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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Theory of ignition and burn propagation in inertial fusion implosions

A detailed analytic model is presented here to investigate the physics of burn propagation in inertially confined plasmas. The onset of ignition and burn propagation occurs when alpha heating of the hot spot causes rapid ablation of shell mass into the hot spot. This allows large energy gains to be achieved since most of the fuel mass is located in the shell. Here, we first present a comprehensive review of previous analytic models that have been used to describe the physics of hot-spot evolution and ignition; we then show that a proper description of a propagating burn wave requires a comprehensive model of hot spot and shell evolution that includes proper mass conservation in the shell, fusion reactivity, and fuel depletion. The analytic theory is in good agreement with detailed radiation-hydrodynamic simulations that predict the onset of burn propagation as occurring when the yield enhancement caused by alpha heating is between 15- and 25-fold, $f_α$ ~ 1.4, where $f_α$ = alpha energy deposited/hot-spot energy at bang time, and the hot-spot burnup fraction is approximately 2%. We show that the definition of ignition is not sensitive to the alpha-particle stopping power nor asymmetries provided that the absorbed fraction of alpha particles $θ_α$ is correctly accounted for. Finally, we use the results of 2-D simulations to show that even when $θ_α$ is small and unknown (as is true in hot spots with mid modes that have significant leakage of alpha particles into the surrounding cold bubbles), one can still relate the experimentally measureable parameter $\chi^{53}_α$ to the yield amplification and the burning-plasma parameter $Q^{\text{hs}}_α$ = alpha energy deposited/total input work delivered to the hot spot

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. Furthermore, we discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.

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

Chaconne: A Statistical Approach to Nonlocal Compression for Supervised Learning, Semi-Supervised Learning, and Anomaly Detection

This project developed a novel statistical understanding of compression analytics (CA), which has challenged and clarified some core assumptions about CA, and enabled the development of novel techniques that address vital challenges of national security. Specifically, this project has yielded the development of novel capabilities including 1. Principled metrics for model selection in CA, 2. Techniques for deriving/applying optimal classification rules and decision theory to supervised CA, including how to properly handle class imbalance and differing costs of misclassification, 3. Two techniques for handling nonlocal information in CA, 4. A novel technique for unsupervised CA that is agnostic with regard to the underlying compression algorithm, 5. A framework for semisupervised CA when a small number of labels are known in an otherwise large unlabeled dataset. 6. The academic alliance component of this project has focused on the development of a novel exemplar-based Bayesian technique for estimating variable length Markov models (closely related to PPM [prediction by partial matching] compression techniques). We have developed examples illustrating the application of our work to text, video, genetic sequences, and unstructured cybersecurity log files.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Laser Ablation Plasmas and Spectroscopy for Nuclear Applications

The development of measurement methodologies to detect and monitor nuclear-relevant materials remains a consistent and significant interest across the nuclear energy, nonproliferation, safeguards, and forensics communities. Optical spectroscopy of laser-produced plasmas is becoming an increasingly popular diagnostic technique to measure radiological and nuclear materials in the field without sample preparation, where current capabilities encompass the standoff, isotopically resolved and phase-identifiable (e.g., UO and UO[Formula: see text]) detection of elements across the periodic table. These methods rely on the process of laser ablation (LA), where a high-powered pulsed laser is used to excite a sample (solid, liquid, or gas) into a luminous microplasma that rapidly undergoes de-excitation through the emission of electromagnetic radiation, which serves as a spectroscopic fingerprint for that sample. This review focuses on LA plasmas and spectroscopy for nuclear applications, covering topics from the wide-area environmental sampling and atmospheric sensing of radionuclides to recent implementations of multivariate machine learning methods that work to enable the real-time analysis of spectrochemical measurements with an emphasis on fundamental research and development activities over the past two decades. Background on the physical breakdown mechanisms and interactions of matter with nanosecond and ultrafast laser pulses that lead to the generation of laser-produced microplasmas is provided, followed by a description of the transient spatiotemporal plasma conditions that control the behavior of spectroscopic signatures recorded by analytical methods in atomic and molecular spectroscopy. High-temperature chemical and thermodynamic processes governing reactive LA plasmas are also examined alongside investigations into the condensation pathways of the plasma, which are believed to serve as chemical surrogates for fallout particles formed in nuclear fireballs. Laser-supported absorption waves and laser-induced shockwaves that accompany LA plasmas are also discussed, which could provide insights into atmospheric ionization phenomena from strong shocks following nuclear detonations. Furthermore, the standoff detection of trace radioactive aerosols and fission gases is reviewed in the context of monitoring atmospheric radiation plumes and off-gas streams of molten salt reactors. Finally, concluding remarks will present future outlooks on the role of LA plasma spectroscopy in the nuclear community.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Relaxation drag history of shock accelerated microparticles

