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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nonresonant Raman Control of Ferroelectric Polarization

Important advances is recently made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light–matter interactions via the imaginary part of the dielectric function through above-bandgap or resonant mode excitation. Nonresonant Raman excitation of coherent modes is experimentally observed and proposed for dynamic material control, but the resulting atomic excursion is limited to perturbative levels. Here, in this study, this challenge is overcome by employing nonresonant ultrashort pulses with low photon energies well below the bandgap. Using mid-infrared pulses, ferroelectric reversal is induced in lithium niobate, and the large-amplitude mode displacements are characterized through femtosecond stimulated Raman scattering and second harmonic generation. This approach, validated by first-principle calculations, defines a novel method for synthesizing hidden phases with unique functional properties and manipulating complex energy landscapes at reduced energy consumption and ultrafast speeds.

Shi, Jiaojian [Stanford Univ., CA (United States);↗
DOE OSTI2020

A High-Rate Aqueous Proton Battery Delivering Power Below -78 °C via an Unfrozen Phosphoric Acid

Lithium-sulfur batteries are attractive for automobile and grid applications due to their high theoretical energy density and the abundance of sulfur. Despite the significant progress in cathode development, lithium metal degradation and the polysulfide shuttle remain two critical challenges in the practical application of Li-S batteries. Development of advanced electrolytes has become a promising strategy to simultaneously suppress lithium dendrite formation and prevent polysulfide dissolution. Here, a new class of concentrated siloxane-based electrolytes, demonstrating significantly improved performance over the widely investigated ether-based electrolytes are reported in terms of stabilizing the sulfur cathode and Li metal anode as well as minimizing flammability. Through a combination of experimental and computational investigation, it is found that siloxane solvents can effectively regulate a hidden solvation-ion-exchange process in the concentrated electrolytes that results from the interactions between cations/anions and solvents. As a result, it could invoke a quasi-solid-solid lithiation and enable reversible Li plating/stripping and robust solid-electrolyte interphase chemistries. The solvation-ion-exchange process in the concentrated electrolytes is a key factor in understanding and designing electrolytes for other high-energy lithium metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
DOE OSTI2020

Does Selectivity of Molecular Catalysts Change with Time? Polymerization Imaged by Single‐Molecule Spectroscopy

Abstract The chemoselectivity of molecular catalysts underpins much of modern synthetic organic chemistry. However, little is known about the selectivity of individual catalysts because this single‐catalyst‐level behavior is hidden by the bulk catalytic behavior. Here, for the first time, the selectivity of individual molecular catalysts for two different reactions is imaged in real time at the single‐catalyst level. This imaging is achieved through fluorescence microscopy paired with spectral probes that produce a snapshot of the instantaneous chemoselectivity of a single catalyst for either a single‐chain‐elongation or a single‐chain‐termination event during ruthenium‐catalyzed polymerization. Superresolution imaging of multiple selectivity events, each at a different single‐molecular ruthenium catalyst, indicates that catalyst selectivity may be unexpectedly spatially and time‐variable.

Garcia, IV, Antonio↗
DOE OSTI2023

Polyethylene Incorporating Diels–Alder Comonomers: A “Trojan Horse” Strategy for Chemically Recyclable Polyolefins

Abstract Polyolefins with periodic unsaturation in the backbone chain are sought after for synthesizing chemically recyclable polymers or telechelic polyolefin macromonomers. Here we introduce a bottom‐up synthesis of unsaturated high‐density polyethylene (HDPE) via copolymerization of ethylene with dimethyl 7‐oxabicyclo[2.2.1]hepta‐2,5‐diene‐3,5‐dicarboxylate followed by post‐polymerization retro‐Diels–Alder to unveil hidden double bonds in the polymer backbone. The incorporation of this “Trojan Horse” comonomer was varied and a series of unsaturated HDPE polymers with block lengths of 1.2, 1.9, and 3.5 kDa between double bonds was synthesized. Cross metathesis of unsaturated HDPE samples with 2‐hydroxyethyl acrylate yielded telechelic ester terminated PE macromonomers suitable for the preparation of ester‐linked PE. These materials were depolymerized and repolymerized, making them suitable candidates for chemical recycling. The ester‐linked PE displayed thermal and mechanical properties comparable to commercial HDPE.

