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At least 91 records · Page 5

CRADA Final Report: CRADA Number NFE-18-07341 with Eonix LLC

Over the past two years, my work has been focused on the development of a commercially viable electrolyte that expands the voltage window of ultracapacitors featuring standard activated carbon electrodes from 2.7V to greater than 3V. During this fellowship in collaboration with staff at Oak Ridge National Laboratory, we refined a high throughput screening process to rapidly analyze the properties and performance of electrolyte candidates in ultracapacitors and establish a quantitative structure activity relationship (QSAR) methodology for candidate selection. Over sixty electrolyte chemistries were screened using these novel techniques.

42 ENGINEERING↗

Rheo–Electric Foundations and Engineering of Carbon Slurry Flow Electrodes

Flowable carbon slurry electrodes promise continuous, scalable electrochemical desalination and energy storage when their rheology (flow and stability) and electronic connectivity are jointly optimized. This report integrates six complementary thrusts that we executed under the Laboratory Directed Research and Development (LDRD) project: (i) a fast, ex situ, centrifuge-compatible method to quantify pellet (packed slurry) electronic conductivity, with a validated cell constant (“shape factor”) and quantitative structure–property insights for carbon mixtures; (ii) a controlled circulation study showing that peristaltic pumping can irreversibly alter particle size and morphology over laboratory-relevant durations; (iii) development of a miniature jet pump that preserves particle integrity while achieving entrainment ratios of 2–4 at practical flow rates; (iv) a comprehensive mapping of how a representative non-ionic surfactant (Tween 20) collapses the carbon gel network, separating network-percolation losses from intrinsic particle contact resistance; (v) in-situ visualization revealing how gravitational settling and flow-driven mixing compete to control charge transport, with dynamic bed layers preventing saturation while achieving 30-fold local concentration enhancement; and (vi) a simple, widely adoptable fabrication route to structured, gas-nitrided titanium nitride (TiN) titanium current collectors that dramatically lower interfacial losses and enable geometry-driven performance gains. Together, these results provide a coherent toolchain and design rules to accelerate formulation screening, de-risk device prototyping, and standardize characterization for carbon slurry electrodes in flow-electrode capacitive deionization (FCDI), electrochemical flow capacitor (EFC), and related systems.

25 ENERGY STORAGE↗

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗

Recent Advances on Computational Modeling of Supported Single-Atom and Cluster Catalysts: Characterization, Catalyst–Support Interaction, and Active Site Heterogeneity

To satisfy the need for catalyst materials with high activity, selectivity, and stability for energy conversion, material design and discovery guided by theoretical insights are a necessity. In the past decades, the rise in theoretical investigations into the properties of catalyst materials, reaction mechanisms, and catalyst design principles has shed light on the catalysis field. Quantitative structure–activity relationships have been developed through incorporating spectroscopic simulations, electronic structure calculations, and reaction mechanistic studies. Here, in this review, we report the state-of-the-art computational approaches to catalyst materials characterization for supported single-atom and cluster catalysts utilizing spectroscopic simulations, i.e., XANES simulation, and material properties investigation via electronic-structure calculations. Furthermore, approaches regarding reaction mechanisms, focusing on active site heterogeneity, are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure–Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD 50 ). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R 2 = 0.88), validation set (R 2 = 0.95), and true external test set (R 2 = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

54 ENVIRONMENTAL SCIENCES↗

TERSSE: Definition of the Total Earth Resources System for the Shuttle Era. Volume 8: User's Mission and System Requirements Data (appendix A of Volume 3)

A computer printout is presented of the mission requirement for the TERSSE missions and their associated user tasks. The data included in the data base represents a broad-based attempt to define the amount, extent, and type of information needed for an earth resources management program in the era of the space shuttle. An effort was made to consider all aspects of remote sensing and resource management; because of its broad scope, it is not intended that the data be used without verification for in-depth studies of particular missions and/or users. The data base represents the quantitative structure necessary to define the TERSSE architecture and requirements, and to an overall integrated view of the earth resources technology requirements of the 1980's.