Experimental measurements of the displacements of shock accelerated microparticles from shortly after shock interaction to the particle relaxation time show time-dependent drag coefficients ( $C_{D}$ ) that are much higher than those predicted by quasi-steady and unsteady drag models. Nylon particles with mean diameter of $4~\unicode[STIX]{x03BC}\text{m}$ , accelerated by one-dimensional normal shocks (Mach number $M_{s}=1.2$ , 1.3 and 1.4), have measured $C_{D}$ values that follow a power-law behaviour. The drag is a function of the time-dependent Knudsen number, $Kn^{\ast }=M_{s}/Re_{p}$ , where the particle Reynolds number ( $Re_{p}$ ) is calculated using the time-dependent slip velocity. Also, some portion of the drag can be attributed to quasi-steady forces, but the total drag cannot be predicted by current unsteady force models that are based on the Basset–Boussinesq–Oseen equation and pressure drag. The largest contribution to the total drag is the unsteady component ( $C_{D,us}$ ) until the particle attains $Kn^{\ast }\approx 0.5{-}1.0$ , then the unsteady contribution decays. The quasi-steady component ( $C_{D,qs}$ ) increases almost linearly with $Kn^{\ast }$ , intersects the $C_{D,us}$ at $Kn^{\ast }\approx 2$ and becomes the primary contributor to the drag towards the end of the relaxation zone as $Re_{p}\rightarrow 0$ . Finally, there are currently no analytical models that are able to predict the nonlinear behaviour of the shock accelerated particles during the relaxation phase of the flow.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Erratum: “Beyond chemical accuracy in the heavy p-block: The first ionization potentials and electron affinities of Ga–Kr, In–Xe, and Tl–Rn” [J. Chem. Phys. 151, 024303 (2019)]

In Ref. 1 incorrect values for the spin-orbit effects on the electron affinities (EAs) of Ga and In were used in Table IV. The KRCI values shown in Table III of Ref. 1, -0.49 and -0.94 kcal/mol for Ga and In, respectively, should have been given. Correct use of these latter values results in final FPD EAs for Ga and In of 6.97 and 8.91 kcal/mol, respectively. The incorrect FPD values were also given in the Abstract and Conclusion sections of Ref. 1. In addition, the experimental EA for In shown in the last column of Table IV was incorrect and should be 8.85 kcal/mol (the actual value being 8.8534 but only two decimal places are displayed throughout this table). A complete replacement for Table IV is given below with the corrected values given in bold font. The corrected FPD and experimental EA values for In in particular changes some of the conclusions of Ref. 1, namely when it is stated both in the abstract and the main text (Sec. III.B and Conclusions) that the experimental EA values for all of the group 13 (Ga, In, and Tl) EAs are too large. This is obviously not true for the EA of In, where the actual FPD value and the experimental result of Walter et al. are in excellent agreement, to within 0.1 kcal/mol. The FPD EAs of the other two group 13 elements, Ga and Tl, differ from their experimental values by 2.5 and 1.3 kcal/mol, respectively, which is still outside of the estimated uncertainty limits of this FPD approach (conservatively ±0.5 kcal/mol), particularly for Ga, therefore the original conclusions for these two elements are largely unchanged.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Complexation of Tc(IV) with ${\text{SO}}_{4}^{2–}$ in NaCl Medium

