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At least 109 records · Page 6

Interstitial Atoms and the Frustrated and Allowed Structural Transitions Principle: Tunability in the Electronic Structure of AuCu 3 ‐type Frameworks in Dy 4 T 1− x Ga 12 (T = Ag, Ir)

In this Article, we explore how the chemical pressure (CP) features of an intermetallic phase may provide opportunities to couple perturbations in electron count with the stabilization of the underlying geometrical structure. AuCu 3 ‐type LnGa 3 (Ln = lanthanide or group 3 metal) phases contain octahedral cavities of negative CP held open by overly compressed Ln–Ga interactions, leading to a series of transition metal‐stuffed derivatives. We present new additions to this family with the synthesis and crystal structures of Dy 4 T 1−x Ga 12 with (T, x) = (Ag, 0.29) and (Ir, 0.15), adopting Y 4 PdGa 12 ‐type superstructures of the AuCu 3 ‐type. density functional Ttheory (DFT)‐CP calculations, when adjusted to avoid dipolar CP features, affirm that T atom incorporation provides a mechanism for the relief of packing tensions, while electronic density of states distributions illustrate that the T atoms serve largely as electron or hole donors to the band structure, as needed for them to attain d 10 configurations. The maximum obtainable value for x may be limited by a mismatch between the Fermi energy and pseudogap, in line with the balance of factors envisioned by the frustrated and allowed structural transitions principle. Furthermore, trends in resistivity measurements on T = Ir, Pd, and Ag compounds are interpretable in terms of the varying degrees of disorder arising from x < 1.0.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An exploration of benchtop X–ray emission spectroscopy for precise characterization of the sulfur redox state in cementitious materials

The evolution of sulfur chemistry in cements is best known in the bailiwick of failure mechanisms via sulfate attack but is equally important for its contributions to the reduction capacity of cementitious materials often used for immobilizing nuclear waste streams destined for long-term storage, e.g., cementitious waste forms (CWF). The total reduction capacity of CWFs, encompassing contributions from both S and Fe reductants, and its implications toward radionuclide immobilization is most often studied by destructive wet chemistry methods requiring acid digestion in the presence of Ce(IV) and subsequent titration and colorimetric interpretation. Here, we investigate a similarly analytical but nondestructive alternative, benchtop high resolution wavelength-dispersive x-ray fluorescence spectroscopy, most commonly known as x-ray emission spectroscopy (XES), for probing the bulk sulfur oxidation state distribution. We present here an initial investigation of S XES, including an improved experimental protocol for lab XES of inhomogeneous samples, both as a complement to the Ce(IV) test and for new scientific opportunities that it enables for observing changes in sulfur chemistry. We discuss future improvements and opportunities, including: (1) the practical challenges associated with coordinating XES and Ce(IV) liquid extraction for a more comprehensive perspective on reduction capacity and for a high-precision evaluation of uncertainties in the Ce(IV) test; and (2) new opportunities, due to the nondestructive nature of XES, for controlled evolution studies aimed at elucidating specific chemical responses of CWF’s exposed to invasive gas or liquid species or to accelerated aging by radiative dose or thermal treatment.

36 MATERIALS SCIENCE↗

Rationally Engineered Vertically Aligned β ‐Ga 2− x W x O 3 Nanocomposites for Self‐Biased Solar‐Blind Ultraviolet Photodetectors with Ultrafast Response

