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Study of the agreement between binary neutron star ejecta models derived from numerical relativity simulations
Neutron star mergers have recently become a tool to study extreme gravity, nucleosynthesis, and the chemical composition of the Universe. To date, there has been one joint gravitational and electromagnetic observation of a binary neutron star merger, GW170817, as well as a solely gravitational observation, GW190425. In order to accurately identify and interpret electromagnetic signals of neutron star mergers, better models of the matter outflows generated by these mergers are required. We compare a series of ejecta models to see where they provide strong constraints on the amount of ejected mass expected from a system, and where systematic uncertainties in current models prevent us from reliably extracting information from observed events. We also examine 2396 neutron star equations of state compatible with GW170817 to see whether a given ejecta mass could be reasonably produced with a neutron star of said equation of state, and whether different ejecta models provide consistent predictions. We find that the difference between models is often comparable to or larger than the error generally assumed for these models, implying better constraints on the models are needed. In conclusion, we also note that the extrapolation of outflow models outside of their calibration window, while commonly needed to analyze gravitational wave events, is extremely unreliable and occasionally leads to completely unphysical results.
Next generation modeling of microbial souring – Parameterization through genomic information
Biogenesis of hydrogen sulfide (H 2 S) (microbial souring) has detrimental impacts on oil production operations and can cause health and safety problems. Understanding the processes that control the rates and patterns of sulfate reduction is crucial in developing a predictive understanding of reservoir souring and associated mitigation processes. This work demonstrates an approach to utilize genomic information to constrain the biological parameters needed for modeling souring, providing a pathway for using microbial data derived from oil reservoir studies. Minimum generation times were calculated based on codon usage bias and optimal growth temperatures based on the frequency of amino acids. We show how these derived parameters can be used in a simplified multiphase reactive transport model by simulating the injection of cold (30 °C) seawater into a 70 °C reservoir, modeling the shift in sulfate reducing microorganisms (SRM) community composition, sulfate and sulfide concentrations through time and space. Finally, we explore the question of necessary model complexity by comparing results using different numbers of SRM. Simulations showed that the kinetics of a SRM community consisting of twenty-five SRM could be adequately represented by a reduced community consisting of nine SRM with parameter values derived from the mean and standard deviations of the original SRM.
Adsorption Thermodynamics for Process Simulation
Adsorption has rapidly evolved in recent decades and is an established separation technology extensively practiced in gas separation industries and others. However, rigorous thermodynamic modeling of multicomponent adsorption equilibrium remains elusive, and industrial practitioners rely heavily on expensive and time-consuming trial-and-error pilot studies to develop adsorption units. Here, this article highlights the need for rigorous adsorption thermodynamic models and the limitations and deficiencies of existing models such as the extended Langmuir isotherm, dual-process Langmuir isotherm, and adsorbed solution theory. It further presents a series of recent advances in the generalization of the classical Langmuir isotherm of single-component adsorption by deriving an activity coefficient model to account for the adsorbed phase adsorbate–adsorbent interactions, substituting adsorbed phase adsorbate and vacant site concentrations with activities, and extending to multicomponent competitive adsorption equilibrium, both monolayer and multilayer. Requiring a minimum set of physically meaningful model parameters, the generalized Langmuir isotherm for monolayer adsorption and the generalized Brunauer–Emmett–Teller isotherm for multilayer adsorption address various thermodynamic modeling challenges including adsorbent surface heterogeneity, isosteric enthalpies of adsorption, BET surface areas, adsorbed phase nonideality, adsorption azeotrope formation, and multilayer adsorption. Also discussed is the importance of quality adsorption data that cover sufficient temperature, pressure, and composition ranges for reliable determination of the model parameters to support adsorption process simulation, design, and optimization.
Holographic Formation of Diffraction Elements for Transformation of Light Beams in Liquid Crystal – Photopolymer Compositions
A theoretical model of holographic formation of diffractive optical elements for transformation of light beam field into Bessel-like fields in liquid crystal – photopolymer (LC–PPM) composite materials with a dyesensitizer is developed. Results of numerical modeling of kinetics ofvariation of the refractive index of a material in the process of formation with different relationships between the photopolymerization rates and diffusion processes are presented. Based on the results of numerical simulation, it is demonstrated that when the photopolarization process dominates, the diffractive element being formed is distorted. This leads to a change in the light field distribution at its output and consequently, to ineffective transformation of the reading beam. Thus, the necessity of optimizing of the recording conditions and of the prepolymeric composition to increase the transformation efficiency of light beam fields is demonstrated.
