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At least 379 records · Page 21

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DOE OSTI2025

Exploration of structure-activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment linking and active learning

The macrodomain of severe acute respiratory syndrome coronavirus 2 nonstructural protein 3 is required for viral pathogenesis and is an emerging antiviral target. We previously performed an x-ray crystallography–based fragment screen and found submicromolar inhibitors by fragment linking. However, these compounds had poor membrane permeability and liabilities that complicated optimization. Here, we developed a shape-based virtual screening pipeline—FrankenROCS. We screened the Enamine high-throughput collection of 2.1 million compounds, selecting 39 compounds for testing, with the most potent binding with a 130 μM median inhibitory concentration (IC 50 ). We then paired FrankenROCS with an active learning algorithm (Thompson sampling) to efficiently search the Enamine REAL database of 22 billion molecules, testing 32 compounds with the most potent binding with a 220 μM IC 50 . Further optimization led to analogs with IC 50 values better than 10 μM. This lead series has improved membrane permeability and is poised for optimization. FrankenROCS is a scalable method for fragment linking to exploit synthesis-on-demand libraries.

Science & Technology - Other Topics↗

DOE OSTI2025

Biosensor-driven strain engineering reveals key cellular processes for maximizing isoprenol production in Pseudomonas putida

Synthetic biology generates vast combinatorial designs, yet high-throughput analytical methods to screen them are poorly matched to interrogate this search space. We address this challenge by developing a biosensor-driven, growth-coupled selection strategy in Pseudomonas putida for isoprenol, a potential aviation fuel precursor. We found and characterized a noncanonical signaling pathway, revealing a functional and physical complex between a hybrid histidine kinase and an alcohol dehydrogenase, whose activity is tuned by heterodimerization. Leveraging this biosensor in a pooled CRISPRi library selection, we identified key host limitations. Iterative combinatorial strain engineering derived from these hits yielded a 36-fold titer increase to ~900 milligrams per liter. Integrated omics analysis revealed that metabolic rewiring toward amino acid catabolism was crucial for this improvement. This observation was found to be beneficial by technoeconomic analysis. Our modular workflow provides a powerful strategy for optimizing complex heterologous pathways and uncovering emergent host biology.

CRISPRi↗

NASA NTRSDate not supplied

Neurovascular Outcomes of Ionizing Radiation in Human Blood-Brain Barrier Models

One of the main health risks in human deep space exploration is central nervous system (CNS) damage by ionizing radiation due to exposure to galactic cosmic rays (GCRs). In animal models, irradiation with simulated GCRs or their components has been shown to cause neurodegeneration and neuroinflammation associated with cognitive and behavioral dysfunction. The extent of CNS damage is partially mediated by the blood-brain barrier (BBB), which regulates the interaction between CNS and systemic responses to stressors in the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal death, neuroinflammation and oxidative stress. However, studies on BBB and astrocyte functions in regulating CNS responses to ionizing radiation have been limited, especially in human tissue/organ analogs. Therefore, we developed a high throughput 3D human neurovascular system model, based on commercially available Mimetas OrganoPlates seeded by primary human cells, to investigate the neurovascular responses to simulated deep space radiation. Using this system, we have demonstrated that 600MeV/n 56Fe irradiation leads to cellular damage and increased blood-brain barrier permeability via dysfunction of brain cells called astrocytes, which appear to be the weakest link and therefore a highly suitable countermeasure target to reduce the impact of space radiation on the blood-brain barrier. We anticipate that our results form merely the first step in ongoing development of organ models, including their adaptation for personalized risk assessment, high throughput approaches to countermeasure screening and validation, and eventual payload adaptation.

organ-on-a-chip↗

NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗

NASA NTRSDate not supplied

Interpretable ML Approaches for Novel Solid State Electrolyte Design

Shreyas J Honrao↗

DOE OSTI2024

Characterization of compositional variation and solidification condition on rapid solidification behavior of beta stabilized titanium