Abstract With the astonishing advancement of present technology and increasing energy consumption, there is an ever‐increasing demand for energy‐efficient multifunctional sensors or transducers based on low‐cost, eco‐friendly material systems. In this context, self‐assembled vertically aligned β ‐Ga 2− x W x O 3 nanocomposite (GWO‐VAN) architecture‐assisted self‐biased solar‐blind UV photodetection on a silicon platform, which is the heart of traditional electronics is presented. Utilizing precisely controlled growth parameters, the formation of W‐enriched vertical β ‐Ga 2− x W x O 3 nanocolumns embedded into the W‐deficient β ‐Ga 2− x W x O 3 matrix is reached. Detailed structural and morphological analyses evidently confirm the presence of β ‐Ga 2− x W x O 3 nanocomposite with a high structural and chemical quality. Furthermore, absorption and photoluminescence spectroscopy explains photo‐absorption dynamics and the recombination through possible donor–acceptor energy states. The proposed GWO‐VAN framework facilitates evenly dispersed nanoregions with asymmetric donor energy state distribution and thus forms build‐in potential at the vertical β ‐Ga 2− x W x O 3 interfaces. As a result, the overall heterostructure evinces photovoltaic nature under the UV irradiation. A responsivity of ≈30 A/W is observed with an ultrafast response time (≈350 µs) under transient triggering conditions. Corresponding detectivity and external quantum efficiency are 7.9 × 10 12 Jones and 1.4 × 10 4 %, respectively. It is believed that, while this is the first report exploiting GWO‐VAN architecture to manifest self‐biased solar‐blind UV photodetection, the implication of the approach is enormous in designing electronics for extreme environment functionality and has immense potential to demonstrate drastic improvement in low‐cost UV photodetector technology.

Das, Debabrata↗

Advances and perspectives on mass transfer and enzymatic hydrolysis in the enzyme-mediated lignocellulosic biorefinery: A review

We report enzymatic hydrolysis is a critical process for the cellulase-mediated lignocellulosic biorefinery to produce sugar syrups that can be converted into a whole range of biofuels and biochemicals. Such a process operating at high-solid loadings (i.e., scarcely any free water or roughly ≥ 15% solids, w/w) is considered more economically feasible, as it can generate a high sugar concentration at low operation and capital costs. However, this approach remains restricted and incurs “high-solid effects”, ultimately causing the lower hydrolysis yields with increasing solid loadings. The lack of available water leads to a highly viscous system with impaired mixing that exhibits strong transfer resistance and reaction limitation imposed on enzyme action. Evidently, high-solid enzymatic hydrolysis involves multi-scale mass transfer and multi-phase enzyme reaction, and thus requires a synergistic perspective of transfer and biotransformation to assess the interactions among water, biomass components, and cellulase enzymes. Porous particle characteristics of biomass and its interface properties determine the water form and distribution state surrounding the particles, which are summarized in this review aiming to identify the water-driven multi-scale/multi-phase bioprocesses. Further aided by the cognition of rheological behavior of biomass slurry, solute transfer theories, and enzyme kinetics, the coupling effects of flow-transfer-reaction are revealed under high-solid conditions. Based on the above basic features, this review lucidly explains the causes of high-solid hydrolysis hindrances, highlights the mismatched issues between transfer and reaction, and more importantly, presents the advanced strategies for transfer and reaction enhancements from the viewpoint of process optimization, reactor design, as well as enzyme/auxiliary additive customization.

45 multi-scale, multi-phase, enzymatic reaction↗

Error rate reduction of single-qubit gates via noise-aware decomposition into native gates

In the current era of Noisy Intermediate-Scale Quantum (NISQ) technology, the practical use of quantum computers remains inhibited by our inability to aptly decouple qubits from their environment to mitigate computational errors. In this paper, we introduce an approach by which knowledge of a qubit’s initial quantum state and the standard parameters describing its decoherence can be leveraged to mitigate the noise present during the execution of a single-qubit gate. We benchmark our protocol using cloud-based access to IBM quantum processors. On ibmq_rome, we demonstrate a reduction of the single-qubit error rate by 38%, from $1.6 \times 10 ^{-3}$ to $1.0 \times 10 ^{-3}$ , provided the initial state of the input qubit is known. On ibmq_bogota, we prove that our protocol will never decrease gate fidelity, provided the system’s T 1 and T 2 times have not drifted above 100 times their assumed values. The protocol can be used to reduce quantum state preparation errors, as well as to improve the fidelity of quantum circuits for which some knowledge of the qubits’ intermediate states can be inferred. This paper presents a pathway to using information about noise levels and quantum state distributions to significantly reduce error rates associated with quantum gates via optimized decomposition into native hardware gates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Comparing three generations of D-Wave quantum annealers for minor embedded combinatorial optimization problems

