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At least 1,603 records · Page 89

Nanoscale π–π stacked molecules are bound by collective charge fluctuations

Non-covalent π-π interactions are central to chemical and biological processes, yet the full understanding of their origin that would unite the simplicity of empirical approaches with the accuracy of quantum calculations is still missing. Here we employ a quantum-mechanical Hamiltonian model for van der Waals interactions, to demonstrate that intermolecular electron correlation in large supramolecular complexes at equilibrium distances is appropriately described by collective charge fluctuations. We visualize these fluctuations and provide connections both to orbital-based approaches to electron correlation, as well as to the simple London pairwise picture. The reported binding energies of ten supramolecular complexes obtained from the quantum-mechanical fluctuation model joined with density functional calculations are within 5% of the reference energies calculated with the diffusion quantum Monte-Carlo method. Our analysis suggests that π-π stacking in supramolecular complexes can be characterized by strong contributions to the binding energy from delocalized, collective charge fluctuations—in contrast to complexes with other types of bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Forced synchronization of autonomous dynamical Boolean networks

We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗

Ensemble Feature Selection in Scientific Data Analysis

Feature selection has emerged as an important method of both examining and predicting results from scientific experiments. However, current feature selection methods cannot consistently provide meaningful descriptions of feature importance in the complex, high-dimensional, natural datasets encountered in scientific data analysis. Here, an ensemble feature selection method is demonstrated to collectively reduce the bias associated with individual feature selection algorithms and provide uncertainty estimates on feature importance ranks for both synthetic and natural datasets.

97 MATHEMATICS AND COMPUTING↗

Improved Platform for the Benchtop Study of Complex Radiological Environments that Affect Fallout Formation: DHS Feasbility Study (Final Report)

Fallout analysis is a crucial field of study as it provides insight into the structural interactions that occur between radiological components and their surroundings following a nuclear event. Due to the complexity of actual fallout samples, setups like the plasma flow reactor (PFR) at LLNL have been developed to synthesize fallout in a controlled environment. While the PFR allows for control of parameters such as initial temperature, cooling rate, and concentration of radiological species, controlling the concentration of water has remained a challenge as samples are introduced into the setup via aqueous solution. To that end, our goal with this project was to develop methodologies to (1) monitor and (2) control the presence of water in the PFR. We found that a commercially available FTIR and MCT detector was sufficient to monitor the presence of both condensed and vapor-phase water, while a desolvating nebulizer could be used to control the amount of water injected into the system. In total, the results of this feasibility study have expanded the ability of the flow reactor to investigate key sensitivities that influence fallout formation.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Average hydrodynamic radius analysis reveals critical solvation thresholds in high-concentration lithium electrolytes

Understanding the solvation structures of lithium salts in carbonate- and ether-based electrolytes is central to explaining the exceptional stability of high-concentration electrolytes (HCEs) and localized high-concentration electrolytes (LHCEs). Conventional techniques such as vibrational spectroscopy and one-dimensional NMR provide only limited information, typically restricted to coordination ratios and ion-pair distributions, without revealing the actual size and mass of solution complexes. Here, in this study, we introduce an average hydrodynamic radius (AHR) analysis based on internally referenced DOSY NMR, which enables direct estimation of the average volume and molecular weight of lithium–solvent complexes in solution. Using LiFSI–EMC and LiFSI–EMC–TTE electrolytes, we demonstrate that the onset of effective lithium metal stabilization and aluminum corrosion suppression coincides with the formation of very large complexes, whose average volume exceeds 300 times that of free EMC molecules. This finding supports a new “blocking mechanism,” wherein bulky solvated complexes impede direct solvent access to reactive surfaces. The AHR analysis thus not only clarifies the fundamental origin of HCE and LHCE effectiveness, but also provides a broadly applicable experimental framework for probing complex solvation structures and guiding rational electrolyte design.

