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In this paper we use variable reaction coordinate variational transition state theory (VRC-TST) to calculate the reaction rate constants for the two reactions, R1: (OH).
Electron and vibrational dynamics of molecules are commonly studied by subjecting them to two interactions with a fast actinic pulse that prepares them in a nonstationary state and after a variable delay period T, probing them with a Raman process induced by a combination of a broadband and a narrowband pulse. This technique, known as femtosecond stimulated Raman spectroscopy (FSRS), can effectively probe time resolved vibrational resonances. We show how FSRS signals can be modeled and interpreted using the stochastic Liouville equations (SLE), originally developed for NMR lineshapes. The SLE provide a convenient simulation protocol that can describe complex dynamics caused by coupling to collective bath coordinates at much lower cost than a full dynamical simulation. The origin of the dispersive features that appear when there is no separation of timescales between vibrational variations and the dephasing time is clarified.
Quantum optimal control plays a crucial role in quantum computing by providing the interface between compiler and hardware. Solving the optimal control problem is particularly challenging for multi-qubit gates, due to the exponential growth in computational complexity with the system's dimensionality and the deterioration of optimization convergence. To ameliorate the computational complexity of time-integration, this paper introduces a multiple-shooting approach in which the time domain is divided into multiple windows and the intermediate states at window boundaries are treated as additional optimization variables. Further, this enables parallel computation of state evolution across time-windows, significantly accelerating objective function and gradient evaluations. Since the initial state matrix in each window is only guaranteed to be unitary upon convergence of the optimization algorithm, the conventional gate trace infidelity is replaced by a generalized infidelity that is convex for non-unitary state matrices. Continuity of the state across window boundaries is enforced by equality constraints. A quadratic penalty optimization method is used to solve the constrained optimal control problem, and an efficient adjoint technique is employed to calculate the gradients in each iteration. We demonstrate the effectiveness of the proposed method through numerical experiments on quantum Fourier transform gates in systems with 2, 3, and 4 qubits, noting a speedup of 80x for evaluating the gradient in the 4-qubit case, highlighting the method's potential for optimizing control pulses in multi-qubit quantum systems.
Berkeley Lab’s annual Tracking the Sun report describes trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The latest edition of the report focuses on systems installed through year-end 2021, and is based on data from roughly 2.5 million systems. New to the report this year is an expanded coverage of paired PV-plus-storage systems, including details on system design and pricing trends. The report describes trends related to: -Project characteristics, including system size, module efficiencies, prevalence of paired PV with storage, use of module-level power electronics, third-party ownership, mounting configurations, panel orientation, and non-residential customer segmentation ownership -Median installed-price trends, both nationally and by state, including preliminary data for first half of 2022 -Variability in pricing according to system size, state, installer, equipment type, and other factors, relying on both descriptive and econometric analysis
Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ~ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ~ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.
Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ∼ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ∼ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.
Here, the employment of cyanato (OCN - ) group in high oxidation state manganese cluster chemistry, in conjunction with carboxylate ions and the organic chelating/bridging ligand 2-(hydroxymethyl)pyridine (hmpH), is reported. The syntheses, crystal structures, and magnetochemical characterization are described for [Mn 16 O 8 (OR) 4 (OCN) 4 (O 2 CMe) 12 (hmp) 6 (ROH) 2 ] (R = Me (1), Et (2)) and [Mn 18 O 14 (O 2 CR) 18 (hmp) 4 (hmpH) 2 (H 2 O) 2 ] (R = Me (3), Et (4)). The 2:1:1:1 reactions of Mn(O 2 CMe) 2 ·4H 2 O, hmpH, NaOCN and NEt 3 in solvent MeOH or EtOH afford the isostructural complexes [Mn 16 O 8 (OR) 4 (OCN) 4 (O 2 CMe) 12 (hmp) 6 (ROH) 2 ] (R = Me (1), Et (2)). The [Mn 16 (μ 4 -O) 4 (μ 3 -O) 4 (μ-OMe) 4 (μ 3 -OR) 6 (μ-OR) 6 ] 10+ core of representative complex 1 comprises a Mn II 4 Mn III 4 double-cubane subunit attached on either side to two symmetry-related Mn II Mn III 3 defective dicubanes. A similar reaction of Mn(O 2 CR) 2 ·4H 2 O, hmpH, NaOCN and NEt 3 , but in solvent MeCN, led instead to the formation of [Mn 18 O 14 (O 2 CR) 18 (hmp) 4 (hmpH) 2 (H 2 O) 2 ] (R = Me (3), Et (4)). Compounds 3 and 4 are very similar to each other and can be described as a central [Mn III 4 (μ-O) 6 ] rodlike subunit attached on either side to two symmetry-related [Mn 7 O 9 ] subunits. Variable-temperature, solid-state dc and ac magnetic susceptibility studies revealed the presence of predominant antiferromagnetic exchange interactions in all compounds, and possible S = 2 or 1 (for 1 and 2) and S = 0 (for 3 and 4) ground state spin values. The combined results demonstrate the ability of cyanato groups to facilitate the formation of new polynuclear Mn II/III complexes with structures different than these obtained from the use of the related azides.