We report on the experimental determination of the stability constant for the complexation of the Tc(IV) cation by ${\text{SO}}_{4}^{2–}$ at 1.0 mol kg -1 ionic strength (NaCl) and pcH 1.51 ± 0.05, using a solvent extraction method. This research represents a first step in determining unknown thermodynamic parameters for the complexation of Tc(IV) by simple polyatomic anions. Radiotracer studies were carried out with 99 Tc and 35 S, using both HDEHP and TOPO as extractants, to characterize the mechanism of the solvent extraction and to examine the extraction of the competing species, TcO(HSO 4 )+ and TcOSO 4 , into the organic phase. The apparent stability constant log 10 β app for the complexation of Tc(IV) by sulfate was found to be 1.13 ± 0.04.

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

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Various Low-Symmetry Phases in High- χ and Conformationally Asymmetric PDMS- b -PTFEA Copolymers

Since the discovery of the Frank–Kasper (FK) phase a decade ago, single-component block copolymer systems have offered unique opportunities to explore the structural and phase-transition properties of topologically close-packed FK phases composed of point-particle motifs. Here, we present the formation of complex low-symmetry phases and their thermodynamic stability using high-χ and high-ε linear diblock copolymers, where χ and ε are the Flory–Huggins interaction parameter and conformational asymmetry between the two blocks, respectively. In this work, a series of polydimethylsiloxane-b-poly(2,2,2-trifluoroethyl acrylate) (PDMS-b-PTFEA) copolymers are prepared by tuning the volume fraction ($f_{\text{PTFEA}}$) of a minor PTFEA block. The packing structures of PDMS-b-PTFEAs are mapped in the space of temperature versus $f_{\text{PTFEA}}$ using X-ray scattering measurements. In addition to the packing structures reported earlier, we identify another A15 phase at higher $f_{\text{PTFEA}}$ close to a hexagonally packed cylinder (HEX) phase and establish the phase sequence of HEX–A15–σ–C14–BCC with decreasing $f_{\text{PTFEA}}$. Theoretical calculation of relative free-energy densities confirms the experimental phase sequence of the composition-dependent sphere-packing structures. Interestingly, the domain sizes of three-dimensional (3D) sphere-packing structures are nearly temperature-invariant compared to that of a two-dimensional HEX structure, and we attribute this dimensional stability to the strong enthalpic effects in the formation of 3D packing structures and the geometrical characteristics of spherical domains. Our results demonstrate that the interaction parameter χ, together with the known role of conformational asymmetry ε, serves as the primary thermodynamic parameter for selecting and stabilizing various low-symmetry packing structures in sphere-forming linear diblock copolymer systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Student-Friendly Guide to Molecular Integrals

Preceding even the Hartree–Fock method, molecular integrals are the very foundation upon which quantum chemical molecular modeling depends. Discussions of molecular integrals are normally found only in advanced and technical texts or articles. The objective of the present article is to provide less experienced readers, or students in a physical/computational chemistry course, a thorough understanding of molecular integrals. Through a series of detailed Handouts, the student/reader can participate in the derivation of molecular integrals, and in turn implement them in computer code. Hartree–Fock theory is discussed in enough detail to motivate the molecular integrals and address such topics as the atomic orbital basis. An introduction to the programming language of choice, Python3, is provided, tailored toward developing the essential skills necessary for implementing molecular integrals. In conclusion, the article is intended to be useful not only to instructors of physical/computational chemistry, but also to any reader who has independently sought a primer on this elusive subject.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Particle acceleration in relativistic magnetic flux-merging events

