Results for “Output” — Page 40

DOE OSTI2021

Revisiting the Temporal Leontief Inverse: New Insights on the Analysis of Regional Technological Economic Change

The current availability of longer series of input-output tables, as well as the release of global input-output databases, has fostered a growing literature analyzing changes in the economic structure and their drivers. In this paper, we take advantage of these time-series by proposing a methodology designed to trace the contribution of different drivers of the change in interindustrial relationships over time. Based on the Temporal Leontief Inverse (TLI), the Extended TLI (ETLI) decomposes the economy-wide effects of changes in direct interindustrial links between years, isolating the impact of different determinants of economic (environmental, energy, etc.) spillovers according to the interests of the researcher. For example, one can explore how the multipliers of a particular industry were affected by changes in technology of other sectors and in the own sector; by changes in trade patterns in specific countries; by indirect changes in intraregional production chains in foreign nations; etc. The ETLI is illustrated by uncovering certain hidden effects not captured in a previous application of the original TLI to the Chicago region between 1980-1997.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.

42 ENGINEERING↗

DOE OSTI2021

Stacked Low-Inertia Converter or Solid-State Transformer: Modeling and Model Predictive Priority-Shifting Control for Voltage Balance

This paper presents control challenges of stacked low-inertia converter (SLIC) or cascaded reduced dc-link solid-state transformer (SST) and proposes a novel model predictive priority-shifting (MPPS) control with implicit modulator and a discrete-time large-signal model for voltage balancing and dc-link regulation. Low-inertia converters, featuring small electrolytic capacitor-less dc links, dramatically reduce cost, size, and weight compared to conventional solutions. However, without a large dc-link buffer, the input and output are tightly coupled, leading to significant control challenges. The control becomes even more challenging with these converters stacked input-series output-parallel (ISOP) for medium-voltage (MV) grid, which causes coupling between the modules besides the coupling within each module. This paper analyzes the multi-objective, multi-degree of freedom control problem, using the modular soft-switching solid-state transformer (M-S4T) as an example of the SLIC. First, distribution of control efforts under controller saturation is critical because multiple control objectives can be conflicting, especially when the module voltages are unbalanced and are being restored. The MPPS can shift the priorities to address this issue. Second, due to the low inertia and high dc-link ripple, classic space vector pulse-width modulation (SVPWM), average model with small-ripple assumption, and control design based on small-signal model cannot accurately modulate, model, and control the nonlinear reduced dc link. Therefore, a discrete-time large-signal model of the M-S4T is established to derive the predictive control in the MPPS. The MPPS and the PI control are compared in MV simulations to show the issue of applying the PI to the SLIC and the effectiveness of the MPPS for voltage balancing and dc-link regulation in a deadbeat manner. Finally, the proposed control is tested on a 5 kV ISOP SiC SST prototype to verify priority shifting to address controller saturation issue and fast and robust voltage balancing.

14 SOLAR ENERGY↗

DOE OSTI2021

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

In-Situ Blade Strain Measurements of a Crossflow Turbine Operating in a Tidal Flow

This data was collected between October 25 and December 12 of 2022 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). The goal was to collect blade strain data from a crossflow turbine operating in a tidal flow. A table in ('Deployment Schedule.PNG') outlines the various dates when each instrument was operational, and more details can be found via literature listed in 'Related Publications'.txt. This dataset includes zipped folders for each instrument containing data in .csv files for the relevant duration specific to each instrument, along with separate README file for each measurement. Some instrument files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. All data contained in this submission is unfiltered/unprocessed data unless otherwise noted in the README file. Blade strain was measured using 8 foil-based strain gauges along the span of a single turbine blade. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) both upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal flow with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Chancey 2019. The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. A single blade of this turbine was further modified to accommodate 8 full-bridge strain gauges (Bharath et al 2023, Bichanich et al 2024). For power performance and other relevant details on the turbine and its characteristics, see O'Byrne 2022.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

