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DOE OSTI2023

Co-crystal structures of the fluorogenic aptamer Beetroot show that close homology may not predict similar RNA architecture

Beetroot is a homodimeric in vitro selected RNA that binds and activates DFAME, a conditional fluorophore derived from GFP. It is 70% sequence-identical to the previously characterized homodimeric aptamer Corn, which binds one molecule of its cognate fluorophore DFHO at its interprotomer interface. We have now determined the Beetroot-DFAME co-crystal structure at 1.95 Å resolution, discovering that this RNA homodimer binds two molecules of the fluorophore, at sites separated by ~30 Å. In addition to this overall architectural difference, the local structures of the non-canonical, complex quadruplex cores of Beetroot and Corn are distinctly different, underscoring how subtle RNA sequence differences can give rise to unexpected structural divergence. Through structure-guided engineering, we generated a variant that has a 12-fold fluorescence activation selectivity switch toward DFHO. Beetroot and this variant form heterodimers and constitute the starting point for engineered tags whose through-space inter-fluorophore interaction could be used to monitor RNA dimerization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Quiescent galaxies in a virialized cluster at redshift 2: evidence for accelerated size growth

ABSTRACT We present an analysis of the galaxy population in XLSSC 122, an X-ray-selected, virialized cluster at redshift z = 1.98. We utilize Hubble Space Telescope (HST)Wide Field Camera 3 (WFC3) photometry to characterize the activity and morphology of spectroscopically confirmed cluster members. The quiescent fraction is found to be $88^{+4}_{-20}$ per cent within 0.5r500, significantly enhanced over the field value of $20^{+2}_{-2}$ per cent at z ∼ 2. We find an excess of ‘bulge-like’ quiescent cluster members with Sérsic index n > 2 relative to the field. These galaxies are found to be larger than their field counterparts at 99.6 per cent confidence, being on average $63^{+31}_{-24}$ per cent larger at a fixed mass of $M_\star = 5\times 10^{10} \, \mathrm{M}_\odot$. This suggests that these cluster member galaxies have experienced an accelerated size evolution relative to the field at z > 2. We discuss minor mergers as a possible mechanism underlying this disproportionate size growth.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Inverse design of pore wall chemistry and topology through active learning of surface group interactions

Design of next-generation membranes requires a nanoscopic understanding of the effect of biologically inspired heterogeneous surface chemistries and topologies (roughness) on local water and solute behavior. In particular, the rejection of small, neutral solutes, such as boric acid, poses a heretofore unsolved challenge. In prior work, a computational inverse design technique using an evolutionary optimization successfully uncovered new surface design strategies for optimized transport of water over solutes in smooth, model pores consisting of two surface chemistries. However, extending such an approach to more complex (and realistic) scenarios involving many surface chemistries as well as surface roughness is challenging due to the expanded design space. In this work, we develop a new approach that uses active learning to optimize in a reduced feature space of surface group interactions, finding parameters that lead to their assembly into ordered, optimal patterns. This approach rapidly identifies novel surface functionalizations that maximize the difference in water and boric acid transport through the nanopore. Moreover, we find that the roughness of the nanopore wall, independent of its chemistry, can be leveraged to enhance transport selectivity: oscillations in the pore wall diameter optimally inhibit boric acid transport by creating energetic wells from which the solute must escape to transport down the pore. Furthermore, this proof-of-concept demonstrates the potential for active learning strategies, in concert with molecular simulations, to rapidly navigate complex design spaces of aqueous interfaces and is promising as a tool for engineering water-mediated surface interactions for a broad range of applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection

