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At least 145 records · Page 8

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DOE OSTI2026

Extracting Critical Metals from Authentic Bauxite Residue

Securing domestic sources of critical minerals (CM) is paramount to ensuring a robust and self-sustaining technology infrastructure. Utilizing untapped non-conventional sources, like acid mine drainage, coal ash, bauxite residue/red mud, and more is an emerging approach that addresses this national concern while identifying new value-added pathways originating from waste streams. Red mud is a byproduct of the Bayer process that produces alumina and contains a wealth of CM – 50 µg/g Ga, 2.9 mg/g total rare earth elements plus yttrium, 12 mg/g Mn, 58 mg/g Al, and others. About 170 MM tonnes of red mud are co-produced annually alongside the 142 MM tonnes of alumina generated, highlighting the abundancy of this feedstock. In this work, different strategies were explored for extracting CM from authentic bauxite residue that involve thermal heating, microwave heating, and sonication all with different acids and buffers. CM recovery was then explored one step further by testing the adsorption of the extracted metals onto NETL’s Multi-functional Sorbent Technology (MUST). Microwave treatment of red mud proved the most effective CM extraction, whereas sonication was less desirable due to scale-up concerns. Successful recovery of CM from this leachate with MUST supports further studies toward optimizing the overall process.

bauxite residue↗

DOE OSTI2024

Tracking the reaction networks of acetaldehyde oxide and glyoxal oxide Criegee intermediates in the ozone-assisted oxidation reaction of crotonaldehyde

The reaction of unsaturated compounds with ozone (O 3 ) is recognized to lead to the formation of Criegee intermediates (CIs), which play a key role in controlling the atmospheric budget of hydroxyl radicals and secondary organic aerosols. The reaction network of two CIs with different functionality, i.e. acetaldehyde oxide (CH 3 CHOO) and glyoxal oxide (CHOCHOO) formed in the ozone-assisted oxidation reaction of crotanaldehyde (CA), is investigated over a temperature range between 390 K and 840 K in an atmospheric pressure jet-stirred reactor (JSR) at a residence time of 1.3 s, stoichiometry of 0.5 with a mixture of 1% crotonaldehyde, 10% O 2 , at an fixed ozone concentration of 1000 ppm and 89% Ar dilution. Molecular-beam mass spectrometry in conjunction with single photon tunable synchrotron vacuum-ultraviolet (VUV) radiation is used to identify elusive intermediates by means of experimental photoionization energy scans and ab initio threshold energy calculations for isomer identification. Addition of ozone (1000 ppm) is observed to trigger the oxidation of CA already at 390 K, which is below the temperature where the oxidation reaction of CA was observed in the absence of ozone. The observed CA + O 3 product, C 4 H 6 O 4 , is found to be linked to a ketohydroperoxide (2-hydroperoxy-3-oxobutanal) resulting from the isomerization of the primary ozonide. Products corresponding to the CIs uni- and bi-molecular reactions were observed and identified. A network of CI reactions is identified in the temperature region below 600 K, characterized by CIs bimolecular reactions with species like aldehydes, i.e., formaldehyde, acetaldehyde, and crotonaldehyde and alkenes, i.e., ethene and propene. The region below 600 K is also characterized by the formation of important amounts of typical low-temperature oxidation products, such as hydrogen peroxide (H 2 O 2 ), methyl hydroperoxide (CH 3 OOH), and ethyl hydroperoxide (C 2 H 5 OOH). Detection of additional oxygenated species such as alcohols, ketene, and aldehydes are indicative of multiple active oxidation routes. This study provides important information about the initial step involved in the CIs assisted oligomerization reactions in complex reactive environments where CIs with different functionalities are reacting simultaneously. It provides new mechanistic insights into ozone-assisted oxidation reactions of unsaturated aldehydes, which is critical for the development of improved atmospheric and combustion kinetics models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Breaking the Brønsted–Evans–Polanyi Relation with Dual-Metal Sites