TEAMER: Triton Systems Oscillating Water Column Modeling Data and Report

This dataset provides the output of six Wave Energy Converter Simulator (WEC-Sim) simulations and accompanying documentation for the modeling of Triton Systems' oscillating water column (OWC) system at tank scale (validated using available data for tuning the model, Tests 1-2) and deployment scale (for which no validation data is available, Tests 4-6). Included are the output data in a MATLAB file structure, a comprehensive report on the modeling and design of the Triton OWC system, and a link to the WEC-Sim GitHub page. This work was supported by funding from TEAMER RFTS 5 (Request for Technical Support).

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Is Clean Hydrogen Production a Good Fit for Questa? (Final Economic Impact Results) [Slides]

The Village of Questa, New Mexico is aiming to become a regional clean energy hub with robust and diverse employment opportunities for the local community supported by the energy sector and by other businesses inspired or attracted by abundant clean energy, outdoor recreation, and cultural opportunities. A coalition of stakeholders in the Village of Questa, comprising the Village, Kit Carson Electric Cooperative (KCEC), Questa Economic Development Fund, and Chevron, is exploring options to develop hydrogen production facilities as an opportunity to create jobs, provide reliable clean energy, and utilize former mine resources. Questa is home to a molybdenum mine owned by Chevron that closed in 2014. Several residents in Questa and surrounding communities lost their jobs when the mine closed and transitioned from active operations into environmental remediation. Although remediation efforts have been ongoing since 2014 and are expected to continue for at least 16 more years, the number of jobs with Chevron is much smaller now than it was before the closure. Between available workforce, brownfield land, and water rights formerly supporting mine operations but now in a transition period, there are considerable local resources that could be directed toward clean energy generation. Questa's electricity supply is already 100% solar during daylight hours thanks to Kit Carson Electric Cooperative's (KCEC's) strategic decision-making and partnering over the last decade. Now, Questa, KCEC, and Chevron are exploring the potential costs and benefits of siting an electrolytic hydrogen production facility and additional solar photovoltaic (PV) capacity in Questa to further advance the region's clean energy economy. In this report, we estimated the potential economic impacts (i.e., jobs, value added, gross output, tax revenue) of constructing and operating a combined hydrogen (32 MW polymer electrolyte membrane electrolizer + 7.5 MW fuel cell) and solar facility (22.5 MW) in the Village of Questa, as well as the resulting economic spillovers to Taos County and the state of New Mexico. We employ an input-output model that leverages IMPLAN's economic data for the region complemented by construction and operating expenses estimated by NREL and feedback from the local coalition to evaluate the direct, indirect and induced effects of the project construction (transient impacts) and operation (more permanent impacts). Based on the area's average trade profile, feedback from the coalition and current market conditions, these projects are expected to support 487 full-time equivalent jobs during construction, generating $\$24$ million in income for those workers and $\$82$ million in local economic activity in the state. Of those jobs, 106 are expected to be construction sector jobs. These investments are also estimated to add $\$36.5$ million to New Mexico's gross state product (GSP). In the Village of Questa, we estimate 16 jobs will be supported in construction and transportation industries, generating $\$0.9$ million in earnings. In Taos County, the construction phase is expected to support 285 jobs primarily in construction and professional services, while manufacturing jobs dominate the results for the Rest of New Mexico. The Village is also estimated to receive $\$0.9$ million in tax revenue from the construction phase alone. Once in operation, the project continues to impact the state and Questa. Around 20 jobs (full-time equivalent for each year of operation) are supported across New Mexico, with approximately 11 directly employed in Questa by both facilities. The total annual local economic activity supported by ongoing operations is just over $\$1.3$ million/yr, generating $\$1.6$ million/yr in additional income in the state. Annual operations are estimated to add $\$2.1$ million to the state's GSP. The Village is expected to receive around $\$43,000$/yr in tax revenue. Impacts vary significantly depending on which businesses are supplying materials, equipment and services, and where construction workers reside. Choosing local suppliers will most benefit Questa and the New Mexico economy, adding up to 500 jobs during construction and 13 long-term jobs. Local and state governments may consider ways to incentivize local businesses in order to maximize economic benefits.

