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DOE OSTI2023

Pulsed Light Synthesis of High Entropy Nanocatalysts with Enhanced Catalytic Activity and Prolonged Stability for Oxygen Evolution Reaction

The ability to synthesize compositionally complex nanostructures rapidly is a key to high-throughput functional materials discovery. In addition to being time-consuming, a majority of conventional materials synthesis processes closely follow thermodynamics equilibria, which limit the discovery of new classes of metastable phases such as high entropy oxides (HEO). Herein, a photonic flash synthesis of HEO nanoparticles at timescales of milliseconds is demonstrated. By leveraging the abrupt heating and cooling cycles induced by a high-power-density xenon pulsed light, mixed transition metal salt precursors undergo rapid chemical transformations. Hence, nanoparticles form within milliseconds with a strong affinity to bind to the carbon substrate. Oxygen evolution reaction (OER) activity measurements of the synthesized nanoparticles demonstrate two orders of magnitude prolonged stability at high current densities, without noticeable decay in performance, compared to commercial IrO 2 catalyst. This superior catalytic activity originates from the synergistic effect of different alloying elements mixed at a high entropic state. It is found that Cr addition influences surface activity the most by promoting higher oxidation states, favoring optimal interaction with OER intermediates. The proposed high-throughput method opens new pathways toward developing next-generation functional materials for various electronics, sensing, and environmental applications, in addition to renewable energy conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Rotation of Crystal Seed During Early Stages of Growth Reveals the Anisotropy of Glass Matrix

Glass-ceramics are a highly valuable class of materials that are needed for realizing many high-tech as well as consumer products. They are made by controlling crystallization of glass. Theoretical atomistic insights obtained using molecular dynamics simulations in this work reveal that crystal seeds can spontaneously rotate within glass at high temperatures due to anisotropic forces at the glass-crystal interface. This rotation challenges the long‐held view of glass as isotropic solid, and highlights how atomic‐scale inhomogeneities influence crystal growth and microstructure, hence the physical properties glass‐ceramics.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Pristine or Highly Defective? Understanding the Role of Graphene Structure for Stable Lithium Metal Plating

The role of graphene host structure/chemistry in plating–stripping in lithium metal anodes employed for lithium metal batteries is first examined in this study. Structural and chemical defects are bad, since highly defective graphene promotes unstable solid electrolyte interphase (SEI) growth. This consumes the fluoroethylene carbonate (FEC) additive in the carbonate electrolyte and is correlated with rapid decay in Coulombic efficiency (CE) and formation of filament-like Li dendrites. A unique flow-aided sonication exfoliation method is employed to synthesize “defect-free” graphene (df-G), allowing for a direct performance comparison with conventional reduced graphene oxide (r-GO). At cycle 1, the r-GO is better electrochemically wetted by Li than df-G, indicating that initially it is more lithiophilic. With cycling, the nucleation overpotential with r-GO becomes higher than with df-G, indicating less facile plating reactions. The df-G yields state-of-the-art electrochemical performance, with the post cycled metal surface being relatively smooth and dendrite-free. Conversely, r-GO templates have CE rapidly degrade from the onset, with extensive dendrites after cycling. Severe SEI growth and associated FEC depletion with r-GO are further confirmed by electrochemical impedance analysis and surface science methods. Finally, a new design rule is provided for Li metal templates: An ideal host must be noncatalytic toward SEI formation.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Commercialization of Lithium Battery Technologies for Electric Vehicles

We present that the currently commercialized lithium-ion batteries have allowed for the creation of practical electric vehicles, simultaneously satisfying many stringent milestones in energy density, lifetime, safety, power, and cost requirements of the electric vehicle economy. The next wave of consumer electric vehicles is just around the corner. Although widely adopted in the vehicle market, lithium-ion batteries still require further development to sustain their dominating roles among competitors. In this review, the authors survey the state-of-the-art active electrode materials and cell chemistries for automotive batteries. The performance, production, and cost are included. The advances and challenges in the lithium-ion battery economy from the material design to the cell and the battery packs fitting the rapid developing automotive market are discussed in detail. Additionally, new technologies of promising battery chemistries are comprehensively evaluated for their potential to satisfy the targets of future electric vehicles.

25 ENERGY STORAGE↗

DOE OSTI2020

Fundamental Insights from a Single-Crystal Sodium Iridate Battery

Electrochemically driven chemical transformations play the key role in controlling storage of energy in chemical bonds and subsequent conversion to power electric vehicles and consumer electronics. The promise of coupling anionic oxygen redox with cationic redox to achieve a substantial increase in capacities has inspired research in a wide range of electrode materials. A key challenge is that these studies have focused on polycrystalline materials, where it is hard to perform precise structural determinations, especially related to the location of light atoms. Here a different approach is utilized and a highly ordered single crystal, Na 2-x IrO 3 is harnessed, to explore the role of defects and structural transformations in layered transition metal oxide materials on redox-activity, capacity, reversibility, and stability. Within a combined experimental and theoretical framework, it is demonstrated that 1) it is possible to cycle Na 2-x IrO 3 , offering proof of principle for single-crystal based batteries 2) structural phase transitions coincide with Ir 4+ /Ir 5+ redox couple with no evident contribution from anionic redox 3) strong irreversibility and capacity fade observed during cycling correlates with the Na + migration resulting in progressive growth of an electrochemically inert O 3 -type NaIrO 3 phase.

25 ENERGY STORAGE↗

DOE OSTI2020

Device Performance of Emerging Photovoltaic Materials (Version 1)

Abstract Emerging photovoltaics (PVs) focus on a variety of applications complementing large scale electricity generation. Organic, dye‐sensitized, and some perovskite solar cells are considered in building integration, greenhouses, wearable, and indoor applications, thereby motivating research on flexible, transparent, semitransparent, and multi‐junction PVs. Nevertheless, it can be very time consuming to find or develop an up‐to‐date overview of the state‐of‐the‐art performance for these systems and applications. Two important resources for recording research cells efficiencies are the National Renewable Energy Laboratory chart and the efficiency tables compiled biannually by Martin Green and colleagues. Both publications provide an effective coverage over the established technologies, bridging research and industry. An alternative approach is proposed here summarizing the best reports in the diverse research subjects for emerging PVs. Best performance parameters are provided as a function of the photovoltaic bandgap energy for each technology and application, and are put into perspective using, e.g., the Shockley–Queisser limit. In all cases, the reported