Linear scaling relationships impose inherent limitations on catalyst activity; the Brønsted−Evans−Polanyi (BEP) relation, which correlates activation and reaction energies, is a prominent example. Here we report a dual-metal site catalyst (DMSC) on ceria that breaks the BEP relation for C−C coupling of methyl intermediates an elementary step in methane coupling to form ethane. The DMSC structure on CeO 2 (111) was discovered by density-functional theory (DFT) structural exploration and confirmed to be stable via ab initio thermodynamics and ab initio molecular dynamics. Homonuclear and heteronuclear DMSCs of Ni, Pd, Pt, Fe, Ru, Os, Co, Rh, and Ir (45 pairs in total) were examined for methyl affinity and methyl−methyl coupling activation energy. We found that many heteronuclear DMSCs break the BEP linear scaling due to a mixed low-affinity/high-affinity coadsorption of the two methyl groups, decoupling the step responsible for the activation energy (E a ) at the low-affinity site from the overall reaction energy (ΔE) determined by both sites. This mechanism of breaking the BEP relationship via the DMSCs offers a catalyst design principle for C−C coupling reactions.

Chen, Yiming [Vanderbilt Univ., Nashville, TN (Uni↗

DOE OSTI2025

Lignin Extraction and Condensation as a Function of Temperature, Residence Time, and Solvent System in Flow-through Reactors

Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175-250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol-water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol-water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol-water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.

09 BIOMASS FUELS↗

DOE OSTI2026

Lignin Extraction and Condensation as a Function of Temperature, Residence Time, and Solvent System in Flow-through Reactors

Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175–250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol–water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol–water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol–water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.

biorefining↗

DOE OSTI2025

Efficient Synthetic Natural Gas Production from Direct Air Capture Using Titania-Based Dual Function Materials

Converting atmospheric CO2 into methane offers a compelling pathway to store intermittent renewable electricity and enhance energy security using existing natural gas infrastructure. However, current CO2 utilization approaches remain energy- and capital-intensive, largely due to the need for separate capture, purification, and conversion steps. Integrating CO2 capture with catalytic methanation represents a transformative strategy for process intensification on both the unit operation and molecular levels. We report a series of Ru-Na/TiO2 dual function materials (DFMs) for a reactive carbon capture (RCC) process consisting of simulated direct air capture and subsequent CO2 methanation (DACM). Superior methane desorption purity was observed on TiO2-based DFMs (>94 %) relative to state-of-the-art Al2O3-supported DFMs (77 %). Modifying the process to begin CO2 adsorption immediately following the methanation stage, rather than beginning adsorption near ambient temperature, resulted in >97 % methane desorption purity, suitable for injection into the natural gas pipeline without additional CO2 separation steps.

organic↗

DOE OSTI2024

Adsorbate-induced adatom formation on Au-Cu bimetallic alloys and its possible consequences for CO 2 electroreduction

The adsorbate-induced formation of sub-nanometer clusters on transition-metal single crystals observed in previous high-pressure microscopic studies hinted at the in-situ formation of unique active sites even on large nanoparticle catalysts. We propose that the adatom formation energy can be used as an energetic descriptor for the initial step toward the adsorbate-induced metal-cluster formation process. This descriptor can be efficiently computed using density functional theory (DFT) calculations and applied for screening and identification of metal catalysts where this phenomenon may play an important role in generating active sites in-situ. As a proof of concept, here, we construct an adatom formation energy database for three Au x Cu y alloys (x:y = 3:1, 1:1, or 1:3) and eighteen adsorbates (H, C, N, O, F, S, Cl, Br, I, CH x , NH x (x = 1 – 3), CO, NO, and OH) commonly involved in catalytic reactions. The energetics of adatom formation were examined in all cases where the (111) terrace, (211) step-edge, and (874) kink were the sources of the adatom. We demonstrate that the presence of an adsorbate could alter not only the energetics for adatom formation but also the elemental nature of the preferred adatom being formed. Using our database, we identified promising systems which favor adsorbate-induced adatom formation under near-ambient conditions. Specifically, CO-induced adatom formation on all three Au-Cu alloy surfaces could occur under CO 2 electroreduction (CO 2 RR) conditions. This phenomenon offers a qualitative explanation for the experimentally observed CO 2 RR activity on Au-Cu alloy catalysts. As a result, our methodology offers an easily expandable and efficient approach for large-scale catalyst screening with regards to adatom/cluster formation under reaction conditions and provides insight into the possible nature of active sites on alloy catalysts from a novel perspective.