Using analytical and numerical methods (fluid and particle-in-cell simulations) we study a number of model problems involving merger of magnetic flux tubes in relativistic magnetically dominated plasma. Mergers of current-carrying flux tubes (exemplified by the two-dimensional ‘ABC’ structures) and zero-total-current magnetic flux tubes are considered. In all cases regimes of spontaneous and driven evolution are investigated. We identify two stages of particle acceleration during flux mergers: (i) fast explosive prompt X-point collapse and (ii) ensuing island merger. The fastest acceleration occurs during the initial catastrophic X-point collapse, with the reconnection electric field of the order of the magnetic field. During the X-point collapse, particles are accelerated by charge-starved electric fields, which can reach (and even exceed) values of the local magnetic field. The explosive stage of reconnection produces non-thermal power-law tails with slopes that depend on the average magnetization $\unicode[STIX]{x1D70E}$ . For plasma magnetization $\unicode[STIX]{x1D70E}\leqslant 10^{2}$ the spectrum power-law index is $p>2$ ; in this case the maximal energy depends linearly on the size of the reconnecting islands. For higher magnetization, $\unicode[STIX]{x1D70E}\geqslant 10^{2}$ , the spectra are hard, $p<2$ , yet the maximal energy $\unicode[STIX]{x1D6FE}_{\text{max}}$ can still exceed the average magnetic energy per particle, ${\sim}\unicode[STIX]{x1D70E}$ , by orders of magnitude (if $p$ is not too close to unity). The X-point collapse stage is followed by magnetic island merger that dissipates a large fraction of the initial magnetic energy in a regime of forced magnetic reconnection, further accelerating the particles, but proceeds at a slower reconnection rate.

Physics↗
DOE OSTI2019

Materials design of perovskite solid solutions for thermochemical applications

Perovskites are excellent candidate materials as oxygen carriers in thermochemical processes. Due to their versatile composition, it is possible to fine-tune the perovskite's properties. Here, we present a method for the rational design of AMO 3– δ perovskite solid solutions with two different species on the Msite in order to tune their redox behavior. To account for the different ionic radii of different transition metal species M, two distinct ions are used in a solid solution on the A site, allowing tolerance-factor adjusted materials design. Using this methodology, we can create stable perovskites over a large range of different compositions. Leveraging the infrastructure of Materials Project, we calculate redox enthalpies for the reduction of over 240 of these $(\text {A}^{\prime}_x\text {A}^{\prime\prime}_{1-x})(\text {M}^{\prime}_y\text {M}^{\prime\prime}_{1-y})$O 3 perovskites to their brownmillerite phases based on density functional theory (DFT). We compare this data to experimentally measured data on thermodynamics of 24 of these materials to verify our theoretical framework. An empirical model is formulated for predicting the enthalpy and entropy changes as a function of the perovskites non-stoichiometry δ , which can be used to simulate the equilibrium composition as a function of temperature and oxygen partial pressure and to create a perovskite search engine based on an energetic analysis of the redox cycles. The data has been added as a contribution to MPContribs, which now includes publicly available user-controlled interactive graphs based on our theoretical and experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Joint galaxy–galaxy lensing and clustering constraints on galaxy formation

ABSTRACT We compare predictions for galaxy–galaxy lensing profiles and clustering from the Henriques et al. public version of the Munich semi-analytical model (SAM) of galaxy formation and the IllustrisTNG suite, primarily TNG300, with observations from KiDS + GAMA and SDSS-DR7 using four different selection functions for the lenses (stellar mass, stellar mass and group membership, stellar mass and isolation criteria, and stellar mass and colour). We find that this version of the SAM does not agree well with the current data for stellar mass-only lenses with $M_\ast \gt 10^{11}\, \mathrm{ M}_\odot$. By decreasing the merger time for satellite galaxies as well as reducing the radio-mode active galactic nucleus accretion efficiency in the SAM, we obtain better agreement, both for the lensing and the clustering, at the high-mass end. We show that the new model is consistent with the signals for central galaxies presented in Velliscig et al. Turning to the hydrodynamical simulation, TNG300 produces good lensing predictions, both for stellar mass-only (χ2 = 1.81 compared to χ2 = 7.79 for the SAM) and locally brightest galaxy samples (χ2 = 3.80 compared to χ2 = 5.01). With added dust corrections to the colours it matches the SDSS clustering signal well for red low-mass galaxies. We find that both the SAMs and TNG300 predict $\sim 50\, {{\ \rm per\ cent}}$ excessive lensing signals for intermediate-mass red galaxies with 10.2 < log10M*[M⊙] < 11.2 at $r \approx 0.6\, h^{-1}\, \text{Mpc}$, which require further theoretical development.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2018

