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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

Antibodies↗
DOE OSTI2025

Low-Cost Screening of Algae for Extreme Tolerance to pH, Temperature, Salinity, and Light

AbstractBioprospecting algae strains with tolerance to extreme conditions such as pH, temperature, salinity, and light is crucial for advancing biotechnology and environmental applications. However, traditional screening methods often involve significant costs and labor, restricting their accessibility and practical use. In this study, we developed and validated low-cost, high-throughput screening techniques, predominantly employing agar plates and liquid culture assays, to effectively differentiate tolerance levels among various algae strains. The methodologies were optimized using the model microalgaChlamydomonas reinhardtiiand its closely related speciesChlamydomonas incertaand the recently discovered extremophilicChlamydomonas pacifica. We systematically evaluated the algae for tolerance to extremes by establishing precise gradients of pH (acidic to alkaline conditions), salinity (0 to 5 M NaCl), temperature (34–42°C), and light intensity (40 to 2977 μE·m⁻²·s⁻¹). Our results demonstrated that these cost-effective, agar plate-based methods effectively distinguished algae strains exhibiting superior tolerance to extreme environmental conditions. These screening techniques not only provided clear differentiation among the closely related strains but also delivered reproducible outcomes suitable for scaling up to larger bioprospecting efforts. Furthermore, the affordability and simplicity of these methods facilitate their implementation in resource-limited laboratories, thereby broadening participation in algae bioprospecting endeavors. This study highlights the potential of low-cost, accessible screening techniques to significantly enhance the discovery and characterization of algal strains with extreme traits. Ultimately, these methods support the development of robust algae-based resources, driving innovation in diverse industrial processes and environmental solutions.Graphical Abstract

Saucedo, Barbara (ORCID:0009000008860839)↗
DOE OSTI2020

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

We report that computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Advanced Algal Biofoundries for the Production of Polyurethane Precursors

The primary goal of the BEEPs project was to develop a process that could accelerate the development of algae as bioproduction platforms, from initial chemical product concept to an economically viable market supply. Under this program we elected to develop strains of algae that could generate polyurethane precursors, while simultaneously developing basic genetic tools to enable improved algal production systems. This program was specifically designed to incorporate National Laboratories as a means to utilize the expertise and facilities for new bio-production platforms. To that end, we designed a program to collaborate with the Agile BioFoundry at Lawrence Berkeley National Laboratory (LBNL) and computational platforms at Pacific Northwest National Laboratory (PNNL). In addition to these National Laboratory partners, we also had academic partners from UC Davis and Georgia Tech, as well as commercial partners Algenesis Materials and BASF. To achieve these goals, we initially focused on developing the genetic tools and high throughput screening technologies necessary to generate and assess production of polymer precursors (succinic acid) in algae and cyanobacteria, including advanced promoters and biosensors. In parallel, we computationally identified potential production bottlenecks and then used the developed genetic tools to increase production rates and yields. Constant feedback of data was used in conjunction with machine learning, high-throughput cell sorting, and synthetic biology, for additional targeted metabolic engineering. Multiple rounds of tool design, building, testing, and learning were supplied to partners at PNNL and LBNL to develop new models and tools that could expedite bioproduction platform development and increase yield performance. We targeted, and achieved, the FOA requirement yield metric of 20 g/L, as a milestone and deliverable from at least one of our engineered strains for the production of succinic acid.

09 BIOMASS FUELS↗
DOE OSTI2025

A high throughput assay to detect enzymatic polyethylene oxidation

Biological plastics deconstruction and upcycling have emerged as sustainable alternatives to traditional recycling technologies for plastics waste. The discovery and engineering of efficient thermostable poly(ethylene terephthalate) (PET) hydrolases have made biological PET recycling possible at scale; however, enzymes for non-PET plastics, which account for approximately 70% of all plastics produced, remain largely undiscovered. To accelerate the discovery of such enzymes, we develop a high-throughput screen to detect initial polymer oxidation, specifically that of the C-H bond to an aldehyde. We test 4-hydrazino-7-nitro-2,1,3-benxoxadiozole hydrazine (NBD-H), which reacts with generated aldehydes to form a fluorescent hydrazone on plasma oxidized low-density polyethylene (LDPE) films. Hydrazone generation correlated well with the area of aldehyde peaks as measured by Fourier Transform Infrared Spectroscopy (FTIR) (R2 = 0.92). Moreover, we demonstrate that the probe reliably identifies LDPE-active dye decolorizing peroxidases (DyPs) that generate aldehydes on LDPE films (1.7 – 3.0 fold change relative to background), serving as an effective screen as demonstrated by receiver operating characteristic area under the curve of 0.95. Furthermore, this assay offers an LDPE oxidation screening platform that can be readily parallelized and automated for accelerated discovery of enzymes involved in polyolefin deconstruction.

biocatalysis↗
DOE OSTI2024

Engineered Microorganisms for Enhanced Rare Earth Element Bio-mining and Separations (Final Technical Report)