Concentrated electrolytes↗

Development of LpxH Inhibitors Chelating the Active Site Dimanganese Metal Cluster of LpxH

Abstract Despite the widespread emergence of multidrug‐resistant nosocomial Gram‐negative bacterial infections and the major public health threat it brings, no new class of antibiotics for Gram‐negative pathogens has been approved over the past five decades. Therefore, there is an urgent medical need for developing effective novel antibiotics against multidrug‐resistant Gram‐negative pathogens by targeting previously unexploited pathways in these bacteria. To fulfill this crucial need, we have been investigating a series of sulfonyl piperazine compounds targeting LpxH, a dimanganese‐containing UDP‐2,3‐diacylglucosamine hydrolase in the lipid A biosynthetic pathway, as novel antibiotics against clinically important Gram‐negative pathogens. Inspired by a detailed structural analysis of our previous LpxH inhibitors in complex with K. pneumoniae LpxH ( Kp LpxH), here we report the development and structural validation of the first‐in‐class sulfonyl piperazine LpxH inhibitors, JH‐LPH‐45 ( 8 ) and JH‐LPH‐50 ( 13 ), that achieve chelation of the active site dimanganese cluster of Kp LpxH. The chelation of the dimanganese cluster significantly improves the potency of JH‐LPH‐45 ( 8 ) and JH‐LPH‐50 ( 13 ). We expect that further optimization of these proof‐of‐concept dimanganese‐chelating LpxH inhibitors will ultimately lead to the development of more potent LpxH inhibitors for targeting multidrug‐resistant Gram‐negative pathogens.

Pharmacology & Pharmacy↗

Metabolic Pathway Engineering

Modern microbial and enzyme engineering and their advancement are increasingly dependent on the marriage of a wide range of sophisticated technologies. For students entering the field of biotechnology, the outlook is indeed daunting. Expertise at levels beyond that of simple familiarity will be needed to conduct competitive research. It goes without saying that to be competitive, all new researchers in this field will need basic preparation in molecular biology, biochemistry, and genetics. Further, experience working with concepts and experimental tools in enzyme biochemistry and kinetics, gene editing, computational metabolic pathway modeling, experimental pathway flux analysis, and computational clustering tools to process complex data sets will be vital for success. We speculate that most biotechnology researchers in early career at this time will build teams of collaborators to address these disparate science fields rather than attempt to become experts in one lab.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗

Integrating out beyond tree level and relativistic superfluids

We revisit certain subtleties of renormalization that arise when one derives a low-energy effective action by integrating out the heavy fields of a more complete theory. Usually these subtleties are circumvented by matching some physical observables, such as scattering amplitudes, but a more involved procedure is required if one is interested in deriving the effective theory to all orders in the light fields (but still to fixed order in the derivative expansion). As a concrete example, we study the U(1) Goldstone low-energy effective theory that describes the spontaneously broken phase of a $\phi$ 4 theory for a complex scalar. Working to lowest order in the derivative expansion, but to all orders in the Goldstones, we integrate out the radial mode at one loop and express the low-energy effective action in terms of the renormalized couplings of the UV completion. This yields the one-loop equation of state for the superfluid phase of (complex) $\phi$ 4 . We perform the same analysis for a renormalizable scalar SO(N) theory at finite chemical potential, integrating out the gapped Goldstones as well, and confirm that the effective theory for the gapless Goldstone exhibits no obvious sign of the original SO(N) symmetry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Embedded symmetric positive semi-definite machine-learned elements for reduced-order modeling in finite-element simulations with application to threaded fasteners

Here, we present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.

