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At least 415 records · Page 23

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NASA NTRS1993

Receptivity in parallel flows: An adjoint approach

Linear receptivity studies in parallel flows are aimed at understanding how external forcing couples to the natural unstable motions which a flow can support. The vibrating ribbon problem models the original Schubauer and Skramstad boundary layer experiment and represents the classic boundary layer receptivity problem. The process by which disturbances are initiated in convectively-unstable jets and shear layers has also received attention. Gaster was the first to handle the boundary layer analysis with the recognition that spatial modes, rather than temporal modes, were relevant when studying convectively-unstable flows that are driven by a time-harmonic source. The amplitude of the least stable spatial mode, far downstream of the source, is related to the source strength by a coupling coefficient. The determination of this coefficient is at the heart of this type of linear receptivity study. The first objective of the present study was to determine whether the various wave number derivative factors, appearing in the coupling coefficients for linear receptivity problems, could be reexpressed in a simpler form involving adjoint eigensolutions. Secondly, it was hoped that the general nature of this simplification could be shown; indeed, a rather elegant characterization of the receptivity properties of spatial instabilities does emerge. The analysis is quite distinct from the usual Fourier-inversion procedures, although a detailed knowledge of the spectrum of the Orr-Sommerfeld equation is still required. Since the cylinder wake analysis proved very useful in addressing control considerations, the final objective was to provide a foundation upon which boundary layer control theory may be developed.

Hill, D. Christopher↗

DOE OSTI2020

From Atom to Engine: Understanding Fundamental Effects of Structure on Combustion Using Tandem Experiment and Computation

The ability to tie structural features to a fuel candidate’s ignition properties provides a path for rational design of advantaged fuels that facilitate higher-efficiency, lower-emitting combustion in engines. The time evolution of a fuel’s radical population directly correlates with global combustion parameters, and because the generated radicals depend on the initial structure and reaction pathways of the fuel molecule, it follows that the molecular structure of a fuel has a direct impact on this cascade, and therefore salient ignition-tied fuel properties. This work aims to understand the impact of structural features in the context of global combustion properties for direct application to emerging engine technologies. Specifically, we highlight the ability to identify potential fuel blendstocks for specific engine strategies by examining and elucidating the radical cascade tied to ignition. A representative subset of alcohols possessing a variety of structural features (e.g. branching, chain length) was selected for study using a robust combination of experimental and computational methods. The approach begins with identifying short-lived radicals and likely abstraction sites upon pyrolysis in a microreactor coupled with photoionization mass spectrometry for direct detection of product species. In tandem, electronic structure calculations were performed to calculate the relevant potential energy surfaces for fuel pyrolysis as well as the subsequent O2 addition-isomerization reactions, which govern ignition behavior. Furthermore, the theoretical energetic and ro-vibrational data are used to calculate rate constants for direct ignition modeling to explore how structural effects impact NTC behavior. The microreactor data combined with electronic structure theory calculations provide a well-informed picture of the reactions relevant to ignition for each fuel in this study, as well as the most likely radicals generated under engine conditions. This thorough fundamental picture of fuel decomposition and ignition chemistry is used to correlate the observed ignition behavior to molecular structural effects.

advanced biofuels↗

DOE OSTI2025

Mesoporous optically clear heat insulators for sustainable building envelopes

Mesoporous materials exhibit highly controlled nanoscale structures, often templated by liquid crystalline assemblies of surfactants, with emergent and often designable physical properties. However, scaling their fabrication to be suitable for uses like envelopes of buildings is challenging. Here we describe fabrication of flexible square-meter-sized films and multi-centimeter-thick slabs made of three-dimensional spatial graphs of mesopore tubes having all structural features under 50 nanometers. Solution-based kinetic fabrication process templates growing networks of cylindrical surfactant micelles with thin tubes of polysiloxane forming gel networks and, upon replacing surfactants and solvents with air, yields lightweight materials with >99% visible-range optical transparency and ~10 mW K-1m-1 thermal conductivity. Such predesigned metamaterials enable transparent thermal barriers for wall-grade insulated glass units, square-meter window retrofits and unconcentrated solar thermal energy harnessing.