Abstract Quantum annealing (QA) is a novel type of analog computation that aims to use quantum mechanical fluctuations to search for optimal solutions of Ising problems. QA in the transverse Ising model, implemented on D-Wave quantum processing units, are available as cloud computing resources. In this study we report concise benchmarks across three generations of D-Wave quantum annealers, consisting of four different devices, for the NP-hard discrete combinatorial optimization problems unweighted maximum clique and unweighted maximum cut on random graphs. The Ising, or equivalently quadratic unconstrained binary optimization, formulation of these problems do not require auxiliary variables for order reduction, and their overall structure and weights are not highly variable, which makes these problems simple test cases to understand the sampling capability of current D-Wave quantum annealers. All-to-all minor embeddings of size 52, with relatively uniform chain lengths, are used for a direct comparison across the Chimera, Pegasus, and Zephyr device topologies. A grid-search over annealing times and the minor embedding chain strengths is performed in order to determine the level of reasonable performance for each device and problem type. Experiment metrics that are reported are approximation ratios for non-broken chain samples, chain break proportions, and time-to-solution for the maximum clique problem instances. How fairly the quantum annealers sample optimal maximum cliques, for instances which contain multiple maximum cliques, is quantified using entropy of the measured ground state distributions. The newest generation of quantum annealing hardware, which has a Zephyr hardware connectivity, performed the best overall with respect to approximation ratios and chain break frequencies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Bimolecular Chemistry in the Ultracold Regime

Advances in atomic, molecular, and optical physics techniques allowed the cooling of simple molecules down to the ultracold regime ([Formula: see text]1 mK) and opened opportunities to study chemical reactions with unprecedented levels of control. This review covers recent developments in studying bimolecular chemistry at ultralow temperatures. We begin with a brief overview of methods for producing, manipulating, and detecting ultracold molecules. We then survey experimental works that exploit the controllability of ultracold molecules to probe and modify their long-range interactions. Further combining the use of physical chemistry techniques such as mass spectrometry and ion imaging significantly improved the detection of ultracold reactions and enabled explorations of their dynamics in the short range. We discuss a series of studies on the reaction KRb + KRb → K 2 + Rb 2 initiated below 1 μK, including the direct observation of a long-lived complex, the demonstration of product rotational state control via conserved nuclear spins, and a test of the statistical model using the complete quantum state distribution of the products.

Chemistry↗

Physics-assisted Latent Space Dynamics Learning for Stiff Collisional-radiative Models

Collisional-radiative (CR) models describe the atomic processes in a plasma by tracking the population density in the ground and excited states for each charge state of the atom/ion. These models predict important plasma properties such as charge state distributions and radiative emissivity and opacity. Accurate descriptions of the CR balance of the plasma are essential in fusion whole device modeling, especially when significant impurities are introduced into the plasmas. In an integrated fusion plasma and CR simulation, a CR model, which is a high-dimensional stiff ODE, needs to be solved on each grid point in the configuration space, and can overwhelm the overall computational cost. In this work, we propose a machine-learning-based method that discovers a latent space and learns its corresponding latent dynamics, which can capture the essential physics to make accurate predictions at much lower online computational cost. The proposed approach is physics-assisted, due to its combination of a physical latent space and a data-driven latent space. It has been demonstrated that the proposed architecture can predict both the full-order CR dynamics and a physical quantity of interest accurately.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Predicting Radiation-Induced Plutonium Redox Chemistry using Multi-scale Modeling Methods