Parke, Sarah M.↗
DOE OSTI2020

Does Selectivity of Molecular Catalysts Change with Time? Polymerization Imaged by Single-Molecule Spectroscopy

The chemoselectivity of molecular catalysts underpins much of modern synthetic organic chemistry. However, little is known about the selectivity of individual catalysts because this single-catalyst-level behavior is hidden by the bulk catalytic behavior. In this work, for the first time, the selectivity of individual molecular catalysts for two different reactions is imaged in real time at the single-catalyst level. This imaging is achieved through fluorescence microscopy paired with spectral probes that produce a snapshot of the instantaneous chemoselectivity of a single catalyst for either a single-chain-elongation or a single-chain-termination event during ruthenium-catalyzed polymerization. Superresolution imaging of multiple selectivity events, each at a different single-molecular ruthenium catalyst, indicates that catalyst selectivity may be unexpectedly spatially and time-variable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Polyethylene Incorporating Diels–Alder Comonomers: A “Trojan Horse” Strategy for Chemically Recyclable Polyolefins

Polyolefins with periodic unsaturation in the backbone chain are sought after for synthesizing chemically recyclable polymers or telechelic polyolefin macromonomers. Here we introduce a bottom-up synthesis of unsaturated high-density polyethylene (HDPE) via copolymerization of ethylene with dimethyl 7-oxabicyclo[2.2.1]hepta-2,5-diene-3,5-dicarboxylate followed by post-polymerization retro-Diels–Alder to unveil hidden double bonds in the polymer backbone. The incorporation of this “Trojan Horse” comonomer was varied and a series of unsaturated HDPE polymers with block lengths of 1.2, 1.9, and 3.5 kDa between double bonds was synthesized. Cross metathesis of unsaturated HDPE samples with 2-hydroxyethyl acrylate yielded telechelic ester terminated PE macromonomers suitable for the preparation of ester-linked PE. These materials were depolymerized and repolymerized, making them suitable candidates for chemical recycling. Here, the ester-linked PE displayed thermal and mechanical properties comparable to commercial HDPE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Real‐Time Observation of Slowed Charge Density Wave Dynamics in Thinned 1T‐TaS 2

Abstract Transient electrical pulsing is used to investigate the slowed charge density wave (CDW) kinetics of 1T‐TaS 2 . These measurements distinguish a fast response of the material, consistent with the onset of self‐heating, from much slower transients that occur on timescales orders of magnitude longer than this. The latter variations appear consistent with slow configurational changes in the CDW, which, due to the thin nature of the 1T‐TaS 2 , can be distinguished from the much faster dynamics of Joule heating. Experiments in which the cooling of the material is interrupted, demonstrate the possibility of “programming” it in different, strongly nonequilibrium, CDW phases. Collectively, the results point to the existence of a complex free‐energy space for the thinned material, whose multi‐valley structure and hidden metastable states govern the resulting thermal and field‐driven dynamics. Crucially, this work demonstrates that while the CDW dynamics in this material may have a thermal character, the timescales associated with these motions can be very different from those on which self‐heating occurs. This discovery will be important for efforts to implement active devices that utilize the CDW states of thinned 1T‐TaS 2 .

Yin, Shenchu↗
DOE OSTI2024

Applying Machine‐Learning Methods to Laser Acceleration of Protons: Lessons Learned From Synthetic Data

ABSTRACT In this study, we consider three different machine‐learning methods—a three‐hidden‐layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine‐learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine‐learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine‐learning model we considered, support vector regression performed very well in our tests.

Desai, Ronak↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

Abstract There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tucker and tensor train formats, are used to approximate the initial large‐tensor. Further, tensor factorization is used to extract latent features that can facilitate discoveries of new mechanisms and signatures hidden in the data, where the explainability of the latent features is of principal importance. Nonnegative tensor factorization extracts latent features that are naturally sparse and parts of the data, which makes them easily interpretable. However, to take into account available domain knowledge and subject matter expertise, often additional constraints need to be imposed, which lead us to canonical decomposition with linear constraints (CANDELINC), a canonical polyadic decomposition with rank deficient factors. In CANDELINC, Tucker compression is used as a preprocessing step, which lead to a larger residual error but to more explainable latent features. Here, we propose a nonnegative CANDELINC (nnCANDELINC) accomplished via a specific nonnegative Tucker decomposition; we refer to as minimal or canonical nonnegative Tucker. We derive several results required to understand the specificity of nnCANDELINC, focusing on the difficulties of preserving the nonnegative rank of a tensor to its Tucker core and comparing the real valued to nonnegative case. Finally, we demonstrate nnCANDELINC performance on synthetic and real‐world examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish newT g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Polymer Science↗
DOE OSTI2023

