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At least 1,261 records · Page 70

Non-linear Wire Rope Isolator Model to Enhance Transportation Simulation of Fragile Equipment

Abstract Introduction Wire rope isolators are often used as means of vibration isolation for the transportation of fragile machine equipment. When deployed as suspension systems in a transportation scenario, a simple spring–damper model is often used to predict the dynamic loads that act on the structure being transported. However, this simple model has proved to be too simplistic to be used in the development of an accurate numerical model that can predict the vibration levels experienced by the inner components of fragile equipment during its transportation. This paper describes the experimental tests conducted on a wire rope isolator used for the transportation of the prototype SSR1 cryomodule. Materials and methods A hysteretic Bouc–Wen model has been used to analytically describe the force–deformation relationship of the wire rope isolators. The developed model of the isolator has been implemented in a larger model to simulate the actual transportation of the prototype SSR1 cryomodule, a section of the new PIP-II linear accelerator under construction at Fermilab. A series of multibody dynamic simulations with rigid and flexible components was used to numerically determine the acceleration of some critical components. Results and conclusions An actual experimental transportation was simulated using two numerical models: the developed Bouc–Wen model and a conventional spring–damper model. It is shown how the Bouc–Wen formulation of the isolator characteristics drastically improves the correspondence between experimental and simulated results if compared to a spring–damper model, especially in the range of 0–30 Hz which is the most critical for transportation problems.

42 ENGINEERING↗

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

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Mechanisms of Zn removal from water by amorphous geopolymer: Molecular-level insights from X-ray absorption spectroscopy, isotope fractionation, and surface complexation modeling

Porous geopolymers have attracted widespread attention as promising heavy metal adsorbents that can be synthesized from aluminosilicate solid wastes. However, the precise microstructural evidence for adsorbed heavy metals on geopolymers remains unclear due to the insensitivity of conventional characterization techniques on minerals with amorphous structure and surface disorder. Batch adsorption and column experiments coupled with X-ray absorption spectroscopy (XAS), Zn stable isotope, and surface complexation model (SCM) were employed to reveal the Zn removal mechanisms with coal fly ash porous geopolymer (CFAPG) at a molecular scale. The macroscopic kinetic and isothermal adsorption of Zn on CFAPG were well described by the pseudo-second-order model and Bi_Langmuir equation, respectively, indicating the presence of abundant heterogeneous active sites on the CFAPG surface. Further, two types of active sites on the CFAPG surface were identified by XAS coupled with Zn isotopes in batch experiments at pH <= 6.0: one is pH-dependent and associated with tetrahedral zinc coordi-nation, and the other is pH-insensitive and associated with octahedral zinc coordination; these sites were confirmed by the bidentate SCM as the variable charge site (surface complexation, >S-OH) and the permanent negative charge site (cation exchange, >X - ), respectively. Furthermore, the important contribution of surface co -precipitation besides surface complexation and cation exchange to the Zn adsorption on CFAPG was identified by XAS coupled with SCM in a flow-through column experiment at pH >6.0. These investigations provide a systemic understanding of the Zn adsorption mechanisms on CFAPG and an SCM reference for the application and prediction of geopolymers in heavy metal-contaminated water remediations.

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Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the In Situ Nature of the Active Site in Catalysis

Microkinetic modeling based on density functional theory (DFT) derived energetics is important for addressing fundamental questions in catalysis. The quantitative fidelity of microkinetic models (MKMs), however, is often insufficient to conclusively infer the mechanistic details of a specific catalytic system. Here, this can be attributed to a number of factors such as an incorrect model of the active site for which DFT calculations are performed, deficiencies in the hypothesized reaction mechanism, inadequate consideration of the surface environment under reaction conditions, and intrinsic errors in the DFT exchange-correlation functional. Despite these limitations, we aim at developing a rigorous understanding of the reaction mechanism and of the nature of the active site for heterogeneous catalytic chemistries under reaction conditions. By achieving parity between experimental and modeling outcomes through robust parameter estimation and by ensuring coverage-consistency between DFT calculations and MKM predictions, it is possible to systematically refine the mechanistic model and, thereby, our understanding of the catalytic active site in situ.