14 SOLAR ENERGY↗

DOE OSTI2025

Equilibrium-gated pattern formation: How molecular dissociation thermodynamics drive emergent behavior in dissipative polymeric systems

Emergent patterns in biological systems arise through dissipative processes that balance reaction and transport phenomena, producing highly functional properties from self-regulating mechanisms. Synthetic fabrication, by contrast, often relies on user-controlled, multistep methods that lack the self-organizing capabilities of natural systems. Inspired by nature, we sought chemical systems that integrate strongly coupled reaction and transport phenomena, identifying frontal ring-opening metathesis polymerization (FROMP) as a method capable of creating diverse forms and functions through reactive processing. By employing discrete molecular initiators, FROMP allows precise control of key reaction steps—inhibition, initiation, and propagation. Using an integrated computational and experimental framework, we uncover how near-equilibrium inhibition dynamics, coupled with far-from-equilibrium reaction kinetics, drive pattern formation in frontally polymerized synthetic materials. We propose the concept of equilibrium-gated pattern formation, demonstrating how initiator chemistry can be tuned to achieve programmable macroscale properties. Our study reveals a surprising insight: Emergent behavior in FROMP systems arises from the inhibition-dominated regime of resin composition, expanding prior observations that such behavior is confined to a narrow compositional space near the boundary between front quenching and uniform front propagation. We identify a broader compositional window, far from the quenching regime, where emergent behavior reliably manifests. This expanded design space significantly enhances the operational flexibility of reactive systems and their capacity for self-organization. Furthermore, these insights provide a roadmap for designing bioinspired materials with self-organizing capabilities, unlocking possibilities in synthetic manufacturing.

Architected Polymers↗

DOE OSTI2023

Emergent Glassy Behavior in a Kagome Rydberg Atom Array

Here, we present large-scale quantum Monte Carlo simulation results on a realistic Hamiltonian of kagome-lattice Rydberg atom arrays. Although the system has no intrinsic disorder, intriguingly, our analyses of static and dynamic properties on large system sizes reveal emergent glassy behavior in a region of parameter space located between two valence bond solid phases. The extent of this glassy region is demarcated using the Edwards-Anderson order parameter, and its phase transitions to the two proximate valence bond solids—as well as the crossover towards a trivial paramagnetic phase—are identified. We demonstrate the intrinsically slow (imaginary) time dynamics deep inside the glassy phase and discuss experimental considerations for detecting such a quantum disordered phase with numerous nearly degenerate local minima. Our proposal paves a new route to the study of real-time glassy phenomena and highlights the potential for quantum simulation of a distinct phase of quantum matter beyond solids and liquids in current-generation Rydberg platforms.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

In Situ Prediction of Microstructure and Mechanical Properties in Laser-Remelted Al-Si Alloys: Towards Enhanced Additive Manufacturing

Laser surface remelting of aluminum alloys has emerged as a promising technique to enhance mechanical properties through refined microstructures. This process involves rapid cooling rates ranging from 10 3 to 10 8 °C/s, which increase solid solubility within aluminum alloys, shifting their eutectic composition to a larger value of silicon content. Consequently, the resulting microstructure combines a strengthened aluminum matrix with silicon fibers. This study focuses on the laser scanning of Al-Si aluminum alloy to reduce the size of aluminum matrix spacings and transform fibrous silicon particles from micrometer to nanometer dimensions. Analysis revealed that the eutectic structure contained 17.55% silicon by weight, surpassing the equilibrium eutectic composition of 12.6% silicon. Microstructure dimensions within the molten zones, termed ‘melt pools’, were extensively examined using Scanning Electron Microscopy (SEM) at intervals of approximately 20 μm from the surface. A notable increase in hardness, exceeding 50% compared to the base plate, was observed in the melt pool regions. Thus, it is exemplified that laser surface remelting introduces a novel strengthening mechanism in the alloy. Moreover, this study develops an in situ method for predicting melt pool properties and dimensions. A predictive model is proposed, correlating energy density and spectral signals emitted during laser remelting with mechanical properties and melt pool dimensions. This method significantly reduces characterization time from days to seconds, offering a streamlined approach for future studies in additive manufacturing.