Rapid solidification (RS) is a field of study that has increased in importance due to the rise of additive manufacturing (AM) techniques. The breakdown of equilibrium at the solid-liquid interface results in an inability to use traditional solidification models that have been applied to great success in casting. As such, there is a current need for a high-throughput testing method to screen alloys at rapid solidification conditions without the significant cost for powder atomization. In this work, twin hammer splat quenching (SQ) is utilized and theoretically developed as a technique uniquely placed to test alloy systems for AM applications. Using analytical thermal spray models and numerical heat transfer simulations, the cooling rate and solidification conditions of splat quenching are compared across several parameters to evaluate the key parameters for generating cooling rates relevant to AM process. Once a framework for the cooling rates and relevancy of SQ are developed, SQ was used to process alloy compositions in the Ti5553 alloy space (AMS 7026) to study the formation of detrimental secondary phases occurring upon rapid quenching from the melt. A martensitic phase is found to occur at the lower end of the standard, but the composition listed in the standard were found to be safe. Finally, the rapid solidification behavior of Ti-5553 is evaluated by comparing splat quenching, laser welding, and plasma atomization that offer a wide window of solidification conditions. The segregation and solidification structure are analyzed and compared to models to provide a deeper understanding of the solidification behavior of beta stabilized titanium alloys.

Williamson, Christopher J.↗

DOE OSTI2020

Use of a segmented cell for the combinatorial development of platinum group metal-free electrodes for polymer electrolyte fuel cells

The development of novel platinum group metal (PGM)-free catalysts is a difficult task, particularly when coupled with the integration into a cathode catalyst layer (CCL). The optimization of such PGM-free electrode structures is often non-linear and iterative, making it a demanding task due to the high number of parameters that affect performance. To accelerate both materials discovery and electrode development, this work demonstrates the application of a high-resolution segmented cell (SC) with 121 segments of 0.413 cm 2 size for combinatorial high-throughput PGM-free catalyst screening and CCL optimization. The approach utilizes three flow-field strips with active areas of 4.45 cm 2 each, distributed over 11 segments. Electrodes with identical catalyst material and fabrication method result in reproducible data with typically less than 10% variation between each segment and match in performance with differential single cell data. High throughput testing of combinatorial sample sets, with varied PGM-free electrocatalyst materials or electrode composition, demonstrated the ability to rapidly discern high performing outliers, and establish or confirm trends in electrode optimization. The results indicated significant performance benefits of electrodes with ionomer content of 45 wt% over lower values, and of water rich inks with 82% water content over those with 50%.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2026

Multiphasic droplet microfluidics platform for controlled bacteria and mammalian cell co-culture

Microfluidics has revolutionized high-throughput miniaturized biological assays. However, co-culture of mammalian cells and bacteria remains challenging in microfluidic systems due to incompatible growth requirements, limited spatial control, and the requirement for a mammalian cell adhesion matrix. Here, we present a microfluidic platform that generates multiphasic droplets which encapsulate mammalian and bacterial cells, enabling their direct and indirect co-culture. By combining photopolymerizable hydrogels with polymer phase separation, we generate core–shell droplets composed of a liquid and a hydrogel compartment. The hydrogel compartment supports mammalian cell adhesion and culture, while the liquid compartment sustains bacterial growth. We demonstrate two droplet architectures that allow physical bacteria–mammalian cell contacts or enforce complete physical separation, representing direct and indirect co-culture. Our multiphasic droplets are stable, customizable, able to sustain co-culture for over 24 hours, and compatible with fluorescence-based cell sorting technologies. Overall, our multiphasic droplet microfluidic platform provides a scalable and versatile tool for high-throughput co-culture and screening of host–microbe interactions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Comparing Transcriptomic Points of Departure to Apical Effect Concentrations For Larval Fathead Minnow Exposed to Chemicals with Four Different Modes Of Action

It is postulated that below a transcriptomic-based point of departure, adverse effects are unlikely to occur, thereby providing a chemical concentration to use in screening level hazard assessment. The present study extends previous work describing a high-throughput fathead minnow assay that can provide full transcriptomic data after exposure to a test chemical. One-day post-hatch fathead minnows were exposed to ten concentrations of three representatives of four chemical modes of action: organophosphates, ecdysone receptor agonists, plant photosystem II inhibitors, and estrogen receptor agonists for 24 h. Concentration response modeling was performed on whole body gene expression data from each exposure, using measured chemical concentrations when available. Transcriptomic points of departure in larval fathead minnow were lower than apical effect concentrations across fish species but not always lower than toxic effect concentrations in other aquatic taxa like crustaceans and insects. The point of departure was highly dependent on measured chemical concentration which were often lower than the nominal concentration. Differentially expressed genes between chemicals within modes of action were compared and often showed statistically significant overlap. In addition, reproducibility between identical exposures using a positive control chemical (CuSO 4 ) and variability associated with the transcriptomic point of departure using in silico sampling were considered. Results extend a transcriptomic-compatible fathead minnow high-throughput assay for possible use in ecological hazard screening.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a “one-pot” process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