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Rational Design of Novel Biomimetic Sequence-Defined Polymers for Mineralization Applications

Silica biomineralization is a naturally occurring process, wherein organisms use proteins and other biological structures to direct the formation of complex, hierarchical nanostructures. Discovery and characterization of such proteins and their underlying mechanisms spurred significant efforts to identify routes for biomimetic mineralization that reproduce the exquisite shapes and size selectivities found in nature. A common strategy has been the use of short peptide sequences with chemistry mimicking those found in natural systems, such as the use of the silaffin-derived R5 peptide. While progress has been made using this approach, there are many limitations that have prevented breakthroughs in biomimicry. To advance our ability to use charged macromolecules for silica formation, we propose to use sequence-defined synthetic polymers known as peptoids, or N-substituted polyglycines, which present significant capability for the precise tuning of sequence and structure beyond what can often be achieved with peptides alone. This study presents a computationally predicted design of these polymers that leads to the controlled formation of silica nanomaterials. We investigate surface adsorption and the mineralization process through analysis of binding mechanisms and energetics of the R5 system. Next, we synthesized two R5-inspired peptoids and validated our prediction in the design of mineralization polymers through characterization using surface plasmon resonance and electron microscopy. Here, this computationally guided study holds great promise for designing new sequences with unprecedented control of the placement of chemical functional groups, thus allowing for further unraveling of silicification mechanisms and the eventual design of sequence-defined synthetic polymers leading to the predictive synthesis of nanostructured functional materials.

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Davis Computational Spectroscopy Workflow—From Structure to Spectra

Here, we describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ~100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with ab initio methods.

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Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the d -Block

First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, electronic structure information is generally lost. Particularly in the field of heterogeneous catalysis, simulated electron band structures provide fundamental insights into catalytic reactivity. This ab initio knowledge is preserved in semiempirical methods such as density functional tight binding (DFTB), which extend the accessible computational length and time scales beyond first-principles approaches. In this paper here we present Shell-Optimized Atomic Confinement (SOAC) DFTB electronic-part-only parametrizations for bulk and surface band structures of all d-block transition metals that enable efficient predictions of electronic descriptors for large structures or high-throughput studies on complex systems outside the computational reach of density functional theory.

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The H•/H– Redox Couple and Absolute Hydration Energy of H–

A supermolecule-continuum approach with water clusters up to n = 16 H2O molecules has been used to predict the absolute hydration free energies at 298 K ($\Delta$G hyd ) of both hydrogen (H•) and hydride (H - ) to be 4.6 and -78 ± 2 kcal/mol, respectively. These values are combined with a high accuracy prediction of the gas phase electron affinity ($\Delta$G gas,298K = -16.9 kcal/mol) to determine the aqueous electron affinity of H• of 99.5 ± 2 kcal/mol, which yields a reduction potential for H• vs SHE of -0.03 ± 0.15 V. This value is in agreement within 0.2 V with most estimates obtained using a wide variety of approaches. The results of this study can be used to improve the absolute hydricity scale in water which provides additional insights into how a putative hydride interacts with solvent, but do not change the ability to predict the relative reactivity of two species using relative hydricity scales.

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Gas-Phase Study of the Elementary Reaction of the D1-Ethynyl Radical (C 2 D; X 2 Σ + ) with Propylene (C 3 H 6 ; X 1 A') under Single-Collision Conditions

The bimolecular gas-phase reactions of the D1-ethynyl radical (C 2 D; X 2 Σ + ) with propylene (C 3 H 6 ; X 1 A’) and partially substituted D3-3,3,3-propylene (C 2 H 3 CD 3 ; X 1 A’) were studied under single collision conditions utilizing the crossed molecular beams technique. Combining our laboratory data with electronic structure and statistical calculations, the D1-ethynyl radical is found to add without barrier to the C1 and C2 carbons of the propylene reactant, resulting in doublet C 5 H 6 D intermediate(s) with lifetime(s) longer than their rotational period(s). These intermediates undergo isomerization and unimolecular decomposition via atomic hydrogen loss through tight exit transition states forming predominantly cis/trans-3-penten-1-yne ((HCC)CH=CH(CH 3 )) and to a minor amount 3-methyl-3-buten-1-yne ((HCC)C(CH 3 )=CH 2 ) via overall exoergic reactions. Although the title reaction does not lead to the cyclopentadiene molecule (c-C 5 H 6 , X 1 A 1 ), high temperature environments can convert the identified acyclic C 5 H 6 isomers through hydrogen atom assisted isomerization to cyclopentadiene (c-C 5 H 6 , X 1 A 1 ). Since both the ethynyl radical and propylene reactants have been observed in cold interstellar environments such as TMC-1 and the reaction is exoergic and all barriers lie below the energy of the separated reactants, the these C 5 H 6 product isomers are predicted to form in those low temperature regions.