Classifying metal‐binding sites with neural networks

Abstract To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine‐tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe‐2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub‐classifications of the [2Fe‐2S] cofactor: (1) a standard [2Fe‐2S] cofactor and (2) a Rieske [2Fe‐2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal‐binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal‐binding site identification in proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Benchmarking Quantum Chemistry Computations with Variational, Imaginary Time Evolution, and Krylov Space Solver Algorithms

Quantum chemistry is a key application area for noisy-intermediate scale quantum (NISQ) devices, and therefore serves as an important benchmark for current and future quantum computer performance. Previous benchmarks in this field have focused on variational methods for computing ground and excited states of various molecules, including a benchmarking suite focused on the performance of computing ground states for alkali-hydrides under an array of error mitigation methods. State-of-the-art methods to reach chemical accuracy in hybrid quantum-classical electronic structure calculations of alkali hydride molecules on NISQ devices from IBM are outlined here. Here it is demonstrated how to extend the reach of variational eigensolvers with symmetry preserving Ansätze. Next, it is outlined how to use quantum imaginary time evolution and Lanczos as a complementary method to variational techniques, highlighting the advantages of each approach. Finally, a new error mitigation method is demonstrated which uses systematic error cancellation via hidden inverse gate constructions, improving the performance of typical variational algorithms. These results show that electronic structure calculations have advanced rapidly, to routine chemical accuracy for simple molecules, from their inception on quantum computers a few short years ago, and they point to further rapid progress to larger molecules as the power of NISQ devices grows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Density estimation via measure transport: Outlook for applications in the biological sciences

Abstract One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.

gene expression data↗
DOE OSTI2025

Electron Microscopy Approaches to Unraveling the Structure of Amorphous Materials

Determining atomic structures in crystalline materials—where atoms are arranged in rigid, periodic lattices—has been highly successful using probes such as electrons, X-rays, and neutrons. In contrast, amorphous materials, despite their ubiquity and technological importance, remain far more challenging to characterize with comparable accuracy and precision. This review highlights existing, emerging, and potential (scanning) transmission electron microscopy ((S)TEM) techniques for probing short- and medium-range order in amorphous materials. Approaches ranging from high-resolution (S)TEM imaging and selected electron diffraction pattern to four-dimensional STEM (4D-STEM) based pair distribution function, fluctuation electron microscopy, tomography, ptychography, and spectroscopic methods are discussed, emphasizing their ability to provide complementary insights across multiple length scales—from sub-angstrom local environments to nanometer-scale correlations. Here, we further explore the promise of multimodal and correlative strategies, as well as the growing role of machine learning and physics-informed AI in enabling real-time, quantitative interpretation of complex structural signatures. Together, these advances point toward a future where electron microscopy not only reveals the hidden order in amorphous systems but also establishes robust structure–property relationships, paving the way for materials innovation in disordered matter.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Yeasts from temperate forests

Yeasts are ubiquitous in temperate forests. While this broad habitat is well-defined, the yeasts inhabiting it and their life cycles, niches, and contributions to ecosystem functioning are less understood. Yeasts are present on nearly all sampled substrates in temperate forests worldwide. They associate with soils, macroorganisms, and other habitats and no doubt contribute to broader ecosystem-wide processes. Researchers have gathered information leading to hypotheses about yeasts' niches and their life cycles based on physiological observations in the laboratory as well as genomic analyses, but the challenge remains to test these hypotheses in the forests themselves. Here, we summarize the habitat and global patterns of yeast diversity, give some information on a handful of well-studied temperate forest yeast genera, discuss the various strategies to isolate forest yeasts, and explain temperate forest yeasts' contributions to biotechnology. Here, we close with a summary of the many future directions and outstanding questions facing researchers in temperate forest yeast ecology. Yeasts present an exciting opportunity to better understand the hidden world of microbial ecology in this threatened and global habitat.

59 BASIC BIOLOGICAL SCIENCES↗