Seasonal water variability in the midwestern United States extensively affects the agricultural community, as it impacts irrigation schedules, growing seasons, and overall ecosystem function. Evapotranspiration (ET) is a critical climatic variable in the water cycle and is used to evaluate spatiotemporal trends in drought and flood conditions. The NASA DEVELOP team partnered with the United States Department of Agriculture (USDA) Midwest Climate Hub, the Minnesota Department of Agriculture, the Illinois State Water Survey, and Michigan State University to compare remotely sensed ET products with in situ observations from January 2001 through December 2020. Remotely sensed actual ET (aET) data were sourced from NASA’s Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and reference ET (refET) data were derived from the Gridded Surface Meteorological (gridMET) dataset. For in situ comparison, aET data were downloaded from the AmeriFlux database while refET data were collected from the Illinois Climate Network and Michigan State University’s Enviro-weather database. For a holistic assessment of ET, this project generated comparisons between remotely sensed and in situ observations, calculated descriptive statistics for validation between refET datasets, and spatially produced statistical validation maps regarding in situ sites. The temporal and spatial gaps of AmeriFlux data limited aET analysis. This comparative assessment of ET products across the Midwest can be used by project partners to assess regional water trends and guide future land management decisions.
We present an approach and relevant models for predicting the probabilistic shoc-to-detonation transition (SDT) behavior and Pop plot (PP) of heterogeneous energetic materials (HEM) via mesoscopic microstructure-explicit (ME) and void explicit (VE) simulations at the millimeter (mm) sample size scale. Although the framework here is general, the particular material considered in this paper is pressed Octahydro-1,3,5,7-tetranitro-1,2,3,5-tetrazocine (HMX). To systematically delineate the effects of material heterogeneities, four material cases are considered. These cases are homogeneous material, material with granular microstructure but no voids, homogeneous material with voids, and material with both granular microstructure and voids. Statistically equivalent microstructure sample sets (SEMSS) are generated and used. Eulerian hydrocode simulations explicitly resolve the material heterogeneities, voids, and the coupled mechanical-thermal-chemical processes. In particular, it is found that both microstructure and voids strongly influence the SDT behavior and PP. The effects of different combinations of microstructure heterogeneity and voids on the SDT process and PP are quantified and rank-ordered. The overall framework uses the Mie–Grüneisen equation of state and a history variable reactive burn model (HVRB). A novel probabilistic representation for quantifying the PP is developed, allowing the calculation of (1) the probability of observing SDT at a given combination of shock pressure and run distance, (2) the run-distance to detonation under a given combination of shock pressure and prescribed probability, and (3) the shock pressure required for achieving SDT at a given run distance with a prescribed probability. The results are in agreement with general trends in experimental data in the literature.
Controlling the shapes of surfaces provides a novel way to direct self-assembly of colloidal particles on those surfaces and may be useful for material design. This motivates the investigation of an optimal control problem for surface shape in this paper. Specifically, we consider an objective (tracking) functional for surface shape with the prescribed mean curvature equation in graph form as a state constraint. The control variable is the prescribed curvature. We prove existence of an optimal control, and using improved regularity estimates, we show sufficient differentiability to make sense of the first order optimality conditions. This allows us to rigorously compute the gradient of the objective functional for both the continuous and discrete (finite element) formulations of the problem. Numerical results are shown to illustrate the minimizers and optimal controls on different domains.
Greensands formations are globally abundant sedimentary rocks rich in Fe clays (typically glauconite) that commonly contain natural hydrocarbon accumulations and may be important reservoirs for geologic storage of anthropogenic CO 2 . Diagenesis in greensands is commonly accompanied by the conversion of primary glauconite to siderite (FeCO 3 ), a process that could be exploited for the permanent trapping of CO 2 . Importantly, siderite formation after glauconite requires that the mostly oxidized Fe in the primary Fe clay minerals is reduced during diagenetic interactions. Here, we explore the effect of solution redox state on the stability of glauconite in sandstones with implications for the diagenetic and/or engineered formation of siderite. We performed two flow-through experiments on intact, glauconite-rich sandstone cores at 150 °C and 150 bar. Both experiments employed a 1 mol NaCl/kg, 0.1 mol NaHCO 3 /kg solution charged with ~0.58 mol CO 2 /kg solution, but the redox state of the injected fluid was manipulated between experiments in order to compare glauconite reactivity and siderite saturation state at oxidizing and reducing end-member conditions. After reaction with the oxidizing (O 2 (aq) ≈ 6 μmol/kg) fluid, chemical and Mӧssbauer spectroscopic analyses indicate the production of Fe(III)-oxy/hydroxide minerals from glauconite, whereas, in the reducing (H 2 (aq) ≈ 5–40 mmol/kg) experiment, thermodynamic calculations and coupled chemical, mineralogical, and Mӧssbauer analyses suggest glauconite dissolution and precipitation of an Fe(II) mineral, likely siderite, and minor magnetite formation. These experimental results, along with thermodynamic calculations, confirm that solution redox state is the master variable dictating siderite formation in greensands.