Highly-Cyclable Room-Temperature Phosphorene Polymer Electrolyte Composites for Li Metal Batteries
Despite significant interest toward solid-state electrolytes owing to their superior safety in comparison to liquid-based electrolytes, sluggish ion diffusion and high interfacial resistance limit their application in durable and high-power density batteries. Here, a novel quasi-solid Li + ion conductive nanocomposite polymer electrolyte containing black phosphorous (BP) nanosheets is reported. The developed electrolyte is successfully cycled against Li metal (over 550 h cycling) at 1 mA cm -2 at room temperature. The cycling overpotential is dropped by 75% in comparison to BP-free polymer composite electrolyte indicating lower interfacial resistance at the electrode/electrolyte interfaces. Molecular dynamics simulations reveal that the coordination number of Li + ions around (trifluoromethanesulfonyl)imide (TFSI - ) pairs and ethylene-oxide chains decreases at the Li metal/electrolyte interface, which facilitates the Li + transport through the polymer host. Here, density functional theory calculations confirm that the adsorption of the LiTFSI molecules at the BP surface leads to the weakening of N and Li atomic bonding and enhances the dissociation of Li + ions. This work offers a new potential mechanism to tune the bulk and interfacial ionic conductivity of solid-state electrolytes that may lead to a new generation of lithium polymer batteries with high ionic conduction kinetics and stable long-life cycling.
Modification of Inclusions in Molten Steel by Mg-Ca Transfer from Top Slag: Experimental Confirmation of the ‘Refractory-Slag-Metal-Inclusion (ReSMI)’ Multiphase Reaction Model
High-temperature experiments and Refractory-Slag-Metal-Inclusion (ReSMI) multiphase reaction simulations were carried out to determine the effect of the ladle slag composition on the formation behavior of non-metallic inclusions in molten steel. Immediately after the slag-metal reaction, magnesium migrated to the molten steel and a MgAl{sub 2}O{sub 4} spinel inclusion was formed due to a reaction between magnesium and alumina inclusions. However, the spinel inclusion changed entirely into a liquid oxide inclusion via the transfer of calcium from slag to metal in the final stage of the reaction. Calcium transfer from slag to metal was more enhanced for lower SiO{sub 2} content in the slag. Consequently, the spinel inclusion was modified to form a liquid CaO-Al{sub 2}O{sub 3}-MgO-SiO{sub 2} inclusion, which is harmless under steelmaking conditions. The modification reaction was more efficient as the SiO{sub 2} content in the slag decreases.
Thermodynamic modeling of countercurrent chemical looping reverse water gas shift process for redox material screening
The reverse water gas shift (RWGS) reaction is a key pathway for CO 2 utilization, particularly within Power-to-X process chains aimed at sustainable fuel and chemical production. Countercurrent chemical looping (CL-RWGS) using non-stoichiometric oxides can overcome equilibrium limitations of conventional RWGS reactors, enabling significantly higher CO 2 conversions. However, modeling the limiting performance of such systems is challenging due to their multiphase nature and coupled spatial and temporal variation in chemical composition. In this work, we present a discretized batch equilibrium model that simulates CL-RWGS reactors as a series of localized equilibrium exchanges between gas and solid elements. The model is numerically stable, computationally efficient, and free of kinetic source terms, making it well-suited for parametric studies and system-level integration. It is validated against established convection–diffusion models and shown to predict reasonable upper bounds on experimental results. Application of the model to a range of oxygen carrier materials identifies cerium–zirconium solid solutions, particularly Ce 0.80 Zr 0.20 O 2 , as a promising class offering superior oxygen storage characteristics compared to state-of-the-art La 0.6 Sr 0.4 FeO 3 . This framework provides a robust platform for materials screening, reactor sizing, and performance optimization in chemical looping systems. The model implementation is available as open-source software to support further research and development.
