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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Discovery of a Highly Selective Glycogen Synthase Kinase‐3 Inhibitor (PF‐04802367) That Modulates Tau Phosphorylation in the Brain: Translation for PET Neuroimaging

Abstract Glycogen synthase kinase‐3 (GSK‐3) regulates multiple cellular processes in diabetes, oncology, and neurology. N ‐(3‐(1H‐1,2,4‐triazol‐1‐yl)propyl)‐5‐(3‐chloro‐4‐methoxyphenyl)oxazole‐4‐carboxamide (PF‐04802367 or PF‐367) has been identified as a highly potent inhibitor, which is among the most selective antagonists of GSK‐3 to date. Its efficacy was demonstrated in modulation of tau phosphorylation in vitro and in vivo. Whereas the kinetics of PF‐367 binding in brain tissues are too fast for an effective therapeutic agent, the pharmacokinetic profile of PF‐367 is ideal for discovery of radiopharmaceuticals for GSK‐3 in the central nervous system. A 11 C‐isotopologue of PF‐367 was synthesized and preliminary PET imaging studies in non‐human primates confirmed that we have overcome the two major obstacles for imaging GSK‐3, namely, reasonable brain permeability and displaceable binding.

Liang, Steven H.↗
DOE OSTI2011

High-Throughput In Vitro Glycoside Hydrolase (HIGH) Screening for Enzyme Discovery

A high-throughput protein-expression and screening method (HIGH method, see picture) provides a rapid approach to the discovery of active glycoside hydrolases in environmental samples. Finally, HIGH screening combines cloning, protein expression, and enzyme hydrolysis in one pot; thus, the entire process from gene expression to activity detection requires only three hours.

Kim, Tae-Wan↗
DOE OSTI2023

A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

Abstract We present a new framework integrating the AI model GPT‐4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT‐4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT‐4 in various capacities, wherein GPT‐4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in‐context learning of AI in the next iteration. This iterative human‐AI interaction enabled GPT‐4 to learn from the outcomes, much like an experienced chemist, by a prompt‐learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT‐4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine‐tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT‐4 to enhance the feasibility and efficiency of research activities.

Chemistry↗
DOE OSTI2015

Discovery of a new blue quasar: SDSS J022218.03-062511.1

We report the discovery of a bright blue quasar: SDSS J022218.03–062511.1. This object was discovered spectroscopically while searching for hot white dwarfs that may be used as calibration sources for large sky surveys such as the Dark Energy Survey or the Large Synoptic Survey Telescope project. In addition, we present the calibrated spectrum, spectral line shifts and report a redshift of z = 0.521 ± 0.0015 and a rest-frame g-band luminosity of 8.71 × 10 11 L ⊙ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Discovery of a new quasar: SDSS J022155.26-064916.6

Here, we report the discovery of a new quasar: SDSS J022155.26-064916.6. This object was discovered while reducing the spectra of a sample of stars being considered as spectrophotometric standards for the Dark Energy Survey. The flux- and wavelength-calibrated spectrum is presented with four spectral lines identified. From these lines, the redshift is determined to be z≈0.806. In addition, the rest-frame u- , g- , and r -band luminosity, determined using a k-correction obtained with synthetic photometry of a proxy quasi stellar object (QSO), are reported as 7.496×10 13 L ⊙ , 2.049×10 13 L ⊙ , and 1.896×10 13 L ⊙ , respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Anaerobic gut fungi: Advances in isolation, culture, and cellulolytic enzyme discovery for biofuel production

Anaerobic gut fungi are an early branching family of fungi that are commonly found in the digestive tract of ruminants and monogastric herbivores. It is becoming increasingly clear that they are the primary colonizers of ingested plant biomass, and that they significantly contribute to the decomposition of plant biomass into fermentable sugars. As such, anaerobic fungi harbor a rich reservoir of undiscovered cellulolytic enzymes and enzyme complexes that can potentially transform the conversion of lignocellulose into bioenergy products. Despite their unique evolutionary history and cellulolytic activity, few species have been isolated and studied in great detail. As a result, their life cycle, cellular physiology, genetics, and cellulolytic metabolism remain poorly understood compared to aerobic fungi. To help address this limitation, this work briefly summarizes the current body of knowledge pertaining to anaerobic fungal biology, and describes progress made in the isolation, cultivation, molecular characterization, and long-term preservation of these microbes. Finally, we also discuss recent cellulase- and cellulosome-discovery efforts from gut fungi, and how these interesting, non-model microbes could be further adapted for biotechnology applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Novel color center platforms enabling fundamental scientific discovery

Abstract Color centers are versatile systems that generate quantum light, sense magnetic fields and produce spin‐photon entanglement. We review how these properties have pushed the limits of fundamental knowledge in a variety of scientific disciplines, from rejecting local‐realistic theories to sensing superconducting phase transitions. In the light of recent progress in material processing and device fabrication, we identify new opportunities for interdisciplinary fundamental discoveries in physics and geochemistry. image

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Revolutionizing Energetic Materials Discovery and Design: The Role of Data Science and Machine Learning

Here this Special Issue of Propellants, Explosives, Pyrotechnics (PEP) is focused on energetic materials discovery and design using Data Science and Machine Learning (DS&ML). The application of DS&ML has proven to be transformative in many areas, where it has been shown to expedite analysis, enable extraction of greater quantities of information from datasets, and guide experiments. However, energetic materials and their applications present unique challenges that often hinder the use of standardized tools and practices. In spite of these challenges, important and compelling advancements are being made toward data-directed research in energetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗
DOE OSTI2024

New Directions for Thermoelectrics: A Roadmap from High‐Throughput Materials Discovery to Advanced Device Manufacturing

Thermoelectric materials, which can convert waste heat into electricity or act as solid‐state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high‐throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high‐throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed‐loop process to generate and analyze broad datasets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to realize scalable, low‐cost, and energy‐efficient fabrication of thermoelectric devices. This review provides an overview of recent advances in discovering thermoelectric materials using high‐throughput methods, including processing, characterization, and screening. Advanced manufacturing methods of thermoelectric devices are also introduced to realize the broad impacts of thermoelectric materials in power generation and solid‐state cooling. In the end, this article also discusses the future research prospects and directions.