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Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. Here, we show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

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Molecular Driving Force for Facet Selectivity of Sequence-Defined Amphiphilic Peptoids at Au–Water Interfaces

Shape-controlled colloidal nanocrystal syntheses often require facet-selective solution-phase chemical additives to regulate surface free energy, atom addition/migration fluxes or particle attachment rates. Because of their highly tunable properties and robustness to a wide range of experimental conditions, peptoids represent a very promising class of next generation functional additives for control over nanocrystal growth. However, understanding the origin of facet selectivity at the molecular level is critical to generalizing their design. In this work, we employ molecular dynamics simulations and biased sampling methods and report stronger selectivity to Au(111) than to Au(100) for Nce3Ncp6, a peptoid that has been shown to assist the formation of five-fold twinned Au nanostars. We find that facet-selectivity is achieved through synergistic effects of both peptoid-surface and solvent-surface interactions. Moreover, the amphiphilic nature of Nce3Ncp6 together with the order of peptoid-peptoid and peptoid-surface binding energies, i.e., peptoid-Au(100) < peptoid-peptoid < peptoid-Au(111), further amplifies its distinct collective behavior on different Au surfaces. Our studies provide a fundamental understanding of the molecular origin of facet-selective adsorption and highlight the possibility of future designs and uses of sequence-defined peptoids for predictive syntheses of nanocrystals with designed shapes and properties.

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Role of Intra-Domain Heterogeneity on Ion and Polymer Dynamics in Block Polymer Electrolytes: An Approach for Spatially Resolving Dynamics and Ion Transport

The design of safe and high-performance, nanostructured, block polymer (BP) electrolytes for lithium-ion batteries requires a thorough understanding of the key parameters that govern local structure and dynamics. Yet, the interfaces between microphase separated domains can introduce complexities in this local behavior that can be challenging to quantify. Herein, the local polymer, cation (Li + ), and anion dynamics were described in salt-doped polystyrene block poly(oligo-oxyethylene methyl ether methacrylate) (PS-b-POEM) through a quantitative framework that considered the effects of polymer architecture, segmental mixing, chain stretching, and confinement on polymer mobility and ion transport. This framework was validated through nuclear magnetic resonance (NMR) spectroscopy measurements on solid (dry) polymer electrolyte samples. Notably, a mobility transition temperature (T mobility ) was identified through NMR spectroscopy that captured the local dynamics more accurately than the thermal glass transition temperature. Additionally, the approach quantitatively described the mobility gradient across a domain when segmental mixing effects were combined with chain stretching and confinement information, especially at higher segregation strengths – facilitating the assessment of local ion diffusion and conductivity. As a result, spatially averaged local ion diffusion predictions quantitatively matched NMR-measured ion diffusivities in the BP samples, while spatially summed ionic conductivity predictions across a domain qualitatively captured trends in the measured ionic conductivities.

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Injection Parameters That Promote Branching of Hydraulic Cracks

Fluid injection into rock formations can either produce complex branched hydraulic fractures, create simple planar fractures, or be dominated by porous diffusion. Currently, the optimum injection parameters to create branched fractures are unknown. We conducted repeatable hydraulic fracturing experiments using analog-rock samples with controlled heterogeneity to quantify the fluid parameters that promote fracture branching. A large range of injection rates and fluid viscosities were used to investigate their effects on induced fracture patterns. Paired with a simple analytical model, our results identify the threshold at which fracture transitions from an isolated planar crack to branched cracks when closed natural fractures exist. These results demonstrate that this transition can be controlled by injection rate and fluid viscosity. In relation to the field practices, the present model predicts slickwater and lower viscosity fluid injections promote fracture branching, with the Marcellus shale used as an example.