Active site↗

DOE OSTI2026

Magnus method for electronic structure calculations at extreme conditions

We present the application of Magnus based methods to the solution of first order coupled ordinary differential equations in High Energy Density (HED) physics applications. Our focus is on the application to quantum mechanical methods, specifically on the solution of the radial Dirac equation for real and complex energies. HED applications require accurate solutions across a wide range of spatial and energy domains, including regimes where the solutions exhibit pronounced oscillatory behavior. Such cases pose significant computational challenges. We demonstrate that Magnus-based integrators can efficiently and accurately address these challenges. We discuss the implementation of the Magnus method for the solution of the radial Dirac equation, including practical considerations such as the evaluation of matrix exponentials, numerical integration, error estimation, and adaptive step size control. We also discuss the application of these methods to complex energy Green’s function techniques and the efficient approximation of integrals of the solutions relevant to HED electronic structure calculations. Here, we demonstrate the accuracy and robustness of the resulting method in applications to the free-particle case, for which analytic solutions are available for comparison, as well as the challenging case of gold at HED conditions.

general physics↗

DOE OSTI2024

Analytical ab initio hessian from a deep learning potential for transition state optimization

Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2–3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Equilibrium-gated pattern formation: How molecular dissociation thermodynamics drive emergent behavior in dissipative polymeric systems

Emergent patterns in biological systems arise through dissipative processes that balance reaction and transport phenomena, producing highly functional properties from self-regulating mechanisms. Synthetic fabrication, by contrast, often relies on user-controlled, multistep methods that lack the self-organizing capabilities of natural systems. Inspired by nature, we sought chemical systems that integrate strongly coupled reaction and transport phenomena, identifying frontal ring-opening metathesis polymerization (FROMP) as a method capable of creating diverse forms and functions through reactive processing. By employing discrete molecular initiators, FROMP allows precise control of key reaction steps—inhibition, initiation, and propagation. Using an integrated computational and experimental framework, we uncover how near-equilibrium inhibition dynamics, coupled with far-from-equilibrium reaction kinetics, drive pattern formation in frontally polymerized synthetic materials. We propose the concept of equilibrium-gated pattern formation, demonstrating how initiator chemistry can be tuned to achieve programmable macroscale properties. Our study reveals a surprising insight: Emergent behavior in FROMP systems arises from the inhibition-dominated regime of resin composition, expanding prior observations that such behavior is confined to a narrow compositional space near the boundary between front quenching and uniform front propagation. We identify a broader compositional window, far from the quenching regime, where emergent behavior reliably manifests. This expanded design space significantly enhances the operational flexibility of reactive systems and their capacity for self-organization. Furthermore, these insights provide a roadmap for designing bioinspired materials with self-organizing capabilities, unlocking possibilities in synthetic manufacturing.

Architected Polymers↗

DOE OSTI2024

Soil microbiome interventions for carbon sequestration and climate mitigation

Mitigating climate change in soil ecosystems involves complex plant and microbial processes regulating carbon pools and flows. Here, we advocate for the use of soil microbiome interventions to help increase soil carbon stocks and curb greenhouse gas emissions from managed soils. Direct interventions include the introduction of microbial strains, consortia, phage, and soil transplants, whereas indirect interventions include managing soil conditions or additives to modulate community composition or its activities. Approaches to increase soil carbon stocks using microbially catalyzed processes include increasing carbon inputs from plants, promoting soil organic matter (SOM) formation, and reducing SOM turnover and production of diverse greenhouse gases. Marginal or degraded soils may provide the greatest opportunities for enhancing global soil carbon stocks. Among the many knowledge gaps in this field, crucial gaps include the processes influencing the transformation of plant-derived soil carbon inputs into SOM and the identity of the microbes and microbial activities impacting this transformation. As a critical step forward, we encourage broadening the current widespread screening of potentially beneficial soil microorganisms to encompass functions relevant to stimulating soil carbon stocks. Moreover, in developing these interventions, we must consider the potential ecological ramifications and uncertainties, such as incurred by the widespread introduction of homogenous inoculants and consortia, and the need for site-specificity given the extreme variation among soil habitats. Incentivization and implementation at large spatial scales could effectively harness increases in soil carbon stocks, helping to mitigate the impacts of climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