Traditional methods for detection of nuclear proliferation indicators are usually applied after nuclear proliferation has already occurred. There is a need to advance these methods to perform early detection of nuclear proliferation indicators. In this project, we formulated an early detection problem as a sequential, decision-making, goal inference problem based on research publications of authors, to determine whether it is possible to infer whether an author will publish on a research activity before it has occurred. To develop and test our approach, we selected a civil nuclear activity for our case study. We constructed a state-action-state transition graph from publications of authors associated with the activity and the co-authors of their publications, using titles, abstracts, and author publication sequences. We then used inverse reinforcement learning to model the goal-directed behavior of authors in trajectories that terminate at selected goal states. Using a Bayesian formulation, we computed the probability that authors would reach each selected state from partially observed trajectories of their state transitions in their research topic space. The state with the highest probability was selected as the most probable goal state. Based on our results, we found that 60% of the times we can infer the correct goal state early; sometimes the inference is either delayed, or multiple states could be inferred as goal states. Overall, our results show that it is possible to perform early detection of research activities of authors in a nuclear technology area. Further research is necessary to establish a more accurate understanding of how topic modeling, topic space grid discretization, and the extent of overlap among trajectories of different goal states, affect the goal inference results. The methods developed in this work may be used to enhance data-driven methods for early detection of nuclear proliferation indicators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The evolution of the heaviest supermassive black holes in jetted AGNs

ABSTRACT We present the space density evolution, from z = 1.5 up to z = 5.5, of the most massive ($M \ge 10^9\, \mathrm{M}_{\odot }$) black holes hosted in jetted active galactic nuclei (AGNs). The analysis is based on a sample of 380 luminosity-selected (λL1350 ≥ 1046 erg s−1 and $P_{5\, \text{GHz}}\ge 10^{27}$ W Hz−1) flat spectrum radio quasars (FSRQs) obtained from the Cosmic Lens All Sky Survey (CLASS). These sources are known to be face-on jetted AGNs (i.e. blazars) and can be exploited to infer the abundance of all the (misaligned) jetted AGNs, using a geometrical argument. We then compare the space density of the most massive supermassive black holes hosted in jetted AGNs with those present in the total population (mostly composed by non-jetted AGNs). We find that the space density has a peak at z ∼ 3, which is significantly larger than the value observed in the total AGN population with similar optical/UV luminosities (z ∼ 2.2), but not as extreme as the value previously inferred from X-ray-selected blazars (z ≳ 4). The jetted fraction (jetted AGNs/total AGNs) is overall consistent with the estimates in the local Universe (10–20 per cent) and at high redshift, assuming Lorentz bulk factors Γ ≈ 5. Finally, we find a marginal decrease in the jetted fraction at high redshifts (by a factor of ∼2). All these evidences point towards a different evolutionary path in the jetted AGNs compared to the total AGN population.

(galaxies:) quasars: supermassive black holes↗

DOE OSTI2024

A Ce-CuZn catalyst with abundant Cu/Zn-OV-Ce active sites for CO 2 hydrogenation to methanol

CO 2 hydrogenation to chemicals and fuels is a significant approach for achieving carbon neutrality. It is essential to rationally design the chemical structure and catalytic active sites towards the development of efficient catalysts. Here we show a Ce-CuZn catalyst with enriched Cu/Zn-OV-Ce active sites fabricated through the atomic-level substitution of Cu and Zn into Ce-MOF precursor. The Ce-CuZn catalyst exhibits a high methanol selectivity of 71.1% and a space-time yield of methanol up to 400.3 g·kg cat -1 ·h -1 with excellent stability for 170 h at 260°C, comparable to that of the state-of-the-art CuZnAl catalysts. Controlled experiments and DFT calculations confirm that the incorporation of Cu and Zn into CeO 2 with abundant oxygen vacancies can facilitate H 2 dissociation energetically and thus improve CO 2 hydrogenation over the Ce-CuZn catalyst via formate intermediates. This work offers an atomic-level design strategy for constructing efficient multi-metal catalysts for methanol synthesis through precise control of active sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A single-atom library for guided monometallic and concentration-complex multimetallic designs

Atomically dispersed single-atom catalysts have the potential to bridge heterogeneous and homogeneous catalysis. Dozens of single-atom catalysts have been developed, and they exhibit notable catalytic activity and selectivity that are not achievable on metal surfaces. Although promising, there is limited knowledge about the boundaries for the monometallic single-atom phase space, not to mention multimetallic phase spaces. Here, single-atom catalysts based on 37 monometallic elements are synthesized using a dissolution-and-carbonization method, characterized and analyzed to build the largest reported library of single-atom catalysts. In conjunction with in situ studies, we uncover unified principles on the oxidation state, coordination number, bond length, coordination element and metal loading of single atoms to guide the design of single-atom catalysts with atomically dispersed atoms anchored on N-doped carbon. We utilize the library to open up complex multimetallic phase spaces for single-atom catalysts and demonstrate that there is no fundamental limit on using single-atom anchor sites as structural units to assemble concentration-complex single-atom catalyst materials with up to 12 different elements. Furthermore, our work offers a single-atom library spanning from monometallic to concentration-complex multimetallic materials for the rational design of single-atom catalysts.