08 HYDROGEN↗

DOE OSTI2024

Is Clean Hydrogen Production a Good Fit for Questa? Final Economic Impact Results

The Village of Questa, New Mexico is aiming to become a regional clean energy hub with robust and diverse employment opportunities for the local community supported by the energy sector and by other businesses inspired or attracted by abundant clean energy, outdoor recreation, and cultural opportunities. A coalition of stakeholders in the Village of Questa, comprising the Village, Kit Carson Electric Cooperative (KCEC), Questa Economic Development Fund, and Chevron, is exploring options to develop hydrogen production facilities as an opportunity to create jobs, provide reliable clean energy, and utilize former mine resources. Questa is home to a molybdenum mine owned by Chevron that closed in 2014. Several residents in Questa and surrounding communities lost their jobs when the mine closed and transitioned from active operations into environmental remediation. Although remediation efforts have been ongoing since 2014 and are expected to continue for at least 16 more years, the number of jobs with Chevron is much smaller now than it was before the closure. Between available workforce, brownfield land, and water rights formerly supporting mine operations but now in a transition period, there are considerable local resources that could be directed toward clean energy generation. Questa's electricity supply is already 100% solar during daylight hours thanks to Kit Carson Electric Cooperative's (KCEC's) strategic decision-making and partnering over the last decade. Now, Questa, KCEC, and Chevron are exploring the potential costs and benefits of siting an electrolytic hydrogen production facility and additional solar photovoltaic (PV) capacity in Questa to further advance the region's clean energy economy. In this report, we estimated the potential economic impacts (i.e., jobs, value added, gross output, tax revenue) of constructing and operating a combined hydrogen (32 MW polymer electrolyte membrane electrolizer + 7.5 MW fuel cell) and solar facility (22.5 MW) in the Village of Questa, as well as the resulting economic spillovers to Taos County and the state of New Mexico. We employ an input-output model that leverages IMPLAN's economic data for the region complemented by construction and operating expenses estimated by NREL and feedback from the local coalition to evaluate the direct, indirect and induced effects of the project construction (transient impacts) and operation (more permanent impacts). Based on the area's average trade profile, feedback from the coalition and current market conditions, these projects are expected to support 487 full-time equivalent jobs during construction, generating $\$24$ million in income for those workers and $\$82$ million in local economic activity in the state. Of those jobs, 106 are expected to be construction sector jobs. These investments are also estimated to add $\$36.5$ million to New Mexico's gross state product (GSP). In the Village of Questa, we estimate 16 jobs will be supported in construction and transportation industries, generating $\$0.9$ million in earnings. In Taos County, the construction phase is expected to support 285 jobs primarily in construction and professional services, while manufacturing jobs dominate the results for the Rest of New Mexico. The Village is also estimated to receive $\$0.9$ million in tax revenue from the construction phase alone. Once in operation, the project continues to impact the state and Questa. Around 20 jobs (full-time equivalent for each year of operation) are supported across New Mexico, with approximately 11 directly employed in Questa by both facilities. The total annual local economic activity supported by ongoing operations is just over $\$1.3$ million/yr, generating $\$1.6$ million/yr in additional income in the state. Annual operations are estimated to add $\$2.1$ million to the state's GSP. The Village is expected to receive around $\$43,000$/yr in tax revenue. Impacts vary significantly depending on which businesses are supplying materials, equipment and services, and where construction workers reside. Choosing local suppliers will most benefit Questa and the New Mexico economy, adding up to 500 jobs during construction and 13 long-term jobs. Local and state governments may consider ways to incentivize local businesses in order to maximize economic benefits.