Rare earth elements (REE) are critical ingredients of sustainable energy technologies, but their extraction from ore and separation from one another pose formidable challenges. To solve the challenge of REE supply, we used advanced genomics, high-throughput screening with synthetic REE minerals, and synthetic biology to engineer two sets of exotic microbes to (1) extract REE from ores, spent cracking catalysts, coal ash and electronic waste with high efficiency and selectivity, and (2) to purify REE into single element batches, all under benign conditions without the need of harsh solvents and high temperatures. This work integrated our expertise in systems and synthetic biology (Buz Barstow); rare-earth geochemistry (Esteban Gazel) and mineral synthesis (Megan Holycross); and microsystems engineering (Mingming Wu) by first elucidating the set of rules that predict an organism’s phenotype and then applying them to solve this critical problem in sustainable energy. These new technologies could help to revitalize the US rare earth industry and provide a new source of these critical elements for future energy technologies. We have already had some big success in tech transfer. Two of our team members (postdoctoral fellow Alexa Schmitz and graduate student Sean Medin) were able to study the supply chain for REE in the United States, and identify an opportunity to commercialize our REE mineral-dissolution technology. Alexa and Sean recently founded REEgen, Inc., an REE biomining company. Dr. Schmitz was recently awarded a fellowship from the Activate Foundation to support the first two years of REEgen. Cornell showed its support for this technology and company, and Dr. Schmitz was awarded the Rising Women Innovator’s award. These two awards unlocked support from Cornell’s Praxis Incubator.

58 GEOSCIENCES↗
DOE OSTI2026

Finding the perfect imperfection: Accelerated, computationally driven discovery and design of quantum defects

Optically addressable spin defects have emerged as the leading platforms for quantum sensing and communication in solid-state systems. While traditional efforts have concentrated on a focused set of well-studied defects, recent advances in high-throughput computational methods have shown promise for large-scale exploration of defects across diverse semiconductor hosts. By cataloging key properties of quantum defects in computational databases, high-throughput screening techniques can systematically suggest and design novel candidates. In this article, we highlight recent advances in data-driven quantum defect design aimed at addressing critical materials science challenges such as host materials selection, defect stability, and desirable electronic and optical properties. Here, we emphasize the importance of electronic-structure-guided searches across various materials and illustrate how high-throughput computations contribute to our understanding of design principles for quantum defects. Additionally, we outline ongoing challenges and emerging opportunities in this rapidly developing field.

Xiong, Yihuang [Dartmouth College, Hanover, NH (Un↗
DOE OSTI2022

Quantification of Metabolic Products from Microbial Hosts in Complex Media Using Optically Diffracting Hydrogels

In this work, we herein describe a highly versatile platform approach for the in situ and real-time screening of microbial biocatalysts for enhanced production of bioproducts using photonic crystal hydrogels. This approach was demonstrated by preparing optically diffracting films based on polymerized N-isopropylacrylamide that contracted in the presence of alcohols and organic acids. The hydrogel films were prepared in a microwell plate format, which allows for high-throughput screening, and characterized optically using a microwell plate reader. While demonstrating the ability to detect a broad range of relevant alcohols and organic acids, we showed that the response of the films correlated strongly with the octanol-water partition coefficient (log P) of the analyte. Differences in the secretion of ethanol and succinic acid from strains of Zymomonas mobilis and Actinobacillus succinogenes, respectively, were further detected via optical characterization of the films. These differences, which in some cases were as low as ~3 g/L, were confirmed by high-performance liquid chromatography, thereby demonstrating the sensitivity of this approach. Our findings highlight the potential utility of this multiplexed approach for the detection of small organic analytes in complex biological media, which overcomes a major challenge in conventional optical sensing methods.

09 BIOMASS FUELS↗
DOE OSTI2022

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ~40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ~3.7 billion candidate molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗
DOE OSTI2022

First Fluorescent Acetylspermidine Deacetylation Assay for HDAC10 Identifies Selective Inhibitors with Cellular Target Engagement**

