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Strategies for Computer-Aided Discovery of Novel Open-Shell Polymers
Organic π-conjugated polymers with a triplet ground state have been the focus of recent research for their interesting and unique electronic properties, arising from the presence of the two unpaired electrons. These compounds are usually built from alternating electron-donating and electron-accepting monomer pairs which lower the HOMO–LUMO gap and yield a triplet state instead of the typical singlet ground state. In this paper, we use density functional theory calculations to explore the design rules that govern the creation of a ground-state triplet conjugated polymer and find that a small HOMO–LUMO gap in the singlet state is the best predictor for the existence of a triplet ground state, compared to previous use of a pro-quinoidal bonding character. Furthermore, this work can accelerate the discovery of new stable triplet materials by reducing the computational resources needed for electronic-state calculations and the number of potential candidates for synthesis.
Ce x Zr 1– x O 2 -Supported CrO x Catalysts for CO 2 -Assisted Oxidative Dehydrogenation of Propane–Probing the Active Sites and Strategies for Enhanced Stability
CO 2 -assisted oxidative dehydrogenation of propane (CO 2 -ODH) represents an attractive approach for propylene production and CO 2 utilization. As a soft oxidant, CO 2 can minimize overoxidation of the hydrocarbons to enhance the propylene selectivity while increasing the equilibrium yield. However, a major challenge of CO 2 -ODH is the rapid deactivation of the catalysts. The current study focuses on designing Ce x Zr 1–x O 2 -mixed oxide-supported CrO x catalysts for CO 2 -ODH with enhanced product selectivity and catalyst stability. By doping 0–30% Ce in the Ce x Zr 1–x O 2 mixed oxide support, propane conversion of 53–79% was achieved at 600 °C, with propylene selectivity up to 82%. Compared to the pure ZrO 2 -supported catalyst (i.e., 5 wt %Cr/ZrO 2 ), 20–30 %Ce doped catalysts (i.e., 5 wt %Cr/Ce 0.2 Zr 0.8 O 2 and 5 wt %Cr/Ce 0.3 Zr 0.7 O 2 ) inhibited the formation of CH 4 and ethylene and improved propylene selectivity from 57 to 77–82%. Detailed characterizations of the 5%Cr/Ce 0.2 Zr 0.8 O 2 catalyst and density functional theory (DFT) calculations indicated that Cr 3+ is the active species during the CO 2 -ODH reaction, and the reaction follows a non-redox dehydrogenation pathway. Coke formation was determined to be the primary reason for catalyst deactivation, and the addition of Ce to the ZrO 2 support greatly enhanced the coke resistance, leading to superior stability. Furthermore, coke removal by oxidizing the catalyst in air is effective in restoring its activity.
Defect Engineering of Ceria Nanocrystals for Enhanced Catalysis via a High-Entropy Oxide Strategy
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Systematic Merging of Nonfullerene Acceptor π-Extension and Tetrafluorination Strategies Affords Polymer Solar Cells with >16% Efficiency
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Polerovirus N-terminal readthrough domain structures reveal molecular strategies for mitigating virus transmission by aphids
Abstract Poleroviruses, enamoviruses, and luteoviruses are icosahedral, positive sense RNA viruses that cause economically important diseases in food and fiber crops. They are transmitted by phloem-feeding aphids in a circulative manner that involves the movement across and within insect tissues. The N-terminal portion of the viral readthrough domain ( N RTD) has been implicated as a key determinant of aphid transmission in each of these genera. Here, we report crystal structures of the N RTDs from the poleroviruses turnip yellow virus (TuYV) and potato leafroll virus (PLRV) at 1.53-Å and 2.22-Å resolution, respectively. These adopt a two-domain arrangement with a unique interdigitated topology and form highly conserved dimers that are stabilized by a C-terminal peptide that is critical for proper folding. We demonstrate that the PLRV N RTD can act as an inhibitor of virus transmission and identify N RTD mutant variants that are lethal to aphids. Sequence conservation argues that enamovirus and luteovirus N RTDs will follow the same structural blueprint, which affords a biological approach to block the spread of these agricultural pathogens in a generalizable manner.