09 BIOMASS FUELS↗

DOE OSTI2024

Salt Hydrate Eutectic Thermal Energy Storage for Building Thermal Regulation (Final Technical Report)

Thermal energy storage is anticipated to play an important role in developing the power grid of the future - a power grid that meets increasing demands of users, is resistant to disruptions, but also allows for greater penetration of renewable resources. Specifically, thermal energy storage materials can be integrated into HVAC systems and building envelopes, where they can be used to shift power demands for building climate control from periods of peak demand to periods of low demand. Phase change materials (PCMs) are compelling as low-cost, high energy density thermal energy storage materials for building thermal management. However, there is a lack of high performance low-cost PCMs within the specific temperature ranges which would most effectively allow for power load shifting. Inorganic salt hydrates represent a promising class of PCMs, but their inherent limitations cause them to be currently unavailable for reliable building applications. The overarching goals of this research effort are to: 1) Discover low-cost, high volumetric density salt hydrate eutectic PCMs to store low-quality heat (10 to 40 °C); 2) Introduce a high thermal conductivity matrix to reduce the time constant for energy storage to ~0.1 to 1 hr, incorporate nucleation catalysts to decrease undercooling, and utilize microencapsulation and shape stabilization approaches, minimizing moisture loss/gain, mitigating phase separation, and maintaining stable melting behavior over the lifetime of the compounds; 3) Evaluate the impact these systems have on peak load shifting, and the potential for overall energy savings under different climatic scenarios and building configurations. These goals will be achieved by an integrated research program consisting of six cohesive research subtasks: 1) Materials discovery of eutectic salt hydrate PCMs by using computationally predicted thermodynamic equilibria, coupled with high-throughput experimental validation, 2) Rapid experimental screening of nucleation catalysts identified through robust computational databases, 3) Embedding salt hydrate PCM into a low cost and scalable high conductivity matrix, 4) Microencapsulation of salt hydrate microspheres using hybrid inorganic-polymer microencapsulation approach, 5) Shape stabilization by thermoreversible salt hydrate salogels, and 6) Analysis of end-use using thermal simulations, and characterization of mock-up energy storage finished components.

25 ENERGY STORAGE↗

DOE OSTI2024

Rapid Design of Cost-Effective Refractory High Entropy Alloys Strengthened by Precipitation

Funded by the ARPA-E ULTIMATE program, this project aims to develop cost-effective refractory high entropy alloys (RHEAs) for gas turbine blades applications at 1300 degree Celsius and above. The alloy design focuses on carbides precipitation strengthening in a strong and ductile solid solution BCC matrix. The goal is to achieve balanced mechanical properties including room temperature ductility & fracture toughness and high temperature strength and creep resistance, while maintaining comparable oxidation resistance and densities to Ni-base superalloys. To accelerate alloy design, multiscale modeling including CALPHAD and first-principles density functional theory (DFT) calculations and machine learning are performed. Specifically, high throughput phase diagram calculations and screening are carried out using CALPHAD; DFT calculations are performed to predict intrinsic ductility, grain boundary strengthening potential, coefficient of thermal expansion (CTE) and temperature-dependent elastic constants. Temperature dependent yield stress, creep strain and CTE are also predicted using machine learning. Down-selected alloys are synthesized in small buttons of about 250g using arc melting for rapid evaluation on microstructure and mechanical properties before employing plasma arc melting and additive manufacturing for producing large ingots. Preliminary computational and experimental results of this project will be presented and future direction in RHEA development will be discussed.

high temperature materials↗

DOE OSTI2025

Enhancing Post-Irradiation Examination (PIE) Efficiency with High-Throughput Experiments

Nuclear materials testing is essential for understanding changes in reactor environments, but traditional methods are costly and time-consuming. High-Throughput Experimentation (HTE) enables rapid screening and optimization of material properties by significantly increasing the number of specimens in the experiment. In this work, HTE methods were employed to irradiate novel materials at the Advanced Test Reactor (ATR) at Idaho National Laboratory. A total of 367 sub-sized tensile specimens were irradiated using the standard capsule design. To facilitate HTE, automated systems such as the Automated Mechanical Testing System were implemented for mechanical testing within hot cell environments, thereby reducing the need for manual interaction with hot cell manipulators and significantly lowering both the cost and duration of experiments. Digital image correlation (DIC) was used to measure displacement during mechanical testing, providing precise, non-contact measurement capabilities. The methodologies developed in this research will support the advancement of HTE techniques for future nuclear materials testing experiments at ATR. This approach allows for more efficient and cost-effective evaluation of new materials, ultimately leading to improved reactor performance, enhanced safety, and extended longevity within the nuclear industry.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Multi-omics Resources for Understanding Gene Regulation in Response to ER Stress in Plants