36 MATERIALS SCIENCE↗

DOE OSTI2021

High-efficiency catalytic reduction of residual oxygen for purification of carbon dioxide streams from high-pressure oxy-combustion systems

Pressurized oxy-combustion is a promising technology for carbon capture, utilization, and storage. For the captured CO 2 to be used for enhanced oil recovery or stored in geological formations, flue gas impurities, including residual O 2 in the CO 2 stream, must be purified to meet the purity specifications. A catalytic approach to reducing residual O 2 with CH 4 was investigated in this study. Five CoMn- and Cu-based catalysts were synthesized or acquired, and a reverse-flow fixed-bed reactor was used to assess their performance for O 2 removal from a simulated oxy-combustion flue gas at 15 bar. The impacts of the operating parameters on O 2 removal, such as temperature, gas hourly space velocity, O 2 /CH 4 ratio, and gas pressure, were investigated. Among the tested catalysts, the two CoMn catalysts were superior in both activity and selectivity, with the reaction lighting off at about 350 °C and achieving 99% O 2 removal at about 500 °C. Finally, the kinetics of the catalytic reaction is discussed, and the Mars–van Krevelen redox mechanism is deemed valid for describing the reaction pathway for the top-performing CoMn catalysts. The catalytic reaction was determined to be first order in CH 4 and zero order in O 2 under the test conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

The LSST AGN Data Challenge: Selection Methods

Abstract Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DCs) arranged by various LSST Scientific Collaborations that are taking place during the project's preoperational phase. The AGN Science Collaboration Data Challenge (AGNSC-DC) is a partial prototype of the expected LSST data on active galactic nuclei (AGNs), aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took place in 2021, focusing on accuracy, robustness, and scalability. The training and the blinded data sets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift, and class label with the addition of variability features and images. We present the results of four submitted solutions to DCs using both classical and machine learning methods. We systematically test the performance of supervised models (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised ones (deep embedding clustering) when applied to the problem of classifying/clustering sources as stars, galaxies, or AGNs. We obtained classification accuracy of 97.5% for supervised models and clustering accuracy of 96.0% for unsupervised ones and 95.0% with a classic approach for a blinded data set. We find that variability features significantly improve the accuracy of the trained models, and correlation analysis among different bands enables a fast and inexpensive first-order selection of quasar candidates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Data-driven multi-element substitution of TiFe alloys for tunable thermodynamics and enhanced activation behaviour for hydrogen storage

Due to their high volumetric hydrogen storage capacity under moderate storage conditions, TiFe alloys have been widely investigated as candidates for practical solid-state hydrogen storage. Partially substituting Ti or Fe sites can improve the key characteristics of TiFe alloys, such as the first hydrogen absorption step (activation) and the equilibrium hydrogen pressure (thermodynamic properties). However, the selection of substitution elements has heavily relied on intuition and trial-and-error. Also, conventional substitution strategies have mainly focused on single-element substitution within the TiFe alloy, limiting the design space and tunability for target applications. Here, to address this limitation, we report a multi-element substitution strategy motivated by an efficient, data-driven machine learning (ML) approach combined with corroborating density functional theory (DFT) calculations. Our models successfully predict experimentally measured hydride stability in five selected alloys using only compositional descriptors. Most importantly, the multi-element substitution leads to enhanced activation properties compared to pure TiFe, achieving near room-temperature activation behaviour. This work provides a method for on-demand tuning of hydrogen storage and activation properties, which may have broad implications for data-driven discovery of energy storage materials.