Source record↗

Evolution of solitary density waves in stellar winds of early-type stars: A simple explanation of discrete absorption component behavior

We model the evolution of a density shell propagating through the stellar wind of an early-type star, in order to investigate the effects of such shells on UV P Cygni line profiles. Unlike previous treatments, we solve the mass, momentum, and energy conservation equations, using an explicit time-differencing scheme, and present a parametric study of the density, velocity, and temperature response. Under the assumed conditions, relatively large spatial scale, large-amplitude density shells propagate as stable waves through the supersonic portion of the wind. Their dynamical behavior appears to mimic propagating 'solitary waves,' and they are found to accelerate at the same rate as the underlying steady state stellar wind (i.e., the shell rides the wind). These hydrodynamically stable structures quantitatively reproduce the anomalous 'discrete absorption component' (DAC) behavior observed in the winds of luminous early-type stars, as illustrated by comparisons of model predictions to an extensive International Ultraviolet Explorer (IUE) time series of spectra of zeta Puppis (O4f). From these comparisons, we find no conclusive evidence indicative of DACs accelerating at a significantly slower rate than the underlying stellar wind, contrary to earlier reports. In addition, these density shells are found to be consistent within the constraints set by the IR observations. We conclude that the concept of propagating density shells should be seriously reconsidered as a possible explanation of the DAC phenomenon in early-type stars.

Waldron, Wayne L.↗

The effect of space flight on spatial orientation

Both during and following early space missions, little neurosensory change in the astronauts was noted as a result of their exposure to microgravity. It is believed that this lack of in-flight adaptation in the spatial orientation and perceptual-motor system resulted from short exposure times and limited interaction with the new environment. Parker and Parker (1990) have suggested that while spatial orientation and motion information can be detected by a passive observer, adaptation to stimulus rearrangement is greatly enhanced when the observer moves through or acts on the environment. Experience with the actual consequences of action can be compared with those consequences expected on the basis of prior experience. Space flight today is of longer duration, and space craft volume has increased. These changes have forced the astronauts to interact with the new environment of microgravity, and as a result substantial changes occur in the perceptual and sensory-motor repsonses reflecting adaptation to the stimulus rearrangement of space flight. We are currently evaluating spatial orientation and the perceptual-motor systems' adaptation to microgravity by examining responses of postural control, head and gaze stability during locomotion, goal oriented vestibulo-ocular reflex (VOR), and structured quantitative perceptual reports. Evidence suggests that humans can successfully replace the gravitational reference available on Earth with cues available within the spacecraft or within themselves, but that adaptation to microgravity is not appropriate for a return to Earth. Countermeasures for optimal performance on-orbit and a successful return to earth will require development of preflight and in-flight training to help the astronauts acquire and maintain a dual adaptive state. An understanding of spatial orientation and motion perception, postural control, locomotion, and the VOR will aid in this process.

Reschke, Millard F.↗

Can machine learning predict fuel properties accurately?

High-potential molecules derived from biomass sources may suitably replace or supplement traditional nonrenewable hydrocarbon fuels to reduce pollution and fuel processing cost. Experimental property testing of these bioproducts is usually conducted years after initial bench-scale experiments, due to high experimental costs and/or high volume requirements. However, neglecting to conduct property testing early in the pathway development cycle can lead to investments spent on scaling-up production of bioproducts and biofuels that do not perform as expected. Instead, machine-learning techniques can be used to develop quantitative structure–property relationships for molecules using a relatively large training set of molecular descriptor data. For this study, we compiled measured properties, IR spectra, and molecular descriptors of bio-based molecules from databases and published studies for training models of bioproduct properties. We trained regression models with molecular descriptors and will compare results of different estimators. This study describes the first steps towards a performance prediction tool for bio-based alternative fuels. Keywords: Machine learning, biofuels, jet fuels, fuel properties

Mayer, Morgan A.↗

Quantitative relationship between structural orthorhombicity, shear modulus, and heat capacity anomaly of the nematic transition in iron-based superconductors

Electronic nematicity in iron pnictide materials has been extensively studied by various experimental techniques, yet its heat capacity anomaly at the phase transition has not been examined quantitatively. In this paper, we review the thermodynamic description of nematicity in Ba(Fe 1- x Cox) 2 As 2 using the Landau free energy, which defines the behavior of three thermodynamic quantities: the structural orthorhombicity that develops below the nematic transition, the softening shear modulus above the transition, and the discontinuous heat capacity at the transition. We derive a quantitative relationship between these three quantities, which is found to hold for a range of dopings. We report this result shows that the nematic transition is exceedingly well described by a mean-field model in the underdoped regime of the phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Electronic energy loss and ion velocity correlation effects in track production in swift-ion-irradiated LiNbO3: A quantitative assessment between structural damage morphology and energy deposition