Proteotoxic stress of the endoplasmic reticulum (ER) is a potentially lethal condition that ensues when the biosynthetic capacity of the ER is overwhelmed. A sophisticated and largely conserved signaling, known as the unfolded protein response (UPR), is designed to monitor and alleviate ER stress. In plants, the emerging picture of gene regulation by the UPR now appears to be more complex than ever before, requiring multi-omics-enabled network-level approaches to be untangled. In the past decade, with an increasing access and decreasing costs of next-generation sequencing (NGS) and high-throughput protein–DNA interaction (PDI) screening technologies, multitudes of global molecular measurements, known as omics, have been generated and analyzed by the research community to investigate the complex gene regulation of plant UPR. In this chapter, we present a comprehensive catalog of omics resources at different molecular levels (transcriptomes, protein–DNA interactomes, and networks) along with the introduction of key concepts in experimental and computational tools in data generation and analyses. Finally, this chapter will serve as a starting point for both experimentalists and bioinformaticians to explore diverse omics datasets for their biological questions in the plant UPR, with likely applications also in other species for conserved mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗

DOE OSTI2025

Uncovering hidden enhancers through unbiased in vivo testing

Chromatin signatures are widely used to identify tissue-specific in vivo enhancers, but their sensitivity and specificity remains unclear. Here we show that many developmental enhancers remain undetectable using currently available chromatin data. In an initial comparison of over 1200 developmental enhancers with tissue-matched chromatin data, 14% (n = 285) lacked canonical enhancer-associated chromatin signatures. To further assess the prevalence of enhancers missed by chromatin profiling approaches, we used a high-throughput transgenic enhancer assay to screen the regulatory landscapes of two key developmental genes at 5 kb resolution, spanning 1.3 Mb of mouse sequence in total. We observed that 23 of 88 (26%) in vivo enhancers discovered by this approach lacked enhancer-associated chromatin signatures in the respective tissue. Our findings suggest the existence of tens of thousands of enhancers that remain undiscovered by currently available chromatin data, underscoring the continued need for expanding resources for enhancer discovery.

Epigenomics↗

DOE OSTI2023

High-throughput identification of spin-photon interfaces in silicon

Color centers in host semiconductors are prime candidates as spin-photon interfaces for quantum applications. Finding an optimal spin-photon interface in silicon would move quantum information technologies toward a mature semiconducting host. However, the space of possible charged defects is vast, making the identification of candidates from experiments alone extremely challenging. Here, we use high-throughput first-principles computational screening to identify spin-photon interfaces among more than 1000 charged defects in silicon. The use of a single-shot hybrid functional approach is critical in enabling the screening of many quantum defects with a reasonable accuracy. We identify three promising spin-photon interfaces as potential bright emitters in the telecom band: Ti$^+_i$, Fe$^0_i$, and Ru$^0_i$. These candidates are excited through defect-bound excitons, stressing the importance of such defects in silicon for telecom band operations. Our work paves the way to further large-scale computational screening for quantum defects in semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Process-Microstructure-Property Relationships in Low Heat Input Wire-Arc Additive Manufacturing (WAAM) of Ni-Based Superalloy Haynes 282

Conference presentation on WAAM process optimization on Ni-based superalloy Haynes® 282®. This alloy is targeted for advanced power generation systems for its superior high temperature mechanical properties. Wire-arc additive manufacturing (WAAM) offers attractive cost and materials savings with high deposition rates. Based on initial build process optimization, WAAM blocks were made, and screened for defects using high-throughput CT scanning. The microstructural evolution in arc energy range of 250-700 J/mm was characterized in the as-built and aged conditions, using electron microscopy. Results showed self-consistent microstructure across conditions, with grain size dependence on arc energy, MC and M6C carbides in as-built, and blocky grain boundary M23C6 in as-heat-treated condition. Further reductions in as-built porosity were explored through 20+ combinations of H2 and CO2 additions to shielding gas. Initial results show improved wetting behavior, and bead overlap with up to 1% H2 and up to 0.25% CO2. Combination of experimentally determined datasets provided comprehensive understanding of process-structure-property relationships in WAAM Haynes 282.

Haynes 282↗