58 GEOSCIENCES↗

Reaction mechanism and kinetics for CO 2 reduction on nickel single atom catalysts from quantum mechanics

Experiments have shown that graphene-supported Ni-single atom catalysts (Ni-SACs) provide a promising strategy for the electrochemical reduction of CO 2 to CO, but the nature of the Ni sites (Ni-N 2 C 2 , Ni-N 3 C 1 , Ni-N 4 ) in Ni-SACs has not been determined experimentally. Here, we apply the recently developed grand canonical potential kinetics (GCP-K) formulation of quantum mechanics to predict the kinetics as a function of applied potential (U) to determine faradic efficiency, turn over frequency, and Tafel slope for CO and H 2 production for all three sites. We predict an onset potential (at 10 mA cm –2 ) U onset = –0.84 V (vs. RHE) for Ni-N 2 C 2 site and U onset = –0.92 V for Ni-N 3 C 1 site in agreement with experiments, and U onset = –1.03 V for Ni-N 4 . We predict that the highest current is for Ni-N 4 , leading to 700 mA cm –2 at U = –1.12 V. To help determine the actual sites in the experiments, we predict the XPS binding energy shift and CO vibrational frequency for each site.

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Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity

We design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM'Q 3 (A/M/M' = alkali, alkaline earth, post-transition metals, lanthanides, and Q = chalcogens). These compounds are shown to possess ultralow lattice thermal conductivity (κ l ), a desired requirement for thermal-barrier coatings and thermoelectrics. Upon screening the thermodynamic stability of ~1 million compounds using the ML model iteratively and performing density-functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their κ l using Peierls–Boltzmann transport equation, which reveals ultralow κ l (<2 Wm -1 K -1 at room temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high efficiency of scale-invariant ML model in predicting novel compounds and presents experimental-research opportunities with these new compounds.

36 MATERIALS SCIENCE↗

Spectroscopic evidence for intact carbonic acid stabilized by halide anions in the gas phase

Here we show elusive carbonic acid being effectively stabilized in the gas phase by interacting with halide anions X – (X = F, Cl, Br, and I). The formed H 2 CO 3 ·X – complexes, characterized by negative ion photoelectron spectroscopy and ab initio calculations, all contain intact trans – trans carbonic acid binding onto the respective halide via two identical strong ionic O–H...X – hydrogen bonds. For X = Cl, Br, and I, the complex spectra exhibit the corresponding X – signature by simply shifting to the higher binding energy side, while an extremely 2 eV wide broader band is observed for X = F. This spectroscopic evidence indicates that an excess electron is removed from each halide in the former case, while a proton is transferred from carbonic acid to fluoride upon electron detachment for the latter. The above H 2 CO 3 ·X – structures as well as those of the previously studied H 2 SO 4 ·X – along the homologous halogen series cannot be explained using the proton affinity (PA) argument. Instead, a qualitative correlation is found between these structural motifs and the constituent acid p K a values, strongly suggesting that p K a is a more suitable factor to predict correct acid–base chemistry between these diprotic oxyacids and halides.

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Exploring the kinetics of actinyl–EDTA reduction by ferrous iron using quantum-mechanical calculations

Here, the reduction of An(VI) (An = U, Np, and Pu) to An(IV) significantly decreases its solubility and mobility. This reaction can be hindered by complexation with inorganic (e.g., carbonate) or organic ligands. Ethylenediaminetetraacetic acid (EDTA) is one such organic ligand that forms stable complexes with actinides. Therefore, it may enhance the mobility of actinides. However, the redox kinetics and mechanisms of actinyl (An(V/VI)O 2 +/2+ )–EDTA are not well characterized yet and are thus studied here using quantum-mechanical calculations. The principle is to approach the actinyl–EDTA and Fe 2+ (reductant) in small incremental steps and calculate the system energy at each distance. The overall reaction is then delineated into sub-processes (encounter frequency in bulk solution, formation of outer-sphere complex, transition from outer- to inner-sphere complex, and electron transfer), and reaction rates are determined for each sub-process. The formation of outer-sphere complexes occurs rapidly in microseconds to seconds over a wide range of actinyl concentrations (pM to μM); in contrast, the transition to the inner-sphere complex is relatively slow (milliseconds to a few seconds). Immediate electron transfer to form the pentavalent actinide is observed along the reaction path for Np(VI) and Pu(VI), but not for U(VI). Surprisingly, in acidic conditions, one of the carboxylic groups gets protonated in EDTA of [UO 2 (edta)] 2- rather than one of the amino groups. This process-based series of calculations can be applied to any redox reaction and allows the prediction of changes to the rate law and rate-limiting step in a more fundamental way for different environments

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