The primary motivation for studying how irradiation modifies the structures and properties of solid materials involves the understanding of undesirable phenomena, including irradiation-induced degradation of components in nuclear reactors and space exploration, and beneficial applications, including material performance tailoring through ion beam modification and defect engineering. In this work, the formation mechanism of latent tracks with different damage morphologies in LiNbO 3 crystals under 0.09–6.17 MeV/u ion irradiation with an electronic energy loss from 2.6–13.2 keV/nm is analyzed by experimental characterizations and numerical calculations. Irradiation-induced damage is preliminarily evaluated via the prism coupling technique to analyze the correlation between the dark-mode spectra and energy loss profiles of irradiated regions. Under the irradiation conditions of different ion velocities and electronic energy losses, different damage morphologies, from individual spherical defects to discontinuous and continuous tracks, are experimentally characterized. During ion penetration process, the ion velocity determines the spatiotemporal distribution of deposited irradiation energy induced by electronic energy loss, meaning that the two essential factors including electronic energy loss and ion velocity co-affect the track damage. The inelastic thermal spike model is used to numerically calculate the spatiotemporal evolutions of energy deposition and the corresponding atomic temperature under different irradiation conditions, and a quantitative relationship is proposed by comparison with corresponding experimentally observed track damage morphologies. Additionally, the obtained quantitative relationship between irradiation conditions and track damage provides deep insight and guidance for understanding the damage behavior of crystal materials in extreme radiation environments and selecting irradiation parameters, including ion species and energies, for ion beam technique application in atomic-level defect manipulation, material modification, and micro/nanofabrication.

36 MATERIALS SCIENCE↗

Quantitative surface studies of moiré intercalated Pb structures under Gr/SiC

Quantitative growth studies using surface diffraction and scanning tunneling microscopy are carried out to determine the intercalation conditions for Pb under Gr/SiC, with initial phase a mixture of buffer-layer and single-layer graphene. Intercalation is a kinetically complex, multistage process with intercalated Pb moving between different subsurface locations with annealing, eventually to the location of higher stability. The transfer is determined by monitoring the difference of a given spot intensity from its clean value, as a function of growth parameters. In addition, a broad, low-intensity (10×10) gr moiré phase is observed exclusively originating from intercalated Pb. Well-defined growth conditions are identified for the moiré spot to be observed reversibly. The (10×10) gr phase follows changes of the Pb(10) spot during growth and annealing experiments, instead of changes of the other Gr/SiC spots indicating preferred nucleation of Pb islands on the (10×10) gr domains. These structural results are discussed in connection to different models proposed for the (10×10) gr supercell and the subsurface Pb coverage. In conclusion, mass transport of subsurface Pb is unusually high, possibly because of the very high compression of the intercalated Pb, when compared to metallic Pb(111).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Method and apparatus for thermographically and quantitatively analyzing a structure for disbonds and/or inclusions

A heat source such as a magnetic induction/eddy current generator remotely heats a region of a surface of a test structure to a desired depth. For example, the frequency of the heating source can be varied to heat to the desired depth. A thermal sensor senses temperature changes in the heated region as a function of time. A computer compares these sensed temperature changes with calibration standards of a similar sample having known disbond and/or inclusion geography(ies) to analyze the test structure. A plurality of sensors can be arranged linearly to sense vector heat flow.

Heyman, Joseph S.↗

Quantitative Characterization of Structural and Mechanical Properties of Boron Nitride Nanotubes in High Temperature Environments

The structural stability and mechanical integrity of boron nitride nanotubes (BNNTs) in high temperature environments are of importance in pursuit of their applications that are involved with extreme thermal processing and/or working conditions, but remain not well understood. In this paper, we perform an extensive study of the impacts of high temperature exposure on the structural and mechanical properties of BNNTs with a full structural size spectrum from nano- to micro- to macro-scale by using a variety of in situ and ex situ material characterization techniques. Atomic force microscopy (AFM) and high resolution transmission electron microscopy measurements reveal that the structures of individual BNNTs can survive at up to 850 °C in air and capture the signs of their structural degradation at 900 °C or above. In situ Raman spectroscopy measurements reveal that the BN bonds in BNNT micro-fibrils undergo substantial softening at elevated temperatures of up to 900 °C. The AFM-based nanomechanical compression measurements demonstrate that the mechanical integrity of individual BNNTs remain intact after being thermally baked at up to 850 °C in air. The studies reveal that BNNTs are structurally and mechanically stable materials in high temperature environments, which enables their usages in high temperature applications.