97 MATHEMATICS AND COMPUTING↗

A methodology for domain overlapping coupling of thermal-hydraulic systems

Multi-scale coupling has increasingly drawn attention as a promising approach for modeling thermal systems. Traditional system codes provide fast and robust predictions at the plant scale, while high-fidelity computational fluid dynamics (CFD)-based tools resolve localized flow and heat transfer phenomena with greater accuracy. By combining these complementary strengths, co-simulations enable multi-scale analysis that would otherwise be computationally prohibitive for a standalone CFD code. Here, this work introduces a robust and problem-agnostic domain overlapping (DO) coupling between the system thermal-hydraulic (STH) code System Analysis Module (SAM) and the coarse-mesh CFD code Pronghorn. Both applications belong to the Comprehensive Reactor Analysis Bundle (BlueCRAB) code suite, a code suite in active development at the Idaho National Laboratory (INL), tailored for multi-physics analysis of advanced reactors. Unlike previous approaches, BlueCRAB supports an agnostic interface between codes based on different fidelity, while its coupling formulation can address arbitrary flow geometries with multiple inlets and outlets in coupled components. The implemented method leads to consistent pressure drops, enthalpies, and scalar concentrations between coupled SAM and Pronghorn simulations. The methodology is demonstrated through two verification tests, which ensure the numerical consistency and conservation across the codes, and through one validation test against experimental data. The proposed problems explore different physical aspects inherent to thermal systems, with particular attention given to nuclear reactor analysis. These include buoyancy-driven flows, complex flow patterns, and setups with multiple inlets and outlets, representing challenges in advanced reactor applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH 4 , CO, and H 2 for chemical looping combustion

In this work, three kinetic models are developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier (OC) particle with CH 4 , CO, and H 2 , using data from thermogravimetric analysis. The complete reduction rate profiles exhibit complex dynamics whose trajectory is significantly different depending on the reducing gas. A Bayesian model building and parameter estimation framework is applied for simultaneous parameter and model structure uncertainty quantification. The final models show excellent agreement between model predictions and calibration data, as well as new data not used for calibration (for the reduction of the OC with CH 4 ). Parameter uncertainty is quantified by determining joint posterior distribution, and model structure uncertainty is addressed by incorporating Gaussian process stochastic functions (represented by Bayesian smoothing splines) into the kinetic models. The final kinetic models with discrepancy functions are readily employable in equation-oriented simulation and optimization platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evolution of ion damage at 773K in Ni- containing concentrated solid-solution alloys

Quantitative analysis of the impact of the compositional complexity in a series of Ni-containing concentrated solid-solution alloys, Ni, NiCo, NiFe, NiCoCr, NiCoFeCr, NiCoFeCrMn and NiCoFeCrPd, on the evolution of defects produced by 1 MeV Kr ion irradiation at 773 K is reported in this paper. The dynamics of the evolution of the damage structure during irradiation to a dose of 2 displacements per atom were observed directly by performing the ion irradiations in electron transparent foils in a transmission electron microscope coupled to an ion accelerator. The defect evolution was assessed through measurement of the defect density, defect size and fraction of perfect and Frank loops. These three parameters were dependent on the alloying element as well as the number of elements. The population of loops was sensitive to the ion dose and alloy composition as faulted Frank loops were observed to unfault to perfect loops with increasing ion dose. Finally, these dependences are explained in terms of the influence of each element on the lifetime of the displacement cascade as well as on defect formation and migration energies.

36 MATERIALS SCIENCE↗

Oxygen vacancy and Ce{sup 3+} ion dependent magnetism of monocrystal CeO{sub 2} nanopoles synthesized by a facile hydrothermal method

Graphical abstract: - Highlights: • Monocrystal CeO{sub 2} nanopoles are synthesized by hydrothermal method. • Suitable reaction time is beneficial for the growth CeO{sub 2} nanopoles. • As-synthesized CeO{sub 2} nanopoles show excellent RTFM. • The RTFM can be attributed to the influences of oxygen vacancies and Ce{sup 3+} ions. - Abstract: Monocrystal CeO{sub 2} nanopoles of 15–25 nm in diameter and 300–900 nm or more in length were synthesized within 100 h using hydrothermal method with CeCl{sub 3}·7H{sub 2}O as cerium source, NaOH as mineralizer, and ethylenediamine as complexant. The results of XRD and SADE analysis indicate that the as-synthesized CeO{sub 2} samples have the fluorite structure. The band gaps of CeO{sub 2} samples are 0.08–0.38 eV smaller than that of bulk CeO{sub 2}. X-ray photoelectron spectroscopy shows that the concentration of Ce{sup 3+} is higher than 20% for CeO{sub 2} samples. All the as-synthesized CeO{sub 2} samples exhibit the room temperature ferromagnetism (RTFM), and the saturation magnetization (M{sub s}) gradually increases with the increase of E{sub g} up to 2.92 eV and then decreases with E{sub g} increasing more. The highest M{sub s} of 0.167 emu/g is obtained from the CeO{sub 2} nanopoles synthesized within 100 h. The RTFM mechanism of CeO{sub 2} nanopoles has been proposed which can be mainly attributed to the influence of oxygen vacancies and Ce{sup 3+} ions.