36 MATERIALS SCIENCE↗

NASA NTRS1994

Theoretical properties of Omega-loops in the convective zone of the Sun. 1: Emerging bipolar magnetic regions

It is proposed that the observed 500 G intensity of the magnetic fields emerging through the surface of the Sun can be understood from the Bernoulli effect in the upwelling Omega-loops of magnetic field. It is also proposed that the inferred 10(exp 5) G azimuthal flux bundles below the base of the convective zone can be understood as a consequence of the large-scale buoyancy associated with the upwelling fluid in and around the rising Omega-loop. The process fits in naturally with the Babcock-Leighton form of the solar alpha-omega-dynamo. The emerging Omega-loop implies the coherence of the upwelling all the way from the bottom of the convective zone, enhancing the convective heat transport to account for the observed variation of the solar irradiance by about two parts in 10(exp 3).

Parker, E. N.↗

NASA NTRS1977

Physical properties of the particles composing the Martian dust storm of 1971-1972

Data obtained with the infrared interferometric spectrometer (IRIS) aboard Mariner 9 during the global Martian dust storm of 1971-1972 are analyzed to study the particle sizes in detail, estimate the dust particle size distribution, and determine the dust optical depth. Theoretical spectra of the emergent intensity from dust clouds with properties similar to those of the Martian cloud are generated, deficiencies of the approximation used are discussed, and the information content of the IR aerosol spectra is assessed. Analysis of IRIS spectra from a series of orbits spread through the early months of the Mariner 9 mission indicates that the dust could have been either igneous material with an SiO2 content similar to or higher than that of a typical terrestrial sediment or clay minerals similar to those on earth. In either case, the Martian dust was differentiated by geologic or weathering processes. The dust particle size distribution between 1 and 10 microns is found to have a slope similar to that of terrestrial dust size distributions far from source regions.

Toon, O. B.↗

DOE OSTI2025

Impact of dynamic Jahn-Teller effect on magnetic excitations, lattice vibration, and thermal conductivity in U 𝑥 ⁢T⁢h 1−𝑥 ⁡O 2 system

Vibrational and magnetic properties of single-crystal uranium-thorium dioxide (U 𝑥 ⁢T⁢h 1−𝑥⁡ O 2 ) with a full range of 0 < 𝑥 < 1 are investigated. Thorium dioxide is a diamagnet whose thermal properties are governed by lattice vibration. The addition of paramagnetic uranium ions leads to the emergence of magnetic effects that alter the thermophysical properties noticeably even at room temperature. The interaction of phonons with magnetic moments of uranium 5⁢𝑓 electrons mediated by magnetoelastic coupling results in an anomalous low-temperature thermal conductivity profile. Analysis of the magnetic susceptibility measurements indicates a departure from the Curie-Weiss relationship characteristic of noninteracting paramagnetic ions, previously associated with the dynamic Jahn-Teller (DJT) effect characterized by coupling between spin and the oxygen sublattice. The T 2⁢g Raman peak position follows a nonlinear trend as a function of uranium concentration and hints that these Raman active optical modes play a role in either DJT or in mediating quadrupole-quadrupole interactions. A first-principle-based thermal transport model is implemented to explain the low-temperature transport measurements, where the anomalous reduction is attributed to phonon-spin resonant scattering. The interplay between spins and phonons is also captured using high-resolution inelastic x-ray scattering (IXS) measurements of phonon linewidths. Our results provide insights into the phonon interactions with the magnetic excitations governing DJT effect and impacting the low-temperature thermal transport processes in this material system. Furthermore, these findings have implications for understanding low-temperature thermal transport and magnetic properties in advanced materials for information processing and energy applications.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Spin Glass Behavior and Giant Magnetoresistance via Aliovalent Fe/Ni Alloying in Amorphous Tetrathiafulvalene-Tetrathiolate Coordination Polymers