An indirect approach to optimize the reaction rates of thermal NO formation for diesel engines
With stringent emission regulations, it has become more important for modern diesel engine manufacturers to accurately predict engine-out nitrogen oxide (NO x ) emissions across a wide range of operating conditions. Thermal NO is the major source of engine-out NO x in modern diesel engines. For thermal NO formation, several earlier studies have recommended the forward and reverse reaction rate coefficients of the rate-limiting reaction (O + N 2 ⇌ NO + N). However, due to deficiencies in sub-models and inadequacies of reduced chemical mechanisms to represent diesel combustion, these recommended values more often than not need to be adjusted in reduced order combustion models to accurately predict engine-out NO x . Hence, in this work a systematic and computationally efficient approach has been proposed to streamline the process of determining the optimum reaction rate coefficients. Here, to develop the optimization approach, four different production diesel engines with different operating conditions in terms of speed, load, and exhaust-gas recirculation have been considered. Numerical simulations have been performed using a detailed zero-dimensional velocity-composition-frequency transported probability density function (0D-VCF-tPDF) model that uses hundreds of notional particles to capture in-cylinder stratification. Four different combinations of hydrocarbon and NO x chemical mechanisms were used to represent chemistry. It was found that for the rate-limiting reaction, the pre-exponent factors (A f1 , A r1 ) and activation energies (E A,f1 , E A,r1 ) of the forward and reverse reaction rates follow a linear band in A f1 - E A,f1 and A r1 - E A,r1 space where predicted engine-out NO x match the measured values closely. By encompassing such bands from different engines and considering constraints on activation energies, a reduced search domain of pre-exponent factors and activation energies was constructed that is expected to be applicable to any diesel engine. Eventually, computationally efficient three-line and one-line search approaches were proposed to determine the optimum values of the pre-exponent factors and activation energies that led to a minimum error between measured and predicted engine-out NO x . Finally, these three-line and one-line NO x optimization approaches were applied to a fifth production diesel engine for which the 0D-VCF-tPDF model showed a very good predictive performance in terms of predicting peak pressure, 50% burn rate, and engine-out NO x when compared to measured and 3D-CFD values.
Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows
This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physicsinformed clustering strategy centered on the K-means framework. The method allows for the injection of underlying physical knowledge into the clustering procedure through a distance function modification: instead of leveraging conventional Euclidean distance vectors, the JSKmeans procedure operates on distance vectors scaled by matrices obtained from dynamical system Jacobians evaluated at the cluster centroids. The goal of this work is to show how the JSKmeans algorithm - without modifying the input dataset - produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves. The algorithm is demonstrated on a complex reacting flow simulation dataset (a channel detonation configuration), where the dynamics in the thermochemical composition space are known through the highly nonlinear and stiff Arrhenius-based chemical source terms. Interpretations of cluster partitions in both physical space and composition space reveal how JSK-means shifts clusters produced by standard K-means towards regions of high chemical sensitivity (e.g., towards regions of peak heat release rate near the detonation reaction zone). Furthermore, the findings presented here illustrate the benefits of utilizing Jacobian-scaled distances in clustering techniques, and the JSK-means method in particular displays promising potential for improving former partition-based modeling strategies in reacting flow (and other multi-physics) applications.
A reactive electrochemomechanical theory for growth and remodeling of polyelectrolyte hydrogels and application to dynamic polymerization of $\text{DNA}$ hydrogels
Here this study develops a framework for growth and remodeling of active polyelectrolyte hydrogels that accounts for effects of compositional changes on the mechanical response. By developing a reactive electrochemomechanical theory, thermodynamical constraints upon reactive and remodeling processes are elucidated within a general framework that allows any number of chemical reactions to evolve the response of the gel and transfer mass and charge between constituents. Fully coupled, nonlinear constitutive relations are adopted for molar fluxes, allowing exploration of effects including cross-diffusion, electrophoresis, and electro-osmosis. A robust finite element implementation is developed in the open source FEBio software (febio.org) by exploiting an equivalence between electrochemomechanics and mixture theory. The implementation is verified against analytical solutions for free swelling, and a proper reduction to a prior chemomechanical theory is demonstrated for neutral gels swollen only by a solvent with no solutes. The theory and implementation are then applied to model the tunable large swelling achieved through dynamic polymerization of DNA crosslinkers seen in our recently developed experimental hydrogel system (Cangialosi et al., 2017). A novel constitutive model for reaction-driven evolution of the locking stretch λ L in a non-Gaussian mechanical free energy was developed, where the increasing concentration of DNA crosslinkers makes further swelling energetically favorable. With a single free parameter, excellent agreement was found between measured and predicted equilibrium swelling ratios. This study demonstrated the ability to extend the electrochemomechanical framework to include chemical reactions and composition-aware constitutive models, and showed that development of reactive models allows simulation of complex dynamic polymerization phenomena not treated before. The theoretical frame here can be further expanded in scope to incorporate additional non-ideal and nonlinear phenomena.