RGM: Random Geological Model Generation Package

This Fortran code is to accompany a manuscript to be submitted to Computers & Geosciences, a high-impact, peer-reviewed journal in computer methods for geosciences research. This Fortran code focuses on generation of synthetic geological models using a multi-randomization strategy. Generating high-fidelity synthetic geological models, including realistic seismic reflector migration images, faults, salt bodies, and relative geological time images, is the key for many supervised machine learning methods that aim to delineate faults and other geological properties of interest from seismic migration images. Our package contains two major functionalities: generating 2D synthetic random geological models and generating 3D synthetic random geological models. In each step of the generation process, we set random values for key properties of a geological model to improve the fidelity of the resulting geological model. The package also includes example codes on how to use the random geological model generation subroutines. We name this package RGM – Random Geological Model generation package.

Gao, Kai↗

DOE OSTI2026

Test of Harmonic Coil Accuracy Using a Multi Vertex Probe

Magnets are integral components of particle accelerators, with different multipoles used to perform various beam conditioning functions, like steering and focusing. Testing accelerator magnets to ensure compliance with specification is a crucial step of fabrication. One of the most popular techniques for verifying magnetic field strength and integrity is the harmonic coil probe. Harmonic coil probes are comprised of passive loops that are rotated around the aperture of a magnet and are used to measure magnetic flux. Oftentimes the measurements of the harmonic coil probe are affected by transverse vibrations along the axis of rotation and torsional vibrations of the position closure sensor, both causing spurious harmonics. To make matters challenging, it is difficult to distinguish between the real harmonics of the magnet and the spurious harmonics during analysis. This research explores the use of a novel configuration of harmonic coil probe, Multi-Vertex Probe (MVP), to address multiple factors that affect the accuracy and repeatability of harmonic coil probe measurement. Most of the results are based on simulation data, in which artificial torsional distortions are introduced and removed with the proposed processing method. In addition to implementing torsional vibration removal in simulation, laboratory analysis regarding the source of vibrations was performed using a pre-fabricated MVP. More specifically, various mechanical conditions were used to explore the standard deviation of main harmonic’s flux amplitude. The conditions included: coupling type (rigid or soft), rotation speed, probe starting position within magnetic field, and cabling location inside the magnetic field. Along with this, the MVP’s measured noise floor in and outside a magnetic field was quantified.

Lofquist, Claire [Northern Illinois U.]↗

DOE OSTI2025

As part of its commitment to advancing safety and reliability assessment methodologies, Argonne National Laboratory pioneered the use of an evaluation method called the Success Path Method (SPM) to improve risk management for offshore oil and gas operations. The development of the SPM at Argonne has been driven by the need to improve existing risk assessment methodologies by focusing on the steps necessary for success rather than failure modes alone. This is particularly important for industrial environments like offshore facilities that perform multiple functions under a continuously evolving set of operational conditions – such as water depth and temperature, currents, and weather conditions. In these dynamic environments, the traditional Probabilistic Risk Assessment (PRA) approach is far too complex as it focuses on what can go wrong – which comprises an infinite failure space that must be fully explored and understood. By shifting the focus to a finite space of success paths, the SPM enables operators and decision makers to prioritize a manageable number of steps that must go right to ensure success. Building on its five decades of experience in safety assessments for the nuclear industry, Argonne made major adaptations to existing risk assessment methods utilizing features similar to fault trees that are traditionally used in PRA to map all pathways in which the system can malfunction. In contrast, SPM identifies the components and processes that must function correctly to achieve specific outcomes – such as preventing the uncontrolled release of hydrocarbons during drilling operations. The SPM framework integrates equipment, procedures, software, processes, and human actions to ensure that physical barriers meet critical safety functions in dynamic operational conditions. This approach helps identify failure modes and improve operational risk management by narrowing the focus to key success elements, which in turn reduces uncertainty and helps users understand, manage, and respond to failures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Modeling Oxidative Dehydrogenation of Propane with Supported Vanadia Catalysts Using Multireference Methods