The discovery of materials with programmable combinations of charge transport and magnetic properties is a major goal in synthetic chemistry. Molecular materials, such as coordination polymers (CPs), are emerging candidates in this field due to their synthetic modularity. While there are many conductive or magnetic CPs, single-component examples with both properties are still emerging. Here, in this work, we demonstrate how alloying paramagnetic Fe(III) centers into a highly conductive Ni-based CP results in novel amorphous materials with high conductivity and giant magnetoresistance. Aliovalent doping with paramagnetic Fe centers engenders spin-glass transitions, while Ni and the strong π-stacking interactions of tetrathiafulvalene-2,3,6,7-tetrathiolate (TTFtt) ligands support conductivity. Ni 0.69 Fe 0.31 TTFtt has σ ≈ 200 S/cm and a 1.8 K magnetoresistance of −52% at 5 T, among the largest for any coordination solid. This work demonstrates not only how magnetic properties can be rationally incorporated into conductive CPs, but also an unexpected potential for amorphous materials in spintronic applications.

amorphous materials↗

DOE OSTI2025

Modulating Poly(oligocyclobutane) Properties Through Backbone Modifications

Poly(1,n′-divinyl)oligocyclobutane (pDVOCB) has emerged as a class of poly(cycloolefin) that is amenable to chemical recycling and demonstrates promising thermomechanical properties. However, their high melting temperatures coupled with insolubility makes melt processing challenging due to thermo-oxidation of internal alkenes. To address these issues, we describe a series of polymers incorporating modifications to the pDVOCB backbone and analyze the effects on material stability and processability. Intentional migration of the internal 1,2-disubstituted alkenes to an exocyclic trisubstituted position yields isomerized pDVOCB (IpDVOCB) which exhibits a depression of thermal transitions by up to 50 °C. Conversely, elimination of stereoirregularity between enchained DVOCB oligomers through alkene saturation yields hydrogenated pDVOCB (HpDVOCB), resulting in elevated thermal transitions by up to 30 °C. Furthermore, these shifts are attributed to changes in crystal defect density which is strongly influenced by chain stereoregularity. Understanding these behaviors guides future polymer design and expands the control and use of this new class of recyclable poly(cycloolefin)s.

Differential scanning calorimetry↗

DOE OSTI2021

Machine learning for polymeric materials: an introduction

We report that Polymers are incredibly versatile materials and have become ubiquitous. Increasingly, researchers are using data science and polymer informatics to design new materials and understand their structure-property relationships. Polymer informatics is an emerging field. While there are many useful tools and databases available, many are not widely utilized. Herein, we introduce the field of polymer informatics and discuss some of the available databases and tools. We cover how to share polymer data, approaches for preparing a dataset for machine learning and recent applications of machine learning to polymer property prediction and polymer synthesis.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗

DOE OSTI2024

Integrating Experiments and Well Logs to Predict Caney Shale Static Mechanical Properties During Production with Supervised Machine Learning

Caney shale is one of the emerging oil reservoirs in Oklahoma. Understanding the impact of effective stress on its mechanical properties is critical for predicting hydraulic fracture geometry and overall hydrocarbon production. The objective of our study is to evaluate the impact of effective stress on the dynamic Young’s modulus using ultrasonic velocity measurements for Caney shale samples. A triaxial cell was utilized to measure ultrasonic (P-wave and S-wave) velocities for ten downhole Caney shale samples under various effective stresses to indirectly assess the impact of pore pressure change. The dynamic Young’s moduli estimated from these measurements were integrated with available conventional well logs (excluding sonic logs) and triaxial test results from Benge et al. (2021) to predict the static Young’s modulus using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that the estimated dynamic Young’s moduli from ultrasonic measurements were higher than the corresponding static Young’s modulus of cores from the same vertical well at similar depths. With increasing effective stress, the dynamic Young’s modulus increased for all samples. The estimated dynamic-to-static correction factor tended to be higher in zones of high neutron porosity (PHIN) and low density compared to other zones. Finally, SHapley Additive exPlanations (SHAP) for RF and XGBoost models identified depth, gamma ray (GR), and PHIN as key features for predicting the static Young’s modulus. This study enhances our understanding of the dynamic and static Young’s moduli for the Caney shale interval, as a function of effective stress and conventional well logs. The findings from this study can improve predictions of production throughout the well's lifespan by offering insights into the mechanical property degradation resulting from pore pressure depletion.