Application of the immobilized low-activity waste glass corrosion model to the static dissolution of 24 statistically-designed alkali-borosilicate waste glasses
Glass corrosion models that capture the complex mechanisms of the glass-water reaction enable the prediction of nuclear waste glass durability in disposal scenarios. Parameterization of such models is challenging because of the need to capture changes in corrosion behavior with time, reaction conditions, and glass composition. In this report we describe and employ the immobilized low activity waste (ILAW) glass corrosion model (IGCM) in geochemical simulations of static dissolution tests, at two temperatures (40 °C and 90 °C), for a matrix of 24 enhanced low activity waste (eLAW) glasses statistically designed to cover a processable composition space defined by 8 major glass components (Al 2 O 3 , B 2 O 3 , CaO, Na 2 O, SiO 2 , SnO 2 , ZrO 2 , and Others as defined in the text). The IGCM includes a first-order chemical affinity term and an ion-exchange term that represents the net exchange of Na+ ions in the pristine glass with protons in aqueous solution. Constant, time-dependent, and time- and pH-dependent functional forms of the ion-exchange term are evaluated to reproduce the change in corrosion behavior with time in saturated, static dissolution tests. The agreement with measured aqueous concentrations of the main glass components (B, Na, and Si) improved significantly upon addition of a time-dependent term, which therefore constitutes a simple representation of the glass-water reaction progress. Due to the limited changes in pH in the static dissolution tests, past a short initial period of rapid increase, addition of a pH dependent term did not appreciably improve the fits, indicating that comprehensive model parameterization requires more than one type of glass corrosion test to capture a wide range of solution chemistries. IGCM parameters were found to be dependent on glass composition, and the parameter sets generated in this work will enable the development of composition–parameter correlation models that offer the promise of predicting IGCM parameters, and thus glass corrosion behavior, solely based on glass composition.
Predicting phase behavior in high entropy and chemically complex alloys
The interest in high entropy alloys and other metallic compounds with four or more elements at near-equiatomic ratios has drawn attention to the ability to rapidly predict phase behavior of these complex materials, particularly where existing thermodynamic data are lacking. In this paper, we discuss aspects of this from the point of view of predicting without utilizing (or fitting) experimental data. Of particular interest are heuristic approaches that provide prediction of single-phase compositions, more rigorous approaches that tackle the thermodynamics from a more fundamental point of view, and simulation approaches that provide further insight into the behaviors. This paper covers cases of all three of these, in order to examine the strengths and weaknesses of each approach, and to indicate directions where these may be utilized and improved upon. Of particular interest is moving beyond “which composition may form a solid solution,” to recognizing the importance of underlying thermodynamic realities that affect the temperature- and composition-dependent transformations of these materials.
Optimizing a detection system for fissile material in nuclear disarmament verification
In arms control treaties, verification plays a crucial role in detecting non-compliance, deterring future violations, and building trust between state parties. Neutron interrogation that induces fission reactions in fissile isotopes and measures the resulting fission neutrons, could be employed for this purpose. This study aims to develop a system which can determine the presence of fissile material while intrinsically protecting information. In this paper, we focus on optimizing the system for discriminating between an enriched uranium block from a depleted uranium (DU) block. The system was built and we report on benchmark measurements with DU and 16% enriched uranium blocks. Furthermore, the Excalibur (Experiment for Calibration with Uranium) neutron source, a neutron spectrometer (redBubble Technology Industries (BTI) N-Probe), and superheated droplet detectors were used for these measurements. MCNP simulations provided insights into detector responses to fissile materials with varying isotopic compositions, confirming that the system functioned as designed.