Song, Kaidong↗
DOE OSTI2011

Discovery of single top quark production

The top quark is by far the heaviest known fundamental partic le with a mass nearing that of a gold atom. Because of this strikingly high mass, the top qu ark has several unique properties and might play an important role in electroweak s ymmetry breaking—the mech- anism that gives all elementary particles mass. Creating top quarks requires access to very high energy collisions, and at present only the Tevatron col lider at Fermilab is capable of reaching these energies. Until now, top quarks have only been observed produced in pai rs via the strong interac- tion. At hadron colliders, it should also be possible to prod uce single top quarks via the electroweak interaction. Studies of single top quark produ ction provide opportunities to measure the top quark spin, how top quarks mix with other quar ks, and to look for new physics beyond the standard model. Because of these interest ing properties, scientists have been looking for single top quarks for more than 15 years. This thesis presents the first discovery of single top quark p roduction. An analysis is per- formed using 2.3 fb − 1 of data recorded by the DØ detector at the Fermilab Tevatron Co l- lider at centre-of-mass energy √ s = 1 . 96 TeV. Boosted decision trees are used to isolate the single top signal from background, and the single top cro ss section is measured to be σ ( p ̄ p → tb + X, tqb + X ) = 3 . 74 +0 . 95 − 0 . 74 pb . Using the same analysis, a measurement of the amplitude of th e CKM matrix element V tb , governing how top and b quarks mix, is also performed. The measurement yields: | V tb f L 1 | = 1 . 05 +0 . 13 − 0 . 12 , where f L 1 is the left-handed W tb coupling. The separation of signal from background is improved by combining the boosted decisi on trees with two other mul- tivariate techniques. A new cross section measurement is pe rformed, and the significance for the excess over the predicted background exceeds 5 stand ard deviations

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Enhancing the discovery prospects for SUSY-like decays with a forgotten kinematic variable

The lack of a new physics signal thus far at the Large Hadron Collider motivates us to consider how to look for challenging final states, with large Standard Model backgrounds and subtle kinematic features, such as cascade decays with compressed spectra. Adopting a benchmark SUSY-like decay topology with a four-body final state proceeding through a sequence of two-body decays via intermediate resonances, we focus our attention on the kinematic variable Δ 4 which previously has been used to parameterize the boundary of the allowed four-body phase space. We highlight the advantages of using Δ 4 as a discovery variable, and present an analysis suggesting that the pairing of Δ 4 with another invariant mass variable leads to a significant improvement over more conventional variable choices and techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

The Abrikosov Vortex Lattice: Its Discovery and Impact

Following some prehistory, we discuss the steps leading to the Abrikosov vortex lattice and type-II superconductivity. Dissipation due to flux flow and flux creep represented an important discovery and leads to the strong interest in magnetic flux pinning. High-temperature superconductors and pancake vortices started the new concepts of vortex matter. Nonlinear effects at high vortex velocities, flux-flow instabilities, and some resulting open questions are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Discovery of the canonical Calvin–Benson cycle

It has been 65 years since the Calvin-Benson cycle was first formulated. In this paper the development of the concepts that are critical to the cycle are traced and the contributions of Calvin, Benson, and Bassham are discussed. Some simplified views often found in text books such as ascending paper chromatography and the use of the “lollipop” for short labeling are discussed and further details given. Key discoveries that underpinned elucidation of the cycle such as the importance of sedoheptulose phosphate and ribulose 1,5-bisphosphate are described. The interchange of ideas between other researchers working on what is now called the pentose phosphate pathway and the development of the ideas of Calvin and Benson are explored while the gluconeogenic aspects of the cycle are emphasized. Concerns raised about anomalies of label distribution in glucose are considered. Here, other carbon metabolism pathways associated with the Calvin-Benson cycle are also described. Finally, there is a section describing the rift between Calvin and Benson.

rubisco↗
DOE OSTI2023

Data-driven discovery of a formation prediction rule on high-entropy ceramics

The interest in high entropy ceramics (HECs) has increased steadily due to their superior properties. However, the prediction of their formation still poses challenges for the discovery of new systems. Here, we discover a rational rule for designing single-phase high entropy metal diborides (HEBs) using data-driven approach. The machine learning (ML) model is trained on data collected via high-throughput experiments (HTEs). K nearest neighbor (KNN) model shows an experimental validation accuracy of 93.75%. By implementing interpretable ML method, we demonstrate that a mismatch of the bonds between boron and transition metals (δ B-TM ) dominates the formation of HEBs. We propose an empirical rule that HEBs favor forming a single phase when δ B-TM < 3.66; otherwise, multiphase. The rule has a high accuracy of 93.33% for new HEBs predictions. In addition, we contribute 165 high quality HEBs data in total, which can promote the development of materials informatics in HEBs. Furthermore, this data-driven strategy can be expanded to accelerate the search for new HECs, paving a pathway to design novel HECs with superior properties rapidly.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Discovery of the arsenic isotopes

Twenty-nine arsenic isotopes have been observed so far and the discovery of these isotopes is discussed here. For each isotope a brief summary of the first refereed publication, including the production and identification method, is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