Kholy, Sherif M.↗

DOE OSTI2024

Predictions of delafossite-hosted honeycomb and kagome phases

Delafossites, typically denoted by the formula ABO 2 , are a class of layered materials that exhibit a wide range of electronic and optical properties. Recently, the idea of modifying these delafossites into ordered kagome or honeycomb phases via strategic doping has emerged as a potential way to tailor these properties. In this study, we use high-throughput density functional theory calculations to explore many possible candidate kagome and honeycomb phases by considering dopants selected from the parent compounds of known ternary delafossite oxides from the inorganic crystal structure database. Here our results indicate that while A-site in existing delafossites can host a limited range of elemental specifies, and display a low propensity for mixing or ordering, the oxide sub-units in the BO 2 much more readily admit guest species. Our study identifies four candidate B-site kagome and fifteen candidate B-site honeycombs with a formation energy more than 50 meV f.u. −1 below other competing phases. The ability to predict and control the formation of these unique structures offers exciting opportunities in materials design, where innovative properties can be engineered through the selection of specific dopants. A number of these constitute novel correlated metals, which may be of interest for subsequent efforts in synthesis. These novel correlated metals may have significant implications for quantum computing, spintronics, and high-temperature superconductivity, thus inspiring future experimental synthesis and characterization of these proposed materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Layer Number Dependence of Chirality and Spin Polarized Lifetime in Chiral 2D Halide Perovskites

Chiral metal halide perovskite semiconductors (CMHS) are fascinating semiconductors with unique chiroptical properties and spin-polarized charge transport. Achieving long spin lifetimes and high carrier mobility concurrently is essential to realize the true potential of CMHS in manipulating charge, spin, and light. While conventional monolayer n = 1 CMHS possess appreciable anisotropy factors of circular dichroism (g CD ) and photoluminescence (g lum ), imparting chirality to quasi-2D CMHS (n > 1) with enhanced carrier mobilities is underexplored. Herein, we systematically investigate the layer number (n-value) dependence and emergent trade-offs in chiroptical properties, spin-relaxation times, and carrier mobilities in chiral quasi-2D (R/S-MPEA) 2 MA n-1 Pb n I 3n+1 single crystals and thin films (R/S-MPEA: R/S-β-methylphenylethylammonium; MA: methylammonium; n = 1–3). Films with n = 2 exhibited the highest g CD of 8 × 10 –3 , an order of magnitude larger than their n = 1 and n = 3 counterparts. On the other hand, n = 3 films demonstrated enhanced spin lifetimes up to 15 ps along with increased carrier mobility up to 11.6 cm 2 V –1 s –1 . As a result, photodiode-type photodetectors based on n = 3 CMHS reveal high specific detectivity and superior discrimination of circularly polarized light, outperforming n = 1 and 2. These findings highlight the potential of quasi-2D CMHS as tunable, high-performance platforms with longer spin lifetime and diffusion length, enabling new functionalities.

14 SOLAR ENERGY↗

DOE OSTI2022

Highly fluorescent purine-containing conjugated copolymers with tailored optoelectronic properties

Conjugated copolymers containing electron donor and acceptor units in their main chain have emerged as promising materials for organic electronic devices due to their tunable optoelectronic properties. In this work, we describe the use of direct arylation polymerization to create a series of fully π-conjugated copolymers containing the highly tailorable purine scaffold as a key design element. To create efficient coupling sites, dihalopurines are flanked by alkylthiophenes to create a monomer that is readily copolymerized with a variety of conjugated comonomers, ranging from electron-donating 3,4-dihydro-2H-thieno[3,4-b][1,4]dioxepine to electron-accepting 4,7-bis(5-bromo-3-hexylthiophen-2-yl)benzo[c][1,2,5]thiadiazole. The comonomer choice and electronic nature of the purine scaffold allow the photophysical properties of the purine-containing copolymers to be widely varied, with optical bandgaps ranging from 1.96–2.46 eV, and photoluminescent quantum yields as high as Φ = 0.61. Frontier orbital energy levels determined for the various copolymers using density functional theory tight binding calculations track with experimental results, and the geometric structures of the alkylthiophene-flanked purine monomer and its copolymer are found to be nearly planar. The utility of direct arylation polymerization and intrinsic tailorability of the purine scaffold highlight the potential of these fully conjugated polymers to establish structure–property relationships based on connectivity pattern and comonomer type, which may broadly inform efforts to advance purine-containing conjugated copolymers for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