Over the the last 70 years plutonium (Pu) has been integral in the development of several technologies that have changed the world, yet our fundamental understanding of its chemistry is still far from complete. This is a testament to this element’s unique and complex properties, such as its ability to coexist as multiple oxidation states in aqueous solution. Careful manipulation of plutonium oxidation states is essential in the study and utilization of its rich chemistry. To achieve this level of control, a comprehensive mechanistic understanding of radiation-induced plutonium redox chemistry is critical due to the unavoidable exposure of plutonium to ionizing radiation fields, both inherent and from in-process applications. For this reason, we have developed an experimentally evaluated multi-scale computer model for the prediction of gamma radiation-induced Pu(IV) redox chemistry in concentrated nitric acid solutions (1.0, 3.0, and 6.0 M). Under these acidic, aqueous solution conditions, cobalt-60 gamma irradiation afforded negligible net change in the steady-state oxidation state distribution of Pu(IV). Multi-scale calculations, which are in excellent agreement with experimental data, indicate that this observation is due to radiation-induced redox cycling between Pu(IV) and Pu(III), as achieved by the reduction of Pu(IV) by radiolytic nitrous acid and hydrogen peroxide, and the oxidation of Pu(III) by nitrate and hydroxyl radicals. These radiation-induced redox processes are augmented by plutonium’s inherent disproportionation reactions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation

Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.

joint state estimation↗

State Requirements for Electric Distribution System Planning

Utilities have conducted distribution planning since they first began building and operating electricity systems. But filing these plans for regulatory and stakeholder review is a relatively recent phenomenon. This report summarizes legislative and regulatory requirements for regulated electric utilities to file some type of distribution system plan in 20 U.S. jurisdictions. Some plans focus on expedited cost recovery for certain types of distribution system improvements; other plans focus on investments for grid modernization or distributed energy resources. Increasingly, states are adopting requirements for Integrated Distribution Plans. Such plans provide holistic grid investment strategies that address state and local policies and increasing complexity at the grid edge. The report covers the following topics for distribution system plans, highlighting advanced practices: -State goals and objectives -Procedural requirements -Forecasting loads and distributed energy resources -Hosting capacity analysis -Baseline information requirements -Grid modernization strategy -Grid needs assessment -Non-wires solutions -Reliability and resilience analyses -Stakeholder engagement -Equity -Pilots -Coordination with other planning processes The report includes links to legislation; regulatory requirements, proceedings, and orders; and filed utility plans. The U.S. Department of Energy’s Office of Electricity provided funding support.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Reconfiguring band-edge states and charge distribution of organic semiconductor–incorporated 2D perovskites via pressure gating

Two-dimensional (2D) semiconductor heterostructures are key building blocks for many electronic and optoelectronic devices. Reconfiguring the band-edge states and modulating their interplay with charge carriers at the interface in a continuous manner have long been sought yet are challenging. Here, using organic semiconductor–incorporated 2D halide perovskites as the model system, we realize the manipulation of band-edge states and charge distribution via mechanical—rather than chemical or thermal—regulation. Compression induces band-alignment switching and charge redistribution due to the different pressure responses of organic and inorganic building blocks, giving controllable emission properties of 2D perovskites. We propose and demonstrate a “pressure gating” strategy that enables the control of multiple emission states within a single material. We also reveal that band-alignment transition at the organic-inorganic interface is intrinsically not well resolved at room temperature owing to the thermally activated transfer and shuffling of band-edge carriers. This work provides important fundamental insights into the energetics and carrier dynamics of hybrid semiconductor heterostructures.

42 ENGINEERING↗

Multi Time-scale Imputation aided State Estimation in Distribution System

With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile the heterogeneous multi-scale time-series data, we present a multi-task Gaussian process framework. This framework exploits the spatio-temporal correlation across the time-series data to impute data at any desired timescale while providing confidence bounds on the imputations. The value of the imputed data for distribution system operation is illustrated via a matrix completion based state estimation strategy. Results on the IEEE 37 bus distribution system reveals the superior performance of the proposed approach relative to linear interpolation approaches.

