

From Oxo to Oxyl to Biradical: Systematic Multireference Calculations of Methane Activation at MOF Nodes

Methane C–H activation at transition-metal sites often involves electronic structures that challenge conventional single-reference electronic structure descriptions. Although Kohn–Sham density functional theory (DFT) is widely used to study catalytic trends, its reliability for reactions involving strongly correlated species remains uncertain. Here we present a systematic multireference investigation of methane activation at metal–organic framework (MOF) node catalysts across the 3d transition-metal series. We introduce an automated workflow for active space selection to enable consistent application of multireference methods, including multiconfiguration pair-density functional theory and n-electron valence state perturbation theory, to these catalytic systems. These calculations show substantial static correlation in the C–H activation reaction step and predict activation barriers that differ from DFT by 30–70 kJ mol–1, with DFT often qualitatively disagreeing in barrier height trends across transition metals. Analysis of multireference wave functions shows that reactivity is governed by the electronic structure of the M–O moiety along a continuum from metal–oxo to oxyl radical and O biradical character. Increased oxygen-centered spin density and weakened M–O bonding are identified as descriptors of catalytic activity which correlate with lower activation barriers.

Wardzala, Jacob

Insights into the mechanisms of NH3 inhibition on Cu-CHA SCR catalysts

This work elucidates the atomic-scale mechanism behind ammonia (NH3) inhibition during the selective catalytic reduction (SCR) of NO? on Cu-CHA catalysts, a key issue limiting low-temperature emission control. Using SCR kinetic analysis, operando electron paramagnetic resonance (EPR) spectroscopy, and density functional theory (DFT), we demonstrate that NH3 inhibition primarily slows the oxidation half-cycle (OHC), while the reduction half-cycle (RHC) remains unaffected. DFT simulations reveal that excess NH3 substantially increases the diffusion barrier for CuI ions, hindering formation of essential CuII-oxo dimer intermediates and thus suppressing OHC kinetics. Operando EPR studies confirm that this inhibition strongly depends on operating temperature and catalyst Cu loading. Our findings highlight strategies to counteract NH3 inhibition, including optimizing Cu loading, precisely managing NH3:NO feed ratios, and enhancing CuI ion mobility through tailored catalyst design and operational adjustments, thereby advancing the efficiency of emission control technologies in automotive applications.

Deka, Dhruba Jyoti

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery

LDRD conclusion poster - Synthesizing Heterometallic Uranium Single Crystals to Understand the Influence of the Secondary Metals on Uranyl Axial Bond Strength

Understanding how transition metals influenced the chemistry of lanthanide and actinide (f-element) materials is critical for advancing separation technologies, materials design, and coordination chemistry. This project examined how incorporating first-row transition metals affected the structural and spectroscopic properties of f-element coordination polymers. In uranium(VI)-based systems synthesized with 2,6-pyridinedicarboxylic acid (PDC) ligands, single-crystal X-ray diffraction and Raman spectroscopy revealed that the presence of transition metals shortened the uranyl axial bond and induced a blue shift in its symmetric stretching vibration—evidence of increased bond strength. Electronic structure analysis, including Density of States (DOS) calculations using density functional theory (DFT), revealed altered orbital overlaps and highlighted the role of transition metal d-orbitals in modulating bonding. Raman modes were modeled using truncated structural fragments in collaboration with the University of Notre Dame, and although the predicted frequencies were lower than experimental values, they remained within expected ranges. In parallel, similar experiments with cerium (Ce) in the presence of cobalt (Co) and PDC demonstrated multi-step single-crystal-to-single-crystal transformations—behavior not observed in the uranium systems. Initial products included light yellow, orange, and polycrystalline materials. Single-crystal X-ray diffraction studies, conducted in collaboration with the Colorado School of Mines, identified the yellow phase as monometallic Ce(PDC)2(H2O)2·4H2O and the orange phase as heterometallic Ce2Co(PDC)4(H2O)6. After standing in solution for one week, both phases fully transformed into a dark yellow crystalline phase, [Ce3(PDC)5(H2O)8].6(H2O). Remarkably, this transformation was reversible—disturbing the equilibrium by removing some crystals caused reversion to the initial Ce(PDC)2(H2O)2·4H2O phase, highlighting dynamic behavior. All three structures were previously unreported. Solid-state UV-visible and Raman spectroscopy further distinguished these phases, revealing ligand-to-metal charge transfer involving Ce and characteristic d–d transitions from Co(II). The precise mechanism driving these transformations remained unclear; however, pH-dependent experiments confirmed that the transformation did not occur when the pH decreased. Overall, the project demonstrated that transition metals could be employed to tune bonding interactions, structural dimensionality, and optical properties in f-element materials, establishing new pathways for designing functional heterometallic systems. The work resulted in several novel structural discoveries and fostered productive collaborations with the University of Notre Dame and the Colorado School of Mines.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C

