Membrane-based reactive separations for process intensification during power generation
Not provided.
Chemistry↗
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Monte Carlo Simulation of Borosilicate Glass Dissolution using Molecular Dynamics-Generated Glass Structures
Atomic-scale insights into the dissolution mechanisms of borosilicate glasses are critical to the development of the waste form corrosion models that inform performance assessments of nuclear waste disposal facilities. Monte Carlo (MC) simulations have been instrumental in advancing the state of knowledge of borosilicate glass dissolution; however, all such simulations to date have relied on lattices to represent the amorphous glass structure, thereby potentially introducing artifacts. Therefore, a new approach, referred to as the amorphous MC approach, has been developed whereby glass structures generated from molecular dynamics simulations are used as starting points for MC simulations of glass dissolution. MC simulations of sodium borosilicate glasses that covered a wide compositional range were performed and revealed correlations between dissolution rate and structural features of the glasses. Notably, the lattice MC approach predicted the fraction of tetrahedral boron to have a lesser influence on the dissolution rate than computed with the amorphous MC approach. The lattice MC simulations also overestimated the dissolution rate for a given glass composition. These findings highlight the importance of using truly amorphous structures in MC simulations of glass corrosion.
Kerisit, Sebastien N.↗
A novel approach for adaptive skeleton toolpath generation
Industry 4.0 is revolutionizing manufacturing through the integration of automation and real-time data sharing in cyber-physical systems. At the forefront of this revolution is large-format additive manufacturing. In large-format printing, parts are often designed to be an even number of beads wide to produce a completely dense part. However, voids can still arise. This is often due to the part not being an even number of bead widths wide in some areas, or in geometry containing acute angles, as the process of generating closed contours cannot completely fill the space. Voids can be tolerated in smaller models, but in large-format additive manufacturing they may cause mechanical defects. To fill these voids, open loop paths called skeletons are often used, but they are typically limited by the physical constraints defined in the slicing software. To address this, researchers at Oak Ridge National Laboratory have extended skeleton toolpaths via an adaptive methodology. These adaptive skeletons were found to better fill void spaces through manipulation of physical parameters of the build process and were calculated as part of the slicing process.
42 ENGINEERING↗
An ultimate storage ring lattice with vertical emittance generated by damping wigglers
Not Available
Huang, Xiaobiao↗
Magnetic-field generation by the ablative nonlinear Rayleigh–Taylor instability
Experiments reporting magnetic-field generation by the ablative nonlinear Rayleigh–Taylor (RT) instability are reviewed. The experiments show how large-scale magnetic fields can, under certain circumstances, emerge and persist in strongly driven laboratory and astrophysical flows at drive pressures exceeding one million times atmospheric pressure.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation
The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.
ChemBERTa↗
Flood Inundation Generation Mechanisms and Their Changes in 1953-2004 in Global Major River Basins
Despite the serious threats posed by floods, the driving mechanisms of floods are still not well understood. Here, we apply a physically-based inundation model coupled with a river routing model (Model for Scale Adaptive River Transport, MOSART) within the Energy Exascale Earth System Model (E3SM) framework to investigate flood inundation dynamics. After calibration using observed streamflow and satellite-derived flood extent, the model is used to simulate global flood inundation from 1953 to 2004. The mean date and seasonality of annual maximum flood, defined based on flood extent, exhibit significant regional differences across 16 major basins. Generally, soil moisture and monthly maximum daily rainfall (MMR) are the dominant drivers of flood in tropical basins while monthly maximum daily snowmelt (MMS) is the dominant driver in high latitude basins. From 1953-1982 to 1975-2004, significant changes in flood generation mechanisms are found in some basins such as Amazon, Lena, Yenisey, and Kolyma. Analysis of the rainfall seasonality and water balance at grid scale reveals a stronger rainfall seasonality in the Amazon during the later period that increases the synchrony between extreme rainfall and wet soil. With high antecedent soil moisture coinciding with rainfall, MMR contributes more to flood in the later period. Fewer extreme rainfall events and increasing soil moisture reduced the contribution of MMR and increased the role of MMS in floods in the Lena and Yenisey basins, respectively. Lastly, increased soil moisture and frequency of large MMS reduced the contribution of the latter to floods in the Kolyma basin.