H2OI95: A Stand-Alone Fortran Code for Evaluating the IAPWS-95 Equation-of-State Model for Water

H2OI95 is a stand-alone Fortran code for evaluating the IAPWS-95 equation-of-state model (Wagner and Pruss, 2002) for the thermodynamic properties of water. It further evaluates the corresponding thermochemical properties of water consistent with the CODATA recommendations (Cox et al., 1989). The IAPWS-95 model is based on a model equation for the dimensionless Helmholtz energy for which the primary variables are the inverse reduced temperature (τ = T cr /T) and reduced density ( δ= ρ/ρ cr ). Here T is the absolute temperature (K), ρ is density (kg/m 3 ), and the subscript “cr” refers to the critical point of water (647.096 K and 22.064 MPa pressure in this model, for which ρ cr is 322 kg/m 3 ). The code solves four basic types of problems, distinguished by the specified inputs: 1, Temperature (K) and density (ρ kg/m 3 ) or reduced density (δ) 2. Temperature (K) and pressure (MPa). 3. Temperature (K) on the saturation (liquid-vapor equilibrium) curve 4. Pressure (MPa) on the saturation curve Each type of problem is run using a corresponding input (text) file. All but the first type of problem require iteration. For example, to solve for desired temperature and pressure, the reduced density must be adjusted to give the desired pressure. Iteration is accomplished using the Newton-Raphson method, though the secant method is also used in solving the fourth type of problem. H2OI95 has been used to conduct numerical studies of convergence and the problem of multiple numerical solutions, only some of which correspond to valid results. Obtaining valid results depends mainly on appropriate choice of starting values. The default values in H2OI95 appear to consistently lead to generally desired results. With modification (not addressed here), H2OI95 can be used to support SUPCRT92 (Johnson et al., 1992) and similar codes that compute chemical thermodynamic properties of species and reactions over a wide range of temperature and pressure (273.16-1273K and 0-1000 MPa).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word’s contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus – VKontakte collected during the Russia-Ukraine crisis in 2014 – 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.

concept drift↗
DOE OSTI2024

Incorporation of rare earth elements $\text{Sc}$, $\text{Y}$ and $\text{La}$ into gibbsite

Rare earth elements (REE) are increasingly critical resources in technological applications, but the current understanding of their separation by uptake into minerals remains limited. We examined the adsorption and solubilization of scandium (Sc), yttrium (Y), and lanthanum (La) into aluminum hydroxide (gibbsite, Al(OH) 3 ). Based on spectroscopic investigations such as inductively coupled plasma-optical emission spectroscopy (ICP-OES) and time of flight-secondary ion mass spectrometry (ToF-SIMS), only Sc exhibited favorable incorporation and adsorption (0.66 and 0.06 atom%, respectively). Analysis of solid state 45 Sc magic angle spinning-nuclear magnetic resonance spectroscopy (MAS-NMR) indicated that Sc exhibits an octahedral coordination consistent with isolated substitutions for Al in the gibbsite structure, leading to a increase in lattice constants detectable by X-ray diffraction. Density functional theory (DFT) calculations reinforced this interpretation by reproducing the detected structural distortion as well as demonstrating the relatively favorable energetic basis for Sc incorporation into gibbsite by substitution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