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A method for quantifying crystallinity within a sample using second harmonic generation microscopy is described herein. In one aspect, a method for reducing the timeframe for accelerated stability testing of amorphous solid dispersions of active pharmaceutical ingredients though identifying regions of interest to quantify crystallinity and composition is presented herein.
According to one inventive concept, a method for forming an aerogel includes forming a SiO 2 gel, forming a mixture of the SiO 2 gel and a TiCl 4 -derived precursor sol, wherein the TiCl 4 sol is comprised of TiCl 4 and a solvent, forming a SiO 2 /TiO 2 wet gel, drying the SiO 2 /TiO 2 wet gel, and heating the dried SiO 2 /TiO 2 gel.
The present disclosure is directed to designing dyes and methods to alter the parameters controlling the dipole-dipole coupling of dyes bound to a nucleotide oligomer architecture, which are used to propagate excitons for use in next generation room temperature quantum information systems. The disclosed dyes and methods are directed to changing the dye stability, symmetry, overlap, and steric hindrance of the dyes to fine tune aggregate systems.
Dissolved organic matter (DOM) is widely recognized to control the solubility and reactivity of trace metals in the environment. However, the mechanisms that govern metal-DOM complexation remain elusive, primarily due to the analytical challenge of fractionating and quantifying metal–organic species within the complex mixture of organic compounds that comprise DOM. Here, we describe a quantitative method for fractionation and element-specific detection of organic–metal complexes using liquid chromatography with online inductively coupled plasma mass spectrometry (LC–ICP-MS). The method implements a post-column compensation gradient to stabilize ICP–MS elemental response across the LC solvent gradient, thereby overcoming a major barrier to achieving quantitative accuracy with LC–ICP-MS. With external calibration and internal standard correction, the method yields concentrations of organic–metal complexes that were consistently within 6% of their true values, regardless of the complex’s elution time. We used the method to evaluate the effects of four stationary phases (C18, phenyl, amide, and pentafluoroylphenyl propyl) on the recovery and separation of environmentally relevant trace metals (Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in Suwannee River Fulvic Acid and Suwannee River Natural Organic Matter. The C18, amide, and phenyl phases generally yielded optimal metal recoveries (>75% for all metals except Pb), with the phenyl phase separating polar species to a greater extent than C18 or amide. We also fractionated organic-bound Fe, Cu, and Ni in oxidized and reduced soils, revealing divergent metal-DOM speciation across soil redox environments. Finally, by enabling quantitative fractionation of DOM-bound metals, our method offers a means for advancing a mechanistic understanding of metal–organic complexation throughout the environment.
Greenhouse effect, largely caused by CO 2 emission, has become a major concern for global climate change. Post-combustion CO 2 capture is one of the critical strategies to mitigate this issue. Membrane technology for carbon capture has drawn significant attention because of the cost and energy efficiency and scalability. Many membranes for CO 2 capture are limited by the trade-off between CO 2 permeability and CO 2 /N 2 selectivity and the long-term stability under practical operating conditions. Facilitated transport membranes with efficient CO 2 carriers have demonstrated potential to surpass the permeability-selectivity trade-off, but these carriers are often lost under operational conditions. In this work, we designed and fabricated a polystyrene sulfonate (PSS) stabilized polyethylenimine (PEI) membrane by a facile and scalable spray-coating method. The deposited defect-free selective layer, in which the amine carriers in PEI can be stabilized electrostatically by PSS, exhibited superior CO 2 separation performance with good long-term stability under practical operating conditions. The separation performance was optimized by spray-coating cycles, CNT network loading, and PSS loading. Our membrane showed CO 2 permeance ranging from 820 to 1,770 GPU and CO 2 /N 2 selectivity varying from 395 to 460 under vacuum operation mode in the temperature range between 80 and 90 °C. Furthermore, the membrane was successfully scaled up to 200 cm 2 with good uniformity. These results might suggest a novel membrane structure and a scalable approach for fabrication of highly efficient CO 2 separation membranes.