Mao, Yuna↗
Automated Symbolic Upscaling: 2. Model Generation for Extended Applicability Regimes
Abstract In this second part of the two paper series, we detail an algorithmic procedure for systematically implementing the generalized closure form strategy presented in Part 1. This strategy extends the applicability of homogenized models with respect to classical homogenization theory, as demonstrated in Part 1 where upscaled models are rigorously derived in moderately reactive physical regimes. After encoding the algorithm into Symbolica, an automated upscaling framework, we upscale two reactive mass transport problems and numerically validate the resulting nonlinear homogenized models by showing the absolute error estimates predicted by homogenization theory are satisfied. In both problems, nontrivial closure forms and closure problems are automatically formulated using the encoded strategy with no human interaction, nor prior knowledge regarding the closure required for the systems. We hope these demonstrations spark further interest in automated analytical frameworks for multiscale modeling, as such capabilities are invaluable for generating rigorous multiscale models of complex phenomena in porous media.
Pietrzyk, Kyle↗
Airborne soil organic particles generated by precipitation
Airborne organic particles play a critical role in the Earth’s climate1, public health2, air quality3, and hydrological and carbon cycles4. These particles exist in liquid, amorphous semi-solid, or solid (glassy) phase states depending on their composition and ambient conditions5. However, sources and formation mechanisms for semi- solid and solid organic particles are poorly understood and typically neglected in atmospheric models6. Here we report field evidence for airborne solid organic particles generated by a “raindrop” mechanism7 pertinent to atmosphere – land surface interactions (Fig. 1). We find that after rain events at Southern Great Plains, Oklahoma, USA, submicron solid particles, with a composition consistent with soil organic matter, contributed up to 60% of atmospheric particles in number. Subsequent experiments indicate that airborne soil organic particles are ejected from the surface of soils caused by intensive rains or irrigation. Our observations suggest that formation of these particles may be a widespread phenomenon in ecosystems where soils are exposed to strong, episodic precipitation events such as agricultural systems and grasslands8. Chemical imaging and micro-spectroscopy analysis of their physico-chemical properties suggests that airborne soil organic particles may have important impacts on cloud formation and efficiently absorb solar radiation and hence, are an important type of particles.
Environmental Molecular Sciences Laboratory↗
A systematic approach to generating accurate neural network potentials: the case of carbon
Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.
Chemistry↗
Bridging the academic and industrial metrics for next-generation practical batteries
Batteries have shaped the modern world to an extent far beyond that of other technologies. This success is the result of the intense collaboration between Academia and Industry that took shape in the past several decades culminating with the advent and improvements of rechargeable Li-ion batteries. However, as applications are getting more demanding, stunted growth in the performance of commercial batteries risks to slow down the adoption of important technologies, such as electric vehicles. Yet, the scientific literature is full of reports describing material designs with allegedly superior performances. A large gap needs to be filled if we wish these lab-based achievements reach commercialization. In this Perspective we discuss some of the most relevant testing parameters that are often overlooked in academic literature, but that are critical for the practical applicability of lab-scale research. Metrics, such as anode energy density, voltage hysteresis, mass of the non-active cell components and anode/cathode mass ratio, will be explained and recommendation for future reporting will be made at the end of each section. We hope that this Perspective, together with other similar guiding principles that have recently started to emerge, will aid the transition from lab-scale research to next-generation practical batteries
Cao, Yuliang↗
Spatial regulation of a common precursor from two distinct genes generates metabolite diversity
We have demonstrated that spatial regulation of the same product from two distinct genes generates metabolite diversity.
59 BASIC BIOLOGICAL SCIENCES↗
Hybrid approach for estimating dynamic states of synchronous generators
Accurate and robust estimation of dynamic states is essential for monitoring and controlling a power grid. To achieve higher accuracy and robustness, this paper proposes a hybrid estimation approach to estimate the dynamic states of synchronous generators using multiple models, classified into a set of local models and a wide-area model. The proposed estimation approach, first, estimates dynamic states using the extended Kalman filter (EKF) based on a local model and a wide-area model. Then, at each time step, probability indexes, which quantify the likelihood of both local and wide-area models, are determined using hypothesis testing based on measurements innovations. Finally, using the weighted fix approach, the states estimated from the local and wide-area models are combined based on their probability indexes. Simulation studies using the two-area four-machine system shows that the proposed approach can improve estimation accuracy and increase robustness against model errors.