36 MATERIALS SCIENCE↗

Characterization of CMPO and its radiolysis products by Direct Infusion ESI-MS

Direct infusion electrospray ionization-mass spectrometry (ESI-MS) approaches were developed for rapid identification of octyl,phenyl,(N,N-(diisobutyl)carbamoylmethyl) phosphine oxide (CMPO) and impurity compounds formed during alpha and gamma irradiation experiments. CMPO is an aggressive Lewis base, and produces extremely abundant metal complex ions in the ESI-MS analysis that make identification of low abundance compounds that are less nucleophilic challenging. Radiolysis products were identified using several approaches including restricting ion trapping so as to exclude the abundant natiated CMPO ions, extraction of acidic products using aqueous NaOH, and extraction of basic products using HNO3. These approaches generated protonated, natiated and deprotonated species derived from CMPO degradation products formed via radiolytic cleavages of several different bonds. Cleavages of the amide and methylene-phosphoryl bonds appear to be favored by both forms of irradiation, while alpha irradiation also appears to induce cleavage of the methylene-carbonyl bond. The degradation products observed are formed from recombination of the initially formed radicals with hydrogen, methyl, isopropyl and hydroxyl radicals that are derived either from CMPO, or the dodecane solvent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Free Energy and Flexibility Analysis of Autoinhibited Human BRAF

The RAF serine/threonine protein kinases function as direct effectors of RAS in the intracellular transmission of extracellular growth signals, and they are key targets for drug discovery, given the high incidence of oncogenic mutations in RAF and other components of this signaling pathway. In its inactive state, RAF is held in an autoinhibited conformation in the cytosol through a combination of intramolecular interactions and binding to a regulatory 14−3−3 protein dimer. Activation of RAF is initiated by its interaction with membrane-localized GTP-bound RAS, which induces conformational changes that release RAF from its autoinhibited state. However, the molecular mechanisms governing RAF activation remain incomplete, largely due to the challenges in experimentally capturing the intermediate conformational states in this process. To address this gap, we developed a comprehensive all-atom model of BRAF based on existing cryo-EM structures. Using this model, we performed extensive molecular dynamics simulations to evaluate the stability and free energy landscape of autoinhibited BRAF in solution. Our analysis reveals conformational flexibility within the autoinhibited complex, suggesting that this dynamic behavior may play a role in facilitating BRAF activation upon engagement with the membrane-bound RAS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evidence for halogen bond covalency in acyclic and interlocked halogen-bonding receptor anion recognition

The synthesis and anion binding properties of novel halogen-bonding (XB) bis-iodotriazole-pyridinium-containing acyclic and [2]catenane anion host systems are described. The XB acyclic receptor displays selectivity for acetate over halides with enhanced anion recognition properties compared to the analogous hydrogen-bonding (HB) acyclic receptor. A reversal in halide selectivity is observed in the XB [2]catenane, in comparison to the acyclic XB receptor, due to the interlocked host’s unique three-dimensional binding cavity, and no binding is observed for oxoanions. Notable halide anion association constant values determined for the [2]catenane in competitive organic–aqueous solvent mixtures demonstrate considerable enhancement of anion recognition as compared to the HB catenane analogue. X-ray crystallographic analysis of a series of halide catenane complexes reveal strong XB interactions in the solid state. These interactions were studied using Cl and Br K-edge X-ray Absorption Spectroscopy (XAS) indicating intense pre-edge features characteristic of charge transfer from the halide to its bonding partner (σ AX←X– * ← X1s), and providing a direct measure of the degree of covalency in the halogen bond(s). Lastly, the data reveal that the degree of covalency is similar to that which is observed in transition metal coordinate covalent bonds. These results are supported by DFT results, which correlate well with the experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

97 MATHEMATICS AND COMPUTING↗