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Two-dimensional covalent organic frameworks (2D COFs) are periodic, permanently porous, and lightweight solids that are polymerized from topologically designed monomers. The predictable design and structural modularity of these materials make them promising candidates for applications including catalysis, environmental remediation, chemical separations, and organic electronics, many of which will require stability to mechanical and thermal stress. Based on their reinforced structures and high degradation temperatures, as determined by thermal gravimetric analysis (TGA), many reports have claimed that COFs have excellent thermal stability. However, their stability to heat and pressure has not been probed using methods that report on structural changes rather than the loss of volatile compounds. Here, we explore two structurally analogous 2D COFs with different polymerization chemistries using in operando X-ray diffraction (XRD), which demonstrates the loss of crystallinity at lower temperatures than the degradation temperatures measured by TGA. Density functional theory calculations suggest that an asymmetric buckling of the COF lattice is responsible for the observed loss of crystallinity. In addition to their thermal stability, XRD of the 2D COFs under gas pressures up to 100 bar showed no loss in crystallinity or structural changes, indicating that these materials are robust to mechanical stress by applied pressure. We expect that these results will encourage further exploration of COF stability as a function of framework design and isolated form, which will guide the design of frameworks that withstand demanding application-relevant conditions.
Isolation of hemicelluloses through alkaline extraction results in formation of salts, which usually necessitates additional salt removal steps. In this study, acetylation of xylans was studied without the removal of potassium acetate (KAc), a commonly produced salt during the alkaline extraction, to find a facile method to alter the hydrophilic characteristics of hemicelluloses. The acetylation reaction was carried out solely with acetic anhydride without any catalysts or solvents added. The weight gain and ATR–FTIR analyses verified the acetylation of xylans in the presence of KAc. A significant decrease in water solubility and moisture uptake was observed upon acetylation in the presence of KAc which is also accompanied by an increase in thermal stability, disclosing an easy, efficient and environmentally friendly method to obtain hydrophobically modified xylans without the use of toxic catalysts and costly salt purification steps.
Here, we consider neutral particle systems described by moments of a phase-space density and propose a realizability-preserving numerical method to evolve a spectral two-moment model for particles interacting with a background fluid moving with nonrelativistic velocities. The system of nonlinear moment equations, with special relativistic corrections to $\mathscr{O}$($v$/$c$), expresses a balance between phase-space advection and collisions and includes velocity-dependent terms that account for spatial advection, Doppler shift, and angular aberration. The model is conservative for the correct $\mathscr{O}$($v$/$c$) Eulerian-frame number density and is consistent, to $\mathscr{O}$($v$/$c$), with Eulerian-frame energy and momentum conservation. This model is closely related to the one promoted by Lowrie et al. and similar to models currently used to study transport phenomena in large-scale simulations of astrophysical environments. The proposed numerical method is designed to preserve moment realizability, which guarantees that the moments correspond to a nonnegative phase-space density. The realizability-preserving scheme consists of the following key components: (i) a strong stability-preserving implicit-explicit (IMEX) time-integration method; (ii) a discontinuous Galerkin (DG) phase-space discretization with carefully constructed numerical uxes; (iii) a realizability-preserving implicit collision update; and(iv) a realizability-enforcing limiter. In time integration, nonlinearity of the moment model necessitates solution of nonlinear equations, which we formulate as fixed-point problems and solve with tailored iterative solvers that preserve moment realizability with guaranteed global convergence. We also analyze the simultaneous Eulerian-frame number and energy conservation properties of the semi-discrete DG scheme and propose a "spectral redistribution" scheme that promotes Eulerian-frame energy conservation. Through numerical experiments, we demonstrate the accuracy and robustness of this DG-IMEX method and investigate its Eulerian-frame energy conservation properties.