Akhlaghi, Shahrokh↗
Analyses of the plasma generated by laser irradiation on sputtered target for determination of the thickness used for plasma generation
In Brookhaven National Laboratory, laser ion source has been developed to provide heavy ion beams by using plasma generation with 1064 nm Nd:YAG laser irradiation onto solid targets. The laser energy is transferred to the target material and creates a crater on the surface. However, only the partial material can be turned into plasma state and the other portion is considered to be just vaporized. Since heat propagation in the target material requires more than typical laser irradiation period, which is typically several ns, only the certain depth of the layers may contribute to form the plasma. As a result, the depth is more than 500 nm because the base material Al ions were detected. On the other hand, the result of comparing each carbon thickness case suggests that the surface carbon layer is not contributed to generate plasma.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Efficient Raman generation in a waveguide: A route to ultrafast quantum random number generation
The inherent uncertainty in quantum mechanics offers a source of true randomness which can be used to produce unbreakable cryptographic keys. We discuss the development of a high-speed random number generator based on the quantum phase fluctuations in spontaneously initiated stimulated Raman scattering (SISRS). We utilize the tight confinement and long interaction length available in a Potassium Titanyl Phosphate waveguide to generate highly efficient SISRS using nanojoule pulse energies, reducing the high pump power requirements of the previous approaches. We measure the random phase of the Stokes output using a simple interferometric setup to yield quantum random numbers at 145 Mbps.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
An ITPA joint experiment to study runaway electron generation and suppression
Recent results from an ITPA joint experiment to study the onset, growth, and decay of relativistic electrons (REs) indicate that loss mechanisms other than collisional damping may play a dominant role in the dynamics of the RE population, even during the quiescent I{sub p} flattop. Understanding the physics of RE growth and mitigation is motivated by the theoretical prediction that disruptions of full-current (15 MA) ITER discharges could generate up to 10 MA of REs with 10–20 MeV energies. The ITPA MHD group is conducting a joint experiment to measure the RE detection threshold conditions on a number of tokamaks under quasi-steady-state conditions in which V{sub loop}, n{sub e}, and REs can be well-diagnosed and compared to collisional theory. Data from DIII-D, C-Mod, FTU, KSTAR, and TEXTOR have been obtained so far, and the consensus to date is that the threshold E-field is significantly higher than predicted by relativistic collisional theory, or conversely, the density required to damp REs is significantly less than predicted, which could have significant implications for RE mitigation on ITER.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Generation and focusing of electron beams with initial transverse-longitudinal correlation
In charged particle beams, one of the roles played by space charge is to couple the transverse and longitudinal dynamics of the beam. This can lead to very complex phenomena which are generally studied using computer simulations. However, in some cases models based on phenomenological or analytic approximations can provide valuable insight into the system behavior. In this paper, we employ such approximations to investigate the conditions under which all the slices of a space charge dominated electron beam with slowly varying current could be focused to a waist with the same radius and at the same location, independent of slice current, and show that this can be accomplished approximately if the initial transverse-longitudinal correlation introduced onto the beam by the electron gun is chosen to compensate for the transverse-longitudinal correlation introduced onto the beam in the drift section. The validity of our approximations is assessed by use of progressively more realistic calculations. We also consider several design elements of electron guns that affect the initial correlations in the beams they generate.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Slowing of Femtosecond Laser-Generated Nanoparticles in a Background Gas
The slowing of Pt nanoparticles in argon background gas was characterized by Rayleigh scattering imaging using a plume of nanoparticles generated by femtosecond laser through thin film ablation (fs-TTFA) of 20 nanometers-thick Pt films. The ablation was performed at threshold laser energy fluences for complete film removal to provide a well-defined plume consisting almost entirely of nanoparticles traveling with a narrow velocity distribution, providing a unique system to unambiguously characterize the slowing of nanoparticles during interaction with background gases. Nanoparticles of ~200 nm diameter were found to decelerate in background Ar gas with pressures less than 50 Torr in good agreement with a linear drag model in the Epstein regime. Based on this model, the stopping distance of small nanoparticles in the plume was predicted and tested by particle collection in an off-axis geometry, and size distribution analysis by transmission electron microscopy. These results permit a basis to interpret nanoparticle propagation through background gases in laser ablation plumes that contain mixed components.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