Dahale, Shweta↗

Non-parametric projections of the net-income distribution for all U.S. states for the Shared Socioeconomic Pathways

Abstract Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. Such impacts are especially important at subnational levels, but projections of income distributions at these levels are scarce. Here, we project U.S. state-level income distributions for the Shared Socioeconomic Pathways (SSPs). We apply a non-parametric approach, specifically a recently developed principal components algorithm to generate net income distributions for deciles across 50 U.S. states and the District of Columbia. We produce these projections to 2100 for three SSP scenarios in combination with varying projections of GDP per capita to represent a wide range of possible futures and uncertainties. In the generation of these scenarios, we also generated tax adjusted historical deciles by U.S. states, which we used for validating model performance. Our method thus produces income distributions by decile for each state, reflecting the variability in state income, population, and tax regimes. Our net income projections by decile can be used in both emissions- and impact-related research to understand distributional effects at various income levels and identify economically vulnerable populations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Distributed Ledger Technology for Fault Tolerant Distribution Grid Operations

This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers' privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.001% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Joint Topology Identification and State Estimation in Unobservable Distribution Grids

Many distribution system operations (e.g., state estimation, control, fault detection/localization) rely on the assumption that the underlying topology is accurately defined. In general, topology identification is a challenging problem in distribution systems as these systems are unobservable with a very limited number of available measurements. In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. Here, two reformulations of the original MINLP problems are investigated. Firstly, in order to remove the nonlinearity in the MINLP formulation, a mixed integer linear programming (MILP) problem that employs auxiliary variables is derived. Furthermore, to achieve a faster solution, convex relaxation of the original formulation is derived. Finally, using a Markovian model for topology changes, prior information about system topology is used to improve topology identification particularly when a limited amount of measurements is available. Simulation results on IEEE 37-bus test feeder and IEEE 123-bus test feeder illustrate the efficiency and scalability of the proposed approaches from both state estimation and topology identification point of view (even with 30% of available data).

42 ENGINEERING↗

Adaptive Linear State Estimation for Unbalanced Distribution System

The inclusion of PMU functionality in distribution relays enables the implementation of a linear state estimator (LSE) in Distribution Systems (DS). However, the unbalanced topology and phase coupling in distribution lines necessitate modifications to the LSE formulation. Additionally, the higher fault frequency in distribution systems requires a state estimation approach that is resilient to contingencies. This work proposes an adaptive linear state estimation algorithm tailored for unbalanced distribution systems with single-phase and two-phase laterals. Furthermore, a modified Optimal PMU Placement (OPP) strategy is introduced to ensure full observability in distribution systems with single-phase and two-phase buses. To maintain adaptability to topology changes, the state estimator incorporates circuit breaker status data provided by PMUs, ensuring robust performance during topology changes triggered by faults. The performance of the algorithm is verified on the IEEE 13-bus, 34-bus, and 123-bus systems.

PMUs↗

The Age of Evapotranspiration: Lower‐Bound Constraints From Distributed Water Fluxes Across the Continental United States

Abstract Unlike streamflow, which can be sampled in aggregate at the catchment outlet, evapotranspiration (ET) is spatially dispersed, challenging large‐scale age estimation. Here, we introduce an approach for constraining the age of ET via mass balance and present the minimum flux‐weighted age of ET across the continental United States using distributed, publicly available water flux data sets. The lower‐bound constraint on ET age can be calculated by assuming that ET is preferentially sourced from the most recent precipitation through a last‐in, first‐out algorithm. From 2012 to 2017, ET was at least several months old across large areas of the western continental United States, including in Mediterranean and (semi‐)arid climate zones and shrub and evergreen needleleaf plant communities. The primary limitation of this approach is that it provides only a minimum flux‐weighted average age to satisfy the mass balance of outgoing fluxes; true ET fluxes are composed of distributions of ages and may be composed of much older water. The primary advantage of the approach is that flux time series of precipitation and ET are sufficient to constrain ET age, and model parameterization is unnecessary. ET ages can be used to validate tracer‐aided and modeling approaches and inform studies of biogeochemistry, water‐rock interactions, and plant water sourcing under drought.

Hahm, W. J.↗