Here, in this work, we revisited the idea of using the coupled-cluster (CC) ground state formalism to target excited states. Our main focus was targeting doubly excited states and double core hole states. Typical equation-of-motion (EOM) approaches for obtaining these states struggle without higher-order excitations than doubles. We showed that by using a non-Aufbau determinant optimized via the maximum overlap method, the CC ground state solver can target higher energy states. Furthermore, just with singles and doubles (i.e., CCSD), we demonstrated that the accuracy of ΔCCSD and ΔCCSD(T) (triples) far surpasses that of EOM-CCSD for doubly excited states. The accuracy of ΔCCSD(T) is nearly exact for doubly excited states considered in this work. For double core hole states, we used an improved ansatz for greater numerical stability by freezing core hole orbitals. The improved methods, core valence separation (CVS)-ΔCCSD and CVS-ΔCCSD(T), were applied to the calculation of the double ionization potential of small molecules. Even without relativistic corrections, we observed qualitatively accurate results with CVS-ΔCCSD and CVS-ΔCCSD(T). Remaining challenges in ΔCC include the description of open-shell singlet excited states with the single-reference CC ground state formalism as well as excited states with genuine multireference character. The tools and intuition developed in this work may serve as a stepping stone toward directly targeting arbitrary excited states using ground state CC methods.
A defining problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce an alternative, data-driven method for parametrizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local gradient of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.
Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. DMMs using deep neural networks to parametrize the transition of Markov probability distributions have recently been shown to provide more expressiveness in modeling sequential data and dynamical system responses. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a rigorous analytical method to prove the necessary and sufficient conditions of DMM's stochastic stability. This task is achieved by spectral analysis of the efficiently computed Jacobians of probabilistic maps modeled by deep neural networks. We make theoretical connections between the eigenvalues of neural network's weights and the different activation function types used on the stability and overall dynamic behavior of DMMs with Gaussian distributions. We empirically substantiate our theoretical results on stochastic stability and eigenvalue spectra via several numerical experiments. Formal stability guarantees of DMMs can substantially improve their robustness and trustworthiness, necessary for reliable use in safety-critical real-world applications.
Remedial Action Schemes (RAS) or Special Protection Schemes (SPS) are used throughout the bulk transmission system as a non-wires method of increasing transmission transfer capability. However, as the population grows within the Western Interconnection, more power needs to be transferred with less ability to build lines. This leads to a greater number of RAS, and the subsequent interaction between these disparate schemes can have unintended consequences. While the large number of RAS within the Western Electricity Coordinating Council (WECC) protect for different things, which can range from the violation of thermal limitations to transient stability issues, they can be described by common methods of operation. In order to understand these methods, a survey was sent out to members of the WECC Modeling & Validation Working Group (MVWG). The response received provides a necessary base in which to understand and build models that can be used within WECC. This report provides information on the survey results regarding RAS/SPS modeling practices for planning and operational studies within the WECC footprint, which is foundational for on-going and future work on RAS. Pacific Northwest National Laboratory (PNNL) and its industry partners are developing innovative methods for adaptively setting RAS parameters based on realistic and near real-time operational conditions to improve power grid reliability and grid asset utilization.
Poly(ε-caprolactone) (PCL) and two different molecular weight (6K and 650K) of polystyrene (PS) were mixed in solution to prepare binary blends of PCL/PS with various compositions. The impact of the molecular weight of PS in the blends was studied on thermal stability and miscibility by the thermogravimetric analysis (TGA) and the differential scanning calorimetry (DSC) method. The TGA results under dynamic conditions in an inert atmosphere show that the thermal stability of the blends depends on the length of PS molecules. The increase of the low molecular weight PS into the PCL/PS blend reduces the thermal stability while the high molecular weight PS improves the thermal stability. The crystallization peak temperature, enthalpy, and crystallinity of the blends are found molecular weight dependent; these parameters with blend compositions deviate from linearity of additive law for low molecular weight PS, while they do follow the additive law for high molecular weight PS. A significant melting point depression of PCL crystals with composition was observed for the blends with the incorporation of the low molecular weight PS, while the no significant melting temperature depression was observed for the high molecular weight PS. The experimental results clearly indicate that in the PCL/PS blends, the thermal stability and the interaction between the neat components strongly depend on the molecular weight of the PS.