Chen, Xiaoming↗

Elucidation of Structure and Physical Properties of Pyrolytic Sugar Oligomers Derived from Cellulose Depolymerization/Dehydration Reactions: A Density Functional Theory Study

Fast pyrolysis of lignocellulosic materials is a promising research area to produce renewable fuels and chemicals. Dehydration is known to be among the most important reaction families during cellulose pyrolysis; water is the most important product. Together with water, dehydration reactions also form a range of poorly known oligomer species of varying molecular sizes, often collected as part of bio-oil water-soluble (WS) fraction. In this work, we used electronic structure calculations to evaluate the relative thermodynamic stabilities of several oligomer species resulting from up to three consecutive dehydration events from cellulose depolymerization intermediates. A library of the thermodynamically favored candidate molecular structures was compiled. Results revealed that most of the water molecules are eliminated from the non-reducing end, forming thermodynamically more stable conjugated compounds. This is consistent with results reported by other researchers in literature where dehydration reactions occur preferably at the non-reducing ends of oligomers. The physical-chemical properties of the proposed structures were estimated using quantitative structure-property relationships (QSPRs) and quantitative property-property relationships (QPPRs). The anhydro-sugars derived from cellulose are often blamed for coke formation during bio-oil hydrotreatment. Understanding their chemical structure could help to develop rational strategies to mitigate coke formation. Furthermore, the thermo-physical properties reported (boiling point, melting point, Gibb’s free energy of formation, enthalpy of formation, and solubility parameters among others) are also fundamental to conducting first principle engineering calculations to design and analyze new pyrolysis reactors and bio-oil up-grading units.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantitative NDE of Composite Structures at NASA

The use of composite materials continues to increase in the aerospace community due to the potential benefits of reduced weight, increased strength, and manufacturability. Ongoing work at NASA involves the use of the large-scale composite structures for spacecraft (payload shrouds, cryotanks, crew modules, etc). NASA is also working to enable the use and certification of composites in aircraft structures through the Advanced Composites Project (ACP). The rapid, in situ characterization of a wide range of the composite materials and structures has become a critical concern for the industry. In many applications it is necessary to monitor changes in these materials over a long time. The quantitative characterization of composite defects such as fiber waviness, reduced bond strength, delamination damage, and microcracking are of particular interest. The research approaches of NASA's Nondestructive Evaluation Sciences Branch include investigation of conventional, guided wave, and phase sensitive ultrasonic methods, infrared thermography and x-ray computed tomography techniques. The use of simulation tools for optimizing and developing these methods is also an active area of research. This paper will focus on current research activities related to large area NDE for rapidly characterizing aerospace composites.

Cramer, K. Elliott↗

Quantitative investigation of surface structure and interatomic potential with impact-collision ion scattering spectroscopy

Helium ion beam interactions with materials have important implications for magnetic confinement fusion, material modification, and helium ion microscopy. These interactions depend on the precise physics of how helium ions channel into the materials, which can vary greatly based on the local crystalline orientation. Here, we performed a dedicated experiment to investigate helium ion channeling in a well-characterized tungsten single crystal. Time-of-flight impact-collision ion scattering spectroscopy was used to obtain multi-angle maps of the backscattering intensity for 3 keV He + → W(111). We found that the backscattering intensity profile arising from helium ion channeling could be well described by a shadow cone analysis. This analysis revealed that subsurface W atoms as deep as the ninth monolayer contributed to the backscattering intensity profile. Binary collision approximation simulations were performed with MARLOWE to model the experimental maps with sufficient accuracy to allow for quantitative comparisons using reliability factors. These quantitative comparisons were applied to investigate how the W lattice structure and He–W interatomic potential affect the multi-angle maps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