Cho, YongJun [Korea Advanced Institute Science and↗

DOE OSTI2022

Conserved features of TERT promoter duplications reveal an activation mechanism that mimics hotspot mutations in cancer

Mutations in the TERT promoter represent the genetic underpinnings of tumor cell immortality. Beyond the two most common point mutations, which selectively recruit the ETS factor GABP to activate TERT, the significance of other variants is unknown. In seven cancer types, we identify duplications of wildtype sequence within the core promoter region of TERT that have strikingly similar features including an ETS motif, the duplication length and insertion site. The duplications recruit a GABP tetramer by virtue of the native ETS motif and its precisely spaced duplicated counterpart, activate the promoter and are clonal in a TERT expressing multifocal glioblastoma. We conclude that recurrent TERT promoter duplications are functionally and mechanistically equivalent to the hotspot mutations that confer tumor cell immortality. The shared mechanism of these divergent somatic genetic alterations suggests a strong selective pressure for recruitment of the GABP tetramer to activate TERT.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

RxnRover/amlro

AMLRO (Active Machine Learning Reaction Optimizer) is an open-source framework designed to accelerate chemical reaction optimization using active learning with classical machine learning regression models. AMLRO integrates space-filling sampling strategies (e.g., Sobol and Latin Hypercube sampling) with iterative model training, prediction, and experiment selection to efficiently navigate complex reaction spaces. The platform supports multiple regression models, flexible multi-objective definitions, and user-defined parameter bounds, enabling data-efficient optimization from small initial datasets. AMLRO is designed for ease of use by experimentalists and can operate as a standalone decision-support tool or be integrated into closed-loop automated experimentation workflows.

Kulathunga, Dulitha Prasanna [Iowa State Universit↗

DOE OSTI2024

Photolithography system including selective light array

A system, device, and method for imparting or transferring a geometric pattern on the surface of a substrate. The device comprises, a housing forming at least a partially enclosed space, a light source body comprising an array of light emitters, a base disposed below the light source body and configured for supporting the substrate having a photoresist layer thereon, and a controller for activating a predetermined number of individual light emitters corresponding to the predetermined geometric pattern. Each individual light emitter within the array of light emitters is selectively activatable to emit a light. The array of light emitters comprises a plurality of light-emitting diodes, a plurality of quantum dots, or both.

Young, Barbara Diane↗

DOE OSTI2024

Sustainable aviation fuels from biomass and biowaste via bio- and chemo-catalytic conversion: Catalysis, process challenges, and opportunities

Sustainable aviation fuel (SAF) production from biomass and biowaste streams is an attractive option for decarbonizing the aviation sector, one of the most-difficult-to-electrify transportation sectors. Despite ongoing commercialization efforts using ASTM-certified pathways (e.g., lipid conversion, Fischer-Tropsch synthesis), production capacities are still inadequate due to limited feedstock supply and high production costs. New conversion technologies that utilize lignocellulosic feedstocks are needed to meet these challenges and satisfy the rapidly growing market. Combining bio- and chemo-catalytic approaches can leverage advantages from both methods, i.e., high product selectivity via biological conversion, and the capability to build C-C chains more efficiently via chemical catalysis. Herein, conversion routes, catalysis, and processes for such pathways are discussed, while key challenges and meaningful R&D opportunities are identified to guide future research activities in the space. Bio and chemo-catalytic conversion primarily utilize the carbohydrate fraction of lignocellulose, leaving lignin as a waste product. This makes lignin conversion to SAF critical in order to utilize whole biomass, thereby lowering overall production costs while maximizing carbon efficiencies. Thus, lignin valorization strategies are also reviewed herein with vital research areas identified, such as facile lignin depolymerization approaches, highly integrated conversion systems, novel process configurations, and catalysts for the selective cleavage of aryl C–O bonds. The potential efficiency improvements available via integrated conversion steps, such as combined biological and chemo-catalytic routes, along with the use of different parallel pathways, are identified as key to producing all components of a cost-effective, 100% SAF.