08 HYDROGEN↗

DOE OSTI2023

Assessment of Mechanical Draft Cooling Tower Thermal Emissions from Visual Images of Plumes

Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were simultaneously captured by multiple stationary digital cameras surrounding the cooling tower. An analysis technique combining structure from motion (SfM), a neural-network-based image segmentation algorithm, and space carving was used to quantify the volumes. Afterwards, the power output was computed using novel techniques in the one-dimensional code that included cooling tower exhaust plume adjacency effects implemented with a modified version of the entrainment function, weather data averaged from eleven stations, and fan operations at the times when plume volumes were measured. The model was then compared with the averaged observed power output, and it validated well with an average error ranging from 6 to 12%, depending on the meteorological data used in the simulations. This methodology can possibly determine power plant fuel consumption rates by applying visible imagery.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Perovskite Solar Cells Go Bifacial—Mutual Benefits for Efficiency and Durability

Bifacial solar cells hold the potential to achieve a higher power output per unit area than conventional monofacial devices without significantly increasing manufacturing costs. However, efficient bifacial designs are challenging to implement in inorganic thin-film solar cells because of their short carrier lifetimes and high rear surface recombination. The emergence of perovskite photovoltaic (PV) technology creates a golden opportunity to realize efficient bifacial thin-film solar cells, owing to their outstanding optoelectronic properties and unique features of device physics. More importantly, transparent conducting oxide electrodes can prevent electrode corrosion by halide ions, mitigating one major instability issue of the perovskite devices. Here, the theory of bifacial PV devices is summarized and the advantages of bifacial perovskite solar cells, such as high power output, enhanced device durability, and low economic and environmental costs, are reviewed. The limitations and challenges for bifacial perovskite solar cells are also discussed. Lastly, the awareness of bifacial solar cells as a feasible commercialization pathway of perovskite PV for mainstream solar power generation and building-integrated PV is advocated and future research directions are suggested.

14 SOLAR ENERGY↗

DOE OSTI2022

More Powerful Twistron Carbon Nanotube Yarn Mechanical Energy Harvesters

We report that stretching a coiled carbon nanotube (CNT) yarn can provide large, reversible electrochemical capacitance changes, which convert mechanical energy to electricity. Here, it is shown that the performance of these “twistron” harvesters can be increased by optimizing the alignment of precursor CNT forests, plastically stretching the precursor twisted yarn, applying much higher tensile loads during precoiling twist than for coiling, using electrothermal pulse annealing under tension, and incorporating reduced graphene oxide nanoplates. The peak output power for a 1 and a 30 Hz sinusoidal deformation are 0.73 and 3.19 kW kg -1 , respectively, which are 24- and 13-fold that of previous twistron harvesters at these respective frequencies. This performance at 30 Hz is over 12-fold that of other prior-art mechanical energy harvesters for frequencies between 0.1 and 600 Hz. The maximum energy conversion efficiency is 7.2-fold that for previous twistrons. Twistron anode and cathode yarn arrays are stretched 180° out-of-phase by locating them in the negative and positive compressibility directions of hinged wine-rack frames, thereby doubling the output voltage and reducing the input mechanical energy.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Millimeter-Thick Liquid Crystalline Elastomer Actuators Prepared by-Surface-Enforced Alignment

Here, liquid crystalline elastomers (LCE) are thermally cyclable, compliant actuators with compelling mechanical properties. The large and programmable deformation of LCE has led to numerous functional examinations spanning optics, medical devices, and robotics. A well-established method to prepare complex LCE actuators is to utilize surface-enforced photoalignment. Herein, a facile and scalable approach is reported to circumvent the physical limits of surface-enforced alignment (e.g., samples that are 50 µm or less) to amplify the achievable force output in LCE. Applying an approach termed direct layering, the thermomechanical response of LCE elements prepared with +1 disclination patterns in a range of compositions and thicknesses is contrasted. The design and preparation of +1 disclination patterns and arrays is explored to assess the contribution of sample geometry and overlap to deformation and force output. The methodology detailed in this contribution allows for the preparation of elements ≈1 mm in thickness that are capable of actuating large objects. Furthermore, the fabrication of these elements uniquely enables the realization of mechanical instabilities to hasten the actuation rate in response to thermal change and to enable leaping.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Low-Cost Durable Na-FeCl2 Battery with Ultrahigh Rate Capability