Data for Protoplast Fusion as a Strategy to Increase Ploidy in Rhodotorula toruloides for Strain Development
Rhodotorula toruloides is a red oleaginous yeast with growing commercial interest because of its hardiness and exceptional lipid production capacity. Because it is a basidiomycete yeast with a complex life cycle, many of the classical breeding methods used with ascomycetes are unavailable for strain improvement. However, we have been able to construct polyploid yeast by fusing protoplasts of parents with the same mating type. Fusing of Y-6985 (A2) and Y-48190 (A2), which had been transformed with complementary antibiotic markers, led to the recovery of two diploids and one triploid. The stability of the fusion yeasts was tested by plating them on non-selective medium after several growth cycles under antibiotics and then testing five colonies per strain for nuclear DNA contents using flow cytometry and standard cell cycle analysis: the triploid and one diploid were stable. Fusants inherited their mitochondria from a single parent, which was demonstrated using restriction fragment length polymorphism (RFLP) of mitochondrial DNA. The phenotypic properties of the parents and fusants were compared in glucose fed-batch bioreactor studies and cellulosic sugar batch cultures. The final lipid titers for the fed-batch cultures were 24.9–39.7 g/L with Y-6985 and the diploid and triploid performing the best and worst, respectively. The fusants demonstrated intermediate hardiness for growth on hydrolysate prepared with dilute-acid pretreated switchgrass and were outperformed by Y-48190. Unlike one of the haploid parents, the fusants grew in 70% v/v concentrated hydrolysate. However, they did not grow as fast as the other haploid. In this study, a modernized protoplast fusion method is resurrected a useful tool for strain development in this yeast, which is complementary with other available methods.
MassIVE MSV000088319 - Metaproteomics reveals enzymatic strategies deployed by anaerobic microbiomes to maintain lignocellulose deconstruction at high solids: Metaproteomics reveals enzymatic strategies deployed by anaerobic microbiomes to maintain lignocellulose deconstruction at high solids
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IBPSA Project 2 BOPTEST: An update on the test cases available in the framework for testing advanced control strategies in buildings
Project 2 develops software infrastructure, test cases, and extensions for the Building Optimization Testing Framework (BOPTEST) to address the expanding needs of building and urban energy system controls through open international collaboration. This paper provides an overview of the new test cases available as of BOPTEST version 0.7.1. Each test case is developed using open-source Modelica libraries and Spawn of EnergyPlus, enabling the creation of high-fidelity building models that incorporate envelope dynamics, Heating Ventilation and Air Conditioning (HVAC) systems, and explicit control representations. Currently, eight test cases are available, with five additional cases under development. These test cases cover a wide range of climates, building types, and HVAC systems. This paper compiles and summarizes test case descriptions, cites original manuscripts that developed them for a more detailed description, and reports baseline control performance metrics. Furthermore, two example applications are presented: one illustrating different levels of control, from supervisory to low-level, and another demonstrating how Model Predictive Control (MPC) solutions must be adapted from continuous to integer to control some building actuators.
Hemispherically symmetric strategies for stratospheric aerosol injection
Stratospheric aerosol injection (SAI) comes with a wide range of possible design choices, such as the location and timing of the injection. Different stratospheric aerosol injection strategies can yield different climate responses; therefore, understanding the range of possible climate outcomes is crucial to making informed future decisions on SAI, along with the consideration of other factors. Yet, to date, there has been no systematic exploration of a broad range of SAI strategies. This limits the ability to determine which effects are robust across different strategies and which depend on specific injection choices. This study systematically explores how the choice of SAI strategy affects climate responses in one climate model. Here, we introduce four hemispherically symmetric injection strategies, all of which are designed to maintain the same global mean surface temperature: an annual injection at the Equator (EQ), an annual injection of equal amounts of SO 2 at 15° N and 15° S (15N+15S), an annual injection of equal amounts of SO 2 at 30° N and 30° S (30N+30S), and a polar injection strategy that injects equal amounts of SO 2 at 60° N and 60° S only during spring in each hemisphere (60N+60S). We compare these four hemispherically symmetric SAI strategies with a more complex injection strategy that injects different quantities of SO 2 at 30° N, 15° N, 15° S, and 30° S in order to maintain not only the global mean surface temperature but also its large-scale horizontal gradients. All five strategies are simulated using version 2 of the Community Earth System Model with the middle atmosphere version of the Whole Atmosphere Community Climate model, version 6, as the atmospheric component, CESM2(WACCM6-MA), with the global warming scenario, Shared Socioeconomic Pathway (SSP)2-4.5. We find that the choice of SAI strategy affects the spatial distribution of aerosol optical depths, injection efficiency, and various surface climate responses. In addition, injecting in the subtropics produces more global cooling per unit injection, with the EQ and the 60N+60S cases requiring, respectively, 59 % and 50 % more injection than the 30N+30S case to meet the same global mean temperature target. Injecting at higher latitudes results in larger Equator-to-pole temperature gradients. While all five strategies restore Arctic September sea ice, the high-latitude injection strategy is more effective due to the SAI-induced cooling occurring preferentially at higher latitudes. These results suggest trade-offs wherein different strategies appear better or worse, depending on which metrics are deemed important.