The oxidative dehydrogenation of propane over supported vanadium oxide catalysts poses significant computational challenges due to complex electronic structure changes along the reaction coordinate, driven primarily by changes in the oxidation states of vanadium. To address these challenges, we systematically test quantum chemical methods, including multireference (MR) approaches, domain-based local pair natural orbital coupled cluster theory (DLPNO-CCSD(T)), and density functional theory (DFT). The initial C–H bond-breaking transition state requires MR treatment due to its multireference character, while subsequent steps permit efficient single-reference calculations. For the rate-limiting C–H activation step mediated by the vanadyl moiety, complete 1 active space second-order perturbation theory (CASPT2) yields an apparent activation barrier (E app 600K ) of 138 kJ/mol, consistent with experimental values (134 ± 4 kJ/mol; Gruene et al. Catal. Today 2010, 157, 137). In contrast, DLPNO-CCSD(T) overestimates this barrier (198 kJ/mol), whereas DFT predictions span 125–150 kJ/mol, depending on the functional. Our multireference investigation of this transition metal oxide-catalyzed process demonstrates that an active space that incorporates the C–H σ and V=O σ/π bonding orbitals, oxygen lone pairs, and their antibonding counterparts adequately captures electronic structure changes along the chemical transformation. Furthermore, these findings provide a general strategy for active space selection in transition metal oxide-catalyzed C/O–H bond activation reactions. The reference dataset from this work, which includes MR calculations with manually selected active spaces for all intermediates and transition states in the propane ODH reaction network, will serve as a benchmark for automating active space selection in similar systems.

Catalysts↗

DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗

DOE OSTI2025

Operando Contactless EFISH Study of the Rate-Determining Step of Light-Driven Water Oxidation on TiO 2 Photoanodes

For many slow solar-fuel-forming reactions, the accumulation of photogenerated minority carriers on the photoelectrode surface leads to light-induced band edge unpinning, affecting the junction properties by decreasing band bending in the semiconductor space charge layer and increasing the driving force of surface reactions in the electric double layer. In this study, we demonstrate a contactless operando electric field-induced second harmonic generation (EFISH) method for measuring the band bending change (δΔΦ SCR L ) on photoelectrodes upon photoexcitation. For ndoped rutile TiO 2 water oxidation photoanodes at pH 7, δΔΦ SCR L increases at more positive potentials or higher illumination power density until it reaches saturation values. We show that under fast mass transport conditions, δΔΦ SCR L is exclusively attributed to the accumulated charged rate-determining species that can be regarded as temporary surface states, and the relationship between the photocurrent and δΔΦ SCR L can be well modeled by assuming that hole trap states function as the reaction center. Kinetic isotope experiments identify proton-coupled electron transfer as the rate-determining step and suggest a possible chemical nature of the key intermediate. We demonstrate that light-induced band edge unpinning is a beneficial feature under high illumination conditions for oxygen evolution reaction on TiO 2 because it maintains the photon-to-current conversion efficiency by enhancing the surface reaction driving force, shedding light on the actual device application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Iridium Polypyridyl Carboxylates as Excited-State PCET Catalysts for the Functionalization of Unactivated C–H Bonds

The design of catalysts capable of functionalizing unactivated C(sp 3 )–H bonds remains a significant goal in synthetic organic chemistry. Herein, we present a novel set of iridium polypyridyl complexes bearing pendent Brønsted basic carboxylates that become potent hydrogen atom abstraction catalysts upon visible light irradiation. Thermochemical and spectroscopic characterization reveal that these excited-state complexes exhibit bond dissociation free energies (BDFEs) of up to 105 kcal mol –1 with long excited-state lifetimes. We demonstrate that these complexes can catalyze C–H alkylation reactions in which the Ir carboxylate mediates both C–H abstraction and formation steps. Mechanistic, spectroscopic, and computational studies are consistent with C−H abstraction proceeding through an excited-state proton-coupled electron transfer (PCET) step. Here, the modular nature of these Ir polypyridyl complexes establishes a foundation for designing tunable and efficient C–H functionalization catalysts based on covalent tethering of excited-state oxidants and bases.

Alcohols↗