A Frequency Analysis Approach for Categorizing Air Traffic Behavior

A method of analyzing National Air Space (NAS) air traffic that uses the Discrete Fourier Transform (DFT) is presented. The DFT is used to transform time domain traffic count data into the frequency domain where the sources of traffic in air spaces can be identified and characterized more easily. It is shown in simulation that individual traffic flows within Air Route Traffic Control Centers can be distinguished by their periodicity in the DFT plot. Next, three Traffic Management Initiatives (playbook rerouting, metered flows, and Ground Delay Programs) are implemented in simulations and their signature effects on the traffic are identified using the DFT. Finally, historical flight data is studied and the DFT is applied to sector traffic count data. It is found that in many cases, variations in traffic due to rerouting and convective weather disturbances are better highlighted in the frequency domain than in the original time domain data. Initial results of the DFT show it has potential as a tool for measuring and/or predicting NAS behavior for daily tactical planning and control purposes.

fourier transform

Strong, Yet Split Hydrogen Bonding with Ice Rules in Delafossite (H/D)RhO 2

Despite remaining enigmatic, strong hydrogen bonding provides an advanced design handle for tailoring the properties of functional materials. Here, in this study, 3 R –(H/D)RhO 2 delafossites (prepared by ion exchange of Na + from NaRhO 2 ) contain H/D in linear coordination with O, linking Rh III O 2 layers. Bragg and real-space X-ray and neutron scattering analysis, vibrational and solid-state NMR spectroscopy, and density functional theory (DFT)–based electronic structure calculations have been employed to understand the nature of the hydrogen bonding. Despite short distances between H/D and the two O to which they are bonded, a clear double-minimum corresponding to a shorter and longer (H/D)–O distance is established. The triangular lattices formed by H/D appear to display ice-like disorder, corroborated by low-temperature heat capacity measurements.

NMR spectroscopy

NH 3 -Mediated Reactive Capture and Conversion: Integrating CO 2 Absorption from Flue Gas with CO Production via NH 4 HCO 3 Electrolysis

Efficient carbon capture and utilization require strategies that minimize energy penalties of CO 2 regeneration and compression. Reactive capture and conversion (RCC) address this challenge by integrating capture with direct electrochemical conversion. Here, we show an NH3-mediated tandem RCC system that couples capture of CO 2 from simulated flue gas (10% v/v CO 2 in N 2 ) with electroreduction of NH 4 HCO 3 to CO over a Ni single-atom catalyst (Ni-SAC). Speciation modeling and capture experiments revealed that a deep CO 2 capture with C/N ratio of 0.65 was achieved using 2.5 M NH 3 from simulated flue gas. Electrolysis of the resulting NH 4 HCO 3 on the Ni- SAC delivered an 85% CO Faradaic efficiency at 100 mA/cm 2 with excellent tolerance to NH 3 /NH 4 + as confirmed by DFT calculations and ab initio molecular dynamics (AIMD) simulations. Further, the technoeconomic analysis established a levelized total cost of CO manufacturing of $25.43/kmol, gauging the practical viability. Overall, this study holds great potential to decarbonize the chemical manufacturing industry while reducing synthetic production costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