Investigation of Point-Contact Strategies for CFD Simulations of Pebble-Bed Reactor Cores
This study numerically investigated the effects of various contact strategies on the thermal hydraulic behavior within a structured bed of 100 explicitly modeled pebbles. Four contact strategies and two thermal hydraulic conditions were considered. The strategies to avoid contact singularities include decreasing the pebble diameter, increasing the pebble diameter, bridging the pebble surfaces near the contact region, and capping the pebble surfaces near the contact region. One strategy, Strategy 3a, which involves bridging with a cylinder equal to 10% of the pebble diameter, was selected as the baseline strategy because it addressed the contact singularity while minimizing the geometric changes that affect the bed porosity. The two thermal hydraulic conditions were full-power operation (Case 1) and pressurized loss of forced cooling or PLOFC (Case 2). Simulations of the conjugate heat transfer within the structured bed were performed using the Reynolds-averaged Navier–Stokes approach with the realizable k-ϵ turbulence model and two-layer all y + wall treatment. The thermal-fluid quantities of interest were compared between the contact strategies for each case. In Case 1, the hydraulic behavior was sensitive to the contact strategy, with large differences in the pressure drop (30%) and volume-average velocity (4%). The thermal behavior was not sensitive, with less than a 0.5% difference across the strategies. To better understand the separate effects of each heat transfer mode, Case 2 was divided into the following subcases: conduction (Case 2a); conduction/radiation (Case 2b); and conduction/radiation/convection (Case 2c). Case 2a represents an early phase of the PLOFC transient. Case 2b represents an intermediate phase of the PLOFC transient, with the pebble temperatures sufficiently high for the radiative heat transfer to be non-negligible. Case 2c represents a late phase of the PLOFC transient after the establishment of the natural circulation of the heat transfer fluid. For Case 2, large differences in the contact strategy were observed only in Case 2a with only conduction. The difference in the maximum pebble temperature was 23% in Case 2a, 2% in Case 2b, and 0.3% in Case 2c.
An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring
The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.
Greenhouse gas emissions reduction strategies that maximize portfolio-wide life cycle cost reduction, resilience, and environmental justice benefits
While strategies to achieve net-zero emissions at an individual site are well understood, new analysis methods are required for organizations seeking to achieve net-zero across multiple facilities, each with concurrent priority goals. At a portfolio level, distinct locations present varied challenges that cannot be addressed through singular solutions, and competing goals can take precedence with the assumption that net-zero emissions strategies deter from energy resilience and cost savings, therefore negatively impacting nearby communities. This study tests these assumptions by analyzing 16 diverse sites (varying in size, climate, and energy use) to identify strategies that reduce emissions and assess the impact these strategies have on life cycle costs, resilience, and communities with environmental justice concerns. Methods were developed to approximate missing information essential to net-zero evaluation. Established methods were augmented to evaluate life cycle costs, resilience, and environmental justice impacts across a set of strategies and accommodate the multi-criteria analyses. Potential benefits from identified strategies were quantified using site characteristics and a set of corresponding metrics. The net-zero analysis found that 11 sites could use on-site strategies to eliminate all but 2% of emissions generated. The remaining emissions can be offset, for instance through sequestration, executed at the portfolio scale. On-site carbon-free energy was found to reduce 51% of emissions across all sites; efficiency reduced 19% of emissions; sequestration 16%; procured carbon-free energy 15%; fuel switching 1.6%; and fleet electrification 1.3%. Building electrification, however, increased emissions by 4.4%. Different strategies also provide cost, resilience, and/or environmental justice benefits—the degree to which varies with individual site conditions. The findings indicate an advantage to considering the strategies as a comprehensive set, which leads to co-benefits, both in the ability to achieve net-zero goals and in advancing other goals. The results present the case for comprehensive advanced planning at the portfolio level to prioritize investments that will balance the minimization of emissions and life cycle cost with the maximization of resilience and environmental justice benefits. The novel methods for evaluation and integration, valuation of benefits, and consideration at the portfolio scale allow organizations to select investments that simultaneously address multiple key priorities.