Combining Pre- and Post-Nucleation Trajectories for the Synthesis of High FAU-Content Faujasite Nanocrystals from Organic-Free Sols
The effects of synthesis conditions on the FAU/EMT content and the size of nanocrystals, formed from inorganic aluminosilicate sols, were investigated. High resolution transmission electron microscopy imaging and comparison of experimental X-ray diffraction patterns with simulations demonstrated that all materials made starting from synthesis mixtures in the composition range (1.8-33) SiO2: 1 Al2O3: (2.7-33) Na2O: (41-1000) H2O contain FAU/EMT intergrowths. Compositions with low water content increase the FAU fraction up to 0.8 but the crystal size exceeds 100 nm. Extension of the higher FAU purity to nanocrystals was achieved only by first mixing the sol at high water content compositions that favor nanocrystal formation and then - after a certain time - lowering by freeze-drying the water to levels favoring the formation of FAU. Cryogenic transmission electron microscopy and small angle X-ray scattering from representative optically clear and colloidally stable precursor sols (aged and crystallized at ambient temperature) reveal the formation of amorphous aggregates before the detection of crystals, in agreement with earlier findings and an existing model for the aggregative growth of the zeolite MFI. The presence of these amorphous aggregates coincides with the aforementioned state of sol that preserves the original trajectory towards nano-crystals after the pronounced reduction of water content by freeze-drying. If water reduction by freeze-drying is applied earlier (before the detection of amorphous aggregates), the sol follows the low water content trajectory towards larger crystals. Despite this memory effect, the sol at this stage is still agnostic towards FAU or EMT formation, the relative content of which is dominantly determined by the final water content. These findings demonstrate that it is possible to combine the effects of pre-and post-nucleation sol composition to steer crystal size and crystal structure, respectively. They confirm precursor nanoparticle evolution, while they emphasize the importance of solution phase composition at both pre- and post-nucleation stages of aggregative crystal growth.
Defect-Enabled Phase Programming of Transition Metal Dichalcogenide Monolayers
The ability to tune the local electronic transport properties of group VI transition metal dichalcogenide (TMD) monolayers by strain-induced structural phase transformations (“phase programming”) has stimulated much interest in the potential applications of such layers as ultrathin programmable and dynamically switchable nanoelectronics components. In this manuscript, we propose a new approach toward controlling TMD monolayer phases by employing macroscopic in-plane strains to amplify heterogeneous strains arising from tailored, spatially extended defects within the monolayer. Furthermore, the efficacy of our proposed approach is demonstrated via numerical simulations of emerging domains localized around arrays of holes, grain boundaries, and compositional heterointerfaces. Quantitative relations between the macroscopic strains required, spatial resolution of domain patterns, and defect configurations are developed. In particular, the introduction of arrays of holes is identified as the most feasible phase programming route.
Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport
MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.
Formation of Bimetallic Nanoparticles via Exsolution Using a Reducible Metal Oxide Capping Layer
Bimetallic nanoparticles are promising catalysts that can improve performance in heterogeneous catalysis and solid-state electrochemistry. Exsolution is a useful method for forming such nanoparticles; however, it is limited by the elements present within the host oxide lattice. Here, in this work, we develop and demonstrate a strategy to form bimetallic particles from La 0.5 Sr 0.5 Ti 0.94 Ni 0.06 O 3 (LSTN) exsolution and using a reducible SnO 2 capping layer, expanding the range of elements available for bimetallic nanoparticle formation. Using this capping layer strategy, we formed nickel–tin (Ni 0 –Sn 0 ) bimetallic nanoparticles via exsolution. We used in situ near-ambient pressure X-ray photoelectron spectroscopy to monitor surface chemical changes during exsolution, showing that first, SnO 2 volatilized. This SnO 2 loss exposed the perovskite surface of LSTN to reducing conditions, which induced Ni exsolution, and compounded with SnO 2 reduction led to the formation of bimetallic Ni 0 –Sn 0 particles. To evaluate the associated microstructural evolution, we measured grazing incidence small-angle X-ray scattering (GISAXS), which confirmed the loss of the SnO 2 capping layer, and scattering simulations suggested the formation of bimetallic particles. We confirmed the bimetallic nanoparticle composition and morphology by Auger spectroscopy and scanning transmission electron microscopy. The resulting bimetallic nanoparticles were smaller and more thermally stable than the monometallic Ni counterparts on LSTN. This capping layer and exsolution approach allow synthesizing multimetallic nanoparticles and can be applied to other reducible metal oxides and perovskite hosts, broadening the compositional space for advanced catalytic materials.