Real-Space Pseudopotential Method for the Calculation of Third-Row Elements X-ray Photoelectron Spectroscopic Signatures

X-ray photoelectron spectroscopy (XPS) is a powerful characterization technique that unveils subtle chemical environment differences via core– electron binding energy (CEBE) analysis. We extend the development of realspace pseudopotential methods to calculating 1s, 2s, and 2p 3/2 CEBEs of third-row elements (S, P, and Si) within the framework of Kohn–Sham density-functional theory (KS-DFT). The new approach systematically prevents variational collapse and simplifies core-excited orbital selection within dense energy level distributions. However, careful error cancellation analysis is required to achieve accuracy comparable to all-electron methods and experiments. Combined with real-space KS-DFT implementation, this development enables large-scale simulations with both Dirichlet boundary conditions and periodic boundary conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

High-Energy Hybridized States Enable Long-Lived Hot Electrons in Cobaloxime-Silicon Nanocrystal System

Strong electronic coupling is achieved between the molecular catalyst cobaloxime ([Co]) and silicon nanocrystals (Si NCs) bridged by an ethylenepyridine group derived from vinylpyridine (vpy) covalently bound to the Si NC surface (Si-vpy-[Co]). The ethylenepyridine tether in Si-vpy-[Co] is key to dramatic changes to the system’s physical properties which are not observed in the corresponding formylpyridine (fpy) system (Si-fpy-[Co]) consistent with strong electronic coupling previously observed only in dark electrochemical systems. UV−vis absorption spectroscopy reveals new [Co]-centered electronic states in Si-vpy-[Co], and transient absorption spectroscopy finds a strong absorption feature appearing within 250 fs and persisting for at least 5 ns. Astoundingly, spectroelectrochemical measurements reveal that this absorption feature is consistent with both the singly reduced [Co] − and doubly reduced [Co] 2− complexes, leading to the conclusion that these long-lived charges are derived from high-energy “hot” electrons residing in [Co]-centered states. Detailed analysis using cyclic voltammetry, spectroelectrochemistry, electron paramagnetic resonance spectroscopy, and density functional theory (DFT) calculations provides insight into the unique electronic structure created in Si-vpy-[Co]. DFT reveals that the new electronic states arise from hybridization between deep Si NC band states and high-energy molecular orbitals of the ethylenepyridine tether and the [Co] catalyst and are facilitated by σ-bonding character at the ethylenepyridine linkage. This study demonstrates that strong electronic coupling achieved through precise molecular chemistry can change the paradigm of otherwise fixed energy levels in hybrid photoelectrochemical systems for artificial photosynthesis and related applications.

14 SOLAR ENERGY

Probing the elastic modulus and hardness of superionic boron cluster solid electrolytes

Polyhedral borane salts are highly tunable compounds with the potential to be used as solid electrolytes due to their high Li and Na superionic conductivity and relatively wide electrochemical stability window. In considering their application to all solid-state batteries, their mechanical properties play a critical role in their operation due to stresses the solid electrolyte is subjected to during battery operation. Density functional theory (DFT) calculations have provided an initial assessment of bulk and elastic moduli for some selected boron cluster solid electrolytes, but direct measurements are still scarce. Here, in this paper, we report the elastic moduli and hardnesses of LiCB 11 H 12 , LiCB 9 H 10 , NaCB 11 H 12 , and NaCB 9 H 10 for the first time using nanoindentation continuous stiffness method (CSM) measurements under inert atmosphere. Experimental modulus values ranging 8.8–12.3 GPa and hardness values ranging 0.15–0.38 GPa are lower than that of other inorganic solid-state electrolytes such as oxide- and sulfide-based solid electrolytes. DFT calculations on the expected moduli of these compounds are also presented and discussed. Analysis of the indentation plasticity index reveals that these compounds have higher plasticity index compared to other common oxide and sulfide solid electrolytes. According to the calculated Pugh's ratio, all compounds in this study except LiCB 11 H 12 are considered ductile.

25 ENERGY STORAGE