Intersecting heuristic adaptive strategies, building design and energy saving intentions when facing discomfort environment: A cross-country analysis
Occupants' adaptive strategies play an important role in the energy consumption of office buildings. Previous research has mostly focused on the adaptive strategies triggered by occupants' indoor discomfort; however, it is crucial to understand if specific adaptive strategies are linked to occupants' energy-saving intentions. This study explores the relationships among employees’ heuristic decision-making in their first choice of adaptive strategies (technological solutions or personal adjustments) when facing extreme discomfort conditions, and their energy-saving intentions, then links these patterns with building design, workplace contextual factors, and demographics. A cross-sectional survey was collected among university employees from China, Brazil, Italy, Poland, Switzerland, and the US. Our results demonstrated that the accessibility to indoor environmental controls (IECs) and office type are the significant factors for adaptive strategies. There was a positive relationship between the number of IEC features and the percentage of employees choosing a technological solution. When feeling too hot, occupants in private offices are more likely to adopt a technological solution, whereas occupants in cubicles are more likely to choose a personal adjustment. Occupants with energy-saving intentions are less likely to choose thermostat adjustments or use portable devices as adaptive strategies than their counterparts. Lastly, the cluster analysis suggests females were more likely to use adaptive strategies for energy-saving purposes than males. The majority of occupants would turn on/off lighting to save energy. The study provides the contributions in the connection between the heuristic decision-making process and energy-saving intentions and recommendations on design strategies for building architects, engineers, and managers.
Strategies for microgrid operation under real-world conditions
Microgrids are an increasingly relevant technology for integrating renewable energy sources into electricity systems. Based on a microgrid implementation in California, in this study we investigate microgrid operation under real-world conditions. These conditions have not yet been considered in combination and encompass energy charges, demand charges, export limits, as well as uncertainty about future electricity demand and generation in the microgrid. Under these conditions, we evaluate the performance of two frequently applied groups of strategies for microgrid operation. The first group is composed of proactive strategies that optimize decisions based on forecasts of future electricity generation and demand. The second group includes reactive strategies that make operational decisions based exclusively on the current state of the microgrid. We evaluate the performance of the strategies under varying operational parameters, forecast accuracies, and microgrid configurations—well beyond our Californian showcase. Our results confirm the expectation that proactive strategies outperform reactive ones in the majority of settings. Yet, reactive strategies can perform better under short control intervals or under moderate prediction errors of PV generation or demand. Furthermore, the interplay between real-world conditions and operational strategies reveals several additional insights for research on microgrid operation. First, we find that demand charges and export limits decisively affect microgrid performance. Second, the impact of forecast errors is highly non-linear and non-monotonous. Third, escalating negative interactions between forecast errors and demand charges make proactive strategies benefit from longer control intervals. This result is contrary to existing best practice, which promotes short control intervals to minimize the impact of uncertainty.
Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure
Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.
Study of Inverter Control Strategies on the Stability of Low-Inertia Microgrid Systems
This paper investigates the stability of low-inertia microgrid systems with two control strategies that have different percentages of grid-forming (GFM) inverters. The first control strategy has approximately 50% GFM inverters, and all the battery inverters are working in GFM control mode. Originally, the second control strategy has approximately 10% GFM inverters, with only two battery inverters working in GFM control mode and the rest working in grid-following (GFL) PQ control mode based on current control, which cannot stabilize the microgrid system. Then, the second control strategy is modified to change the GFM inverters from droop control to isochronous control and the GFL battery inverters from traditional current control to voltage control for power control. Both control strategies can maintain system stability; however, the first control strategy can better handle contingency events. The study indicates that 1) a microgrid system with a higher percentage of GFM inverters has better stability; and 2) a microgrid with a lower percentage of GFM inverters can have poor stability, but improved control strategies in inverters can improve system stability. This study improves the understanding of how different percentages of GFM inverters and inverter control strategies affect the system stability of low-inertia microgrids.