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This work reports on the fundamental details of the crystal structure and phase transformations in Ba(Fe 0.7 Ta 0.3 )O 3-δ , the best known temperature-independent oxygen-sensing ceramic material for applications in extreme environments. Ba(Fe 0.7 Ta 0.3 )O 3-δ ceramics were synthesized using conventional solid-state ceramic reaction under variable sintering temperatures (T s = 1200–1350 °C). Combined X-ray diffraction (XRD) and high-resolution transmission electron microscopy (TEM) measurements revealed the T s -induced phase transformations and their origin in Ba(Fe 0.7 Ta 0.3 )O 3-δ . Associated with phase transformations, pseudo-cubic (PC) reflections, such as {200} PC , {211} PC , and {220} PC , exhibited distinct anomalies with increasing T s . At T s = 1200 °C, Ba(Fe 0.7 Ta 0.3 )O 3-δ stabilized in mixed orthorhombic + rhombohedral phases (Amm2 + R3m). With increasing T s (≥1250 °C), Ba(Fe 0.7 Ta 0.3 )O 3-δ ceramics stabilized in tetragonal/rhombohedral [P4mm + R3m] mixed phases, while variations in the quantity of the respective phases were observed. Because both structure and crystal chemistry play key roles in achieving enhanced performance in chemical sensing and catalytic converters, detailed understanding of the phase transformations and crystal structure of Ba(Fe 0.7 Ta 0.3 )O 3-δ ceramics, as derived in this work, will be useful to develop chemical sensors with optimum performance for high-temperature and corrosive environments.
Carbon–nitrogen interaction reactions play an important role in governing the reactivity of ammonia blended fuels. However, there remains uncertainties regarding their detailed reaction pathways and rate constants, hampering the development of high-fidelity chemical kinetic models. In this study, the kinetics of ĊH 3 + ṄH 2 , a key C–N interaction reaction in ammonia/methane blend combustion have been investigated. The potential energy surface has been explored using the high-level ANL0F method, yielding highly accurate stationary point energies that agree with ATcT values within 0.1 kcal mol –1 . Variable reaction coordinate transition state theory is used to treat the barrierless association and decomposition reaction channels, based on directly sampled radical-radical interaction energies at the CASPT2-F12(2e,2o)/cc-pVTZ-F12 level of theory. The minimum transitional mode numbers of states obtained are then coupled with the RRKM/master equation to calculate temperature- and pressure-dependent rate constants. Our a priori calculations capture available experimental measurements from the literature very well. The calculated rate constants have been incorporated into an NH 3 /CH 4 chemical kinetic model currently under development at the University of Galway. The effect of the updated kinetic data for ĊH 3 + ṄH 2 on model predicted NH 3 /CH 4 fuel reactivity is elucidated.
Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.
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In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.
According to the advocated paradigm shift in toxicology, acquisition of knowledge on the mechanisms underlying the toxicity of chemicals, such as perturbations of biological pathways, is of primary interest. Pluripotent stem cells (PSCs), such as human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs), offer a unique opportunity to derive physiologically relevant human cell types to measure molecular and cellular effects of such pathway modulations. Here we compared the neuronal differentiation propensity of hESCs and hiPSCs with the aim to develop novel hiPSC-based tools for measuring pathway perturbation in relation to molecular and cellular effects in vitro. Among other fundamental pathways, also, the cAMP responsive element binding protein (CREB) pathway was activated in our neuronal models and gave us the opportunity to study time-dependent effects elicited by chemical perturbations of the CREB pathway in relation to cellular effects. We show that the inhibition of the CREB pathway, using 2-naphthol-AS-E-phosphate (KG-501), induced an inhibition of neurite outgrowth and synaptogenesis, as well as a decrease of MAP2{sup +} neuronal cells. These data indicate that a CREB pathway inhibition can be related to molecular and cellular effects that may be relevant for neurotoxicity testing, and, thus, qualify the use of our hiPSC-derived neuronal model for studying chemical-induced neurotoxicity resulting from pathway perturbations. - Highlights: • HESCs derived neuronal cells serve as benchmark for iPSC based neuronal toxicity test development. • Comparisons between hESCs and hiPSCs demonstrated variability of the epigenetic state • CREB pathway modulation have been explored in relation to the neurotoxicant exposure KG-501 • hiPSC might be promising tools to translate theoretical AoPs into toxicological in vitro tests.