Na-based batteries have long been regarded as an inexpensive, sustainable candidate for large-scale stationary energy storage applications. Unfortunately, the market penetration of conventional Na-NiCl2 batteries is approaching its limit for several reasons, including limited rate capability and high Ni cost. Herein, a Na-FeCl2 battery operating at 190 °C is reported that allows a capacity output of 116 mAh g-1 at an extremely high current density of 33.3 mA cm-2 (˜0.6C). The superior rate performance is rooted in the intrinsically fast kinetics of the Fe/Fe2+ redox reaction. Furthermore, it is demonstrated that a small amount of Ni additive (10 mol%) effectively mitigates capacity fading of the Fe/NaCl cathode caused by Fe particle pulverization during long-term cycling. The modified Fe/Ni cathode exhibits excellent cycling stability, maintaining a discharge energy density of over 295 Wh kg-1 for 200 cycles at 10 mA cm-2 (˜C/5).

Zhan, Xiaowen↗

DOE OSTI2025

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Process prediction and detection of faults using probabilistic bidirectional recurrent neural networks on real plant data

Attaining Industry 4.0 for manufacturing operations requires advanced monitoring systems and real-time data analytics of plant data, among other topics. We propose a Probabilistic Bidirectional Recurrent Network (PBRN) for industrial process monitoring for the early detection of faults. The model is based on a Gated Recurrent Unit (GRU) neural network that allows the model to retain long-term dependencies between sensor data along a time horizon, hence learning the dynamic behavior of the process. To reduce the false-positive detection rate of the model, we compel the model to learn from a highly noisy sensor reading while outputting noise-free sensor outputs. The performance of the proposed model is compared to other data-driven statistical process monitoring schemes using real plant data from an industrial Air Separations Unit (ASU) containing noisy sensor readings. We show that the model can learn from noisy data without reducing its performance. Using two different fault cases, we demonstrate the model’s ability to carry out early fault detection with average false-positive rates of 2.9% and 4.9% for both fault cases. The missed detection rates are 0.1% and 0.2%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Biopolymer‐assisted Synthesis of P‐doped TiO 2 Nanoparticles for High‐performance Lithium‐ion Batteries: A Comprehensive Study

Abstract TiO 2 material has gained significant attention for large‐scale energy storage due to its abundant, low‐cost, and environmentally friendly properties, as well as the availability of various nanostructures. Phosphorus doping has been established as an effective technique for improving electronic conductivity and managing the slow ionic diffusion kinetics of TiO 2 . In this study, non‐doped and phosphorus doped TiO 2 materials were synthesized using sodium alginate biopolymer as chelating agent. The prepared materials were evaluated as anode materials for lithium‐ion batteries (LIBs). The electrodes exhibit remarkable electrochemical performance, including a high reversible capacity of 235 mAh g −1 at 0.1 C and excellent first coulombic efficiency of 99 %. An integrated approach, combining operando XRD and ex‐situ XAS, comprehensively investigates the relationship between phosphorus doping, material structure, and electrochemical performance, reinforced by analytical tools and first principles calculations. Furthermore, a full cell was designed using 2 %P‐doped TiO 2 anode and LiFePO 4 cathode. The output voltage was about 1.6 V with high initial specific capacity of 148 mAh g −1 , high rate‐capability of 120 mAh g −1 at 1 C, and high‐capacity retention of 96 % after 1000 cycles at 1 C.

El Halya, Nabil↗

DOE OSTI2023

A convection-permitting dynamically downscaled dataset over the Midwestern United States

Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.

54 ENVIRONMENTAL SCIENCES↗