09 BIOMASS FUELS↗

DOE OSTI2022

Microkinetic Modeling of the Transient CO 2 Methanation with DFT–Based Uncertainties in a Berty Reactor

The transient operation of methanation reactors can become desirable when coupled with fluctuating renewable energies in Power-to-Gas scenarios, which requires suitable kinetic approaches that can describe the transient catalytic phenomena. A combined experimental and theoretical investigation of the transient CO 2 methanation is conducted using concentration forcing to derive a suitable microkinetic model. Methanation experiments are performed with a Ni/SiO 2 catalyst in a Berty-type reactor at industrially relevant conditions. The microkinetics are based on previous work and were automatically constructed for the Ni(111) facet using the Reaction Mechanism Generator. A feasible set of energetic parameters of the microkinetic models was identified in a theory-constrained optimization procedure within the DFT uncertainty space that can accurately reproduce the experimental results on a first-principles basis. The microkinetic model unravels that the formation of H 2 O* and CH 3 * control the activity and selectivity of Ni(111) under the investigated conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Layered Transition Metal Oxides as Ca Intercalation Cathodes: A Systematic First-Principles Evaluation

Finding high-voltage Ca cathode materials is a critical step to unleashing the full potential of high-energy-density Ca-ion batteries. Therefore, first-principles calculations are used to demonstrate that P-type layered calcium transition metal (TM) oxide materials (CaTM 2 O 4 ) with a range of TM substitutions (TM = Ti, V, Cr, Mn, Fe, Co, and Ni) have excellent battery-related properties including thermodynamic stability, average voltage, energy density, synthesizability, ionic mobility, and electronic structure. However, the thermodynamic stability of the charged phase and TM redox activity are shown to be sensitive to TM selection, with CaCo 2 O 4 having the best balance of all considered properties. The utility of combining multiple TMs to expand the chemical search space for TM substitutions is demonstrated by mixing Co and Ni in layered CaTM 2 O 4 .

25 ENERGY STORAGE↗

DOE OSTI2022

CO2 Hydrogenation to Hydrocarbons over Fe/BZY Catalysts

This manuscript reports a CO2 hydrogenation process in a catalytic laboratory-scale packed-bed reactor using an Fe/BZY15 (BaZr0.8Y0.15O3-d) catalyst to form hydrocarbons (e.g., CH4, C2+) at elevated pressure of 30 bar and temperatures in the range 270 = T = 375 degrees C. The effects of temperature, feed composition (i.e., CO2/H2 ratio, and residence time (i.e., Weight Hourly Space Velocity (WHSV) are studied to understand the relationship between CO2 conversion and carbon selectivity. Catalyst characterization elucidates the relationships between the catalyst structure, surface adsorbates, and reaction pathways. Thermodynamic analyses guide the experimental conditions and assist interpreting results. While the feed composition and temperature influence the product distribution, the results suggest that the higher-carbon (C2+) selectivity and yield depend strongly on residence time. The results suggest that the CO2 hydrogenation reaction pathway is similar to Fischer-Tropsch (FT) synthesis. The reaction begins with CO2 activation to form CO, followed by chain-growth reactions similar to the FT process. The CO2 activation depends on the redox activity of the catalyst. However, the carbon chain growth depends primarily on the residence time. as is the case for the FT synthesis, high residence time (on the orders of hours) is required to achieve high C2+ yield. For such high residence times, catalyst-fouling carbon deposition can be problematic. The coke-resistant BZY15 catalyst support contributes to the catalytic activity and enables a coke-free operation for more than 100 h time-on-stream.

bi-functional catalyst↗

DOE OSTI2022

PCM Selection for Heat Pump Integrated with Thermal Energy Storage for Demand Response in Residential Buildings

Phase Change Materials (PCM) based Thermal energy storage (TES) is a widespread solution to shift buildings’ peak energy demand and add stability to the grid. PCMs can be used for space heating and cooling applications in residential buildings by integrating into the heat pump equipment or building envelope via several possible configurations. The heat pump integrated active PCM storage can provide significant energy savings and reduce peak demand, but the benefits vary substantially depending on the system configuration, storage capacity, and the temperature range of the PCM used. It is critical to provide a review of the materials and their suitability in heat pump integrated PCM systems. This paper will present an analysis of the PCM material selection for heat pump integrated TES in various configurations. The comprehensive review will compare the phase change materials with different melting temperatures, and configurations, corresponding to the energy savings and demand impact reported in the literature. This work is significant to guide the design of high-performance heat pump integrated PCM systems.

Sultan, Sara↗