Abstract Histone deacetylases (HDACs) are important epigenetic regulators involved in many diseases, especially cancer. Five HDAC inhibitors have been approved for anticancer therapy and many are in clinical trials. Among the 11 zinc‐dependent HDACs, HDAC10 has received relatively little attention by drug discovery campaigns, despite its involvement, e. g., in the pathogenesis of neuroblastoma. This is due in part to a lack of robust enzymatic conversion assays. In contrast to the protein lysine deacetylase and deacylase activity of most other HDAC subtypes, it has recently been shown that HDAC10 has strong preferences for deacetylation of oligoamine substrates like acetyl‐putrescine or ‐spermidine. Hence, it is also termed a polyamine deacetylase (PDAC). Here, we present the first fluorescent enzymatic conversion assay for HDAC10 using an aminocoumarin‐labelled acetyl‐spermidine derivative to measure its PDAC activity, which is suitable for high‐throughput screening. Using this assay, we identified potent inhibitors of HDAC10‐mediated spermidine deacetylation in vitro. Based on the oligoamine preference of HDAC10, we also designed inhibitors with a basic moiety in appropriate distance to the zinc binding hydroxamate that showed potent inhibition of HDAC10 with high selectivity, and we solved a HDAC10‐inhibitor structure using X‐ray crystallography. We could demonstrate selective cellular target engagement for HDAC10 but a lysosomal phenotype in neuroblastoma cells that was previously associated with HDAC10 inhibition was not observed. Thus, we have developed new chemical probes for HDAC10 that allow further clarification of the biological role of this enzyme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Exploring the Origin of Anionic Redox Activity in Super Li-Rich Iron Oxide-Based High-Energy-Density Cathode Materials

Super alkali-rich materials (alkali:transition metal ratio of ≥2:1), such as Li 5 FeO 4 , exhibit the potential to realize anionic redox upon deep delithiation. Li 5 FeO 4 was shown to undergo reversible cycling between Li 4 FeO 3.5 and Li 3 FeO 3.5 with combined cation/anion redox, a remarkable capacity of 189 mAh/g, and no O 2 release. However, the impact of phase transformations on the reaction thermodynamics and the correlation between the structural changes and reaction reversibility remain unclear. In this study, we use first-principles calculations to examine the delithiation and (re)lithiation reactions of Li 5 FeO 4 . We show that the experimentally observed charge and discharge processes go through non-equilibrium pathways. Upon delithiation, the compound undergoes a phase transformation from Li 5 FeO 4 , with tetrahedrally coordinated (T d ) Fe ions, to a delithiated disordered rocksalt structure, with octahedral (O h ) Fe ions. Fe-ion migration has an asymmetric kinetic barrier that makes T d → O h migration facile, whereas the reverse process has a much larger barrier, explaining the difficulties in reaction reversibility. We further elucidate the transition metal and O redox sequences during the charge cycle and identify the complex electrochemistry associated with the dual participation of cationic redox (Fe 3+ /Fe 4+ ) and anionic redox (O 2– /O – /O o ). Armed with this knowledge, we conduct high-throughput screening of known alkali-rich transition metal oxides by evaluating their potential to enable reversible anionic redox, with multiple candidates proposed for further experimental trials. Furthermore, our work provides a useful guide for the further development of super alkali-rich anionic-redox-active electrodes for high-energy-density batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

High throughput discovery of enhanced visible photoactivity in Fe–Cr vanadate solar fuels photoanodes

Metal oxide solar absorbers are well suited for photoelectrochemical applications where requisite properties include stability in highly oxidizing environments, in addition to solar energy conversion. Metal vanadates are of particular interest due to their relatively low band gap energies compared to traditional, wide-gap photocatalysts. Concerted efforts on BiVO 4 -based photoanodes have revealed multiple avenues for improving the solar conversion efficiencies for photon energies above 2.5 eV but have not addressed the ultimate performance limitations from the undesirably high band gap energy. Fe and Cr vanadates have a lower band gap and thus a higher potential solar conversion efficiency, although to-date the absorbed 2–2.5 eV photons are not effectively converted to the desired anodic photocurrent. By using combinatorial synthesis and high throughput screening, we demonstrate that cation substitutions with the monoclinic MVO 4 phase (M = Cr, Fe) improves the utilization of photons in this energy range. Given the portfolio of photoanode improvement techniques available, we suggest optimization of (Cr 0.5 Fe 0.5 )VO 4 -based photoanodes as a promising path for enable solar fuel technologies.

14 SOLAR ENERGY↗
DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learned potential for high-throughput phonon calculations of metal—organic frameworks

Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics.

Elena, Alin Marin↗
DOE OSTI2021

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Antibiotic that inhibits trans -translation blocks binding of EF-Tu to tmRNA but not to tRNA

trans-Translation is conserved throughout bacteria and is essential in many species. High-throughput screening identified a tetrazole-based trans-translation inhibitor, KKL-55, that has broad-spectrum antibiotic activity. A biotinylated version of KKL-55 pulled down elongation factor thermo-unstable (EF-Tu) from bacterial lysates. Purified EF-Tu bound KKL-55 in vitro with a K d = 2 µM, confirming a high-affinity interaction. An X-ray crystal structure showed that KKL-55 binds in domain 3 of EF-Tu, and mutation of residues in the binding pocket abolished KKL-55 binding. RNA-binding assays in vitro showed that KKL-55 inhibits binding between EF-Tu and transfer-messenger RNA (tmRNA) but not between EF-Tu and tRNA. These data demonstrate a new mechanism for the inhibition of EF-Tu function and suggest that this specific inhibition of EF-Tu•tmRNA binding is a viable target for antibiotic development.

59 BASIC BIOLOGICAL SCIENCES↗