Threshold virtual impedance (TVI)-based current limiting for grid-forming converters (GFMs) has gained great interest due to its ability to maintain voltage source behaviour during faults. However, sequence component extraction (SCE) and negative-sequence control (NSC) are often overlooked in small-signal stability assessments during faults. This paper develops small-signal sequence impedance models for GFMs under four well-known SCE methods based on TVI current limiting control during symmetrical fault conditions. Using the developed impedance models, the impacts of SCE and NSC, and the voltage and current control loop bandwidths, on system stability during faults are investigated. Additionally, since negative-sequence TVI (TVI-) is typically added along with its positive-sequence counterpart, which is often inductive, inductive and resistive TVI- are examined. The findings suggest that a higher voltage or current control loop bandwidth has a negative impact on system stability, while SCE and NSC largely reduce the stable range for voltage and current control loop bandwidth during faults, and that the severity of such impacts is determined by the particular SCE method. Furthermore, it is observed that inductive TVI- significantly degrades system stability, while resistive TVI- can enhance stability when suitable SCE methods are appropriately selected and designed. Matlab/Simulink electromagnetic transient simulations validate these analytical results.
Direct formation of ultra-small nanoparticles on carbon supports by rapid high temperature synthesis method offers new opportunities for scalable nanomanufacturing and the synthesis of stable multi-elemental nanoparticles. However, the underlying mechanisms affecting the dispersion and stability of nanoparticles on the supports during high temperature processing remain enigmatic. In this work, we report the observation of metallic nanoparticles formation and stabilization on carbon supports through in situ Joule heating method. We find that the formation of metallic nanoparticles is associated with the simultaneous phase transition of amorphous carbon to a highly defective turbostratic graphite (T-graphite). Molecular dynamic (MD) simulations suggest that the defective T-graphite provide numerous nucleation sites for the nanoparticles to form. Furthermore, the nanoparticles partially intercalate and take root on edge planes, leading to high binding energy on support. This interaction between nanoparticles and T-graphite substrate strengthens the anchoring and provides excellent thermal stability to the nanoparticles. These findings provide mechanistic understanding of rapid high temperature synthesis of metal nanoparticles on carbon supports and the origin of their stability.
This paper deals with a distributed decision method for determining the electricity price considering the negative price in the market trading. In addition, we analyze the stability and convergence properties for the market trading system using the concept of passivity-short. At first, mathematical models of market participants' behavior which maximize their own profits are discussed. Then, we construct a trading system based on dynamic pricing considering plug and play operation that each market participant intermittently participates in market trading. As for the optimal electricity price, even if the optimal price might be negative, we indicate the stability of the system can be guaranteed by using passivity-short which is a relaxed concept of passivity. Finally, numerical simulation results illustrate that each value decided by the market trading converges to the optimal value.
The ocean has absorbed over 90% of the heat from recent anthropogenic greenhouse warming. This chapter reviews a variety of ocean-related methods for slowing down and eventually halting this warming. “Blue carbon” reservoirs store more carbon than terrestrial equivalents, although are still limited compared with global negative emissions needs. Oceanic carbon uptake could be augmented via iron fertilization, although this method presents challenges. Algal-based biomass energy with carbon capture and storage (ABECCS) offers opportunities for carbon-neutral fuel alternatives as well as for negative emissions. Total carbon dioxide removal potential is estimated at 15–33 Pg CO 2 /year by mid-century and 33–95 Pg CO 2 /year by the end of the century, which could offset current and projected CO 2 emissions. ABECCS can also coproduce high-protein feed for aquaculture and reduce demand for agricultural land. Furthermore, combined with effective ocean blue carbon management and terrestrial methods, ocean-based carbon removal can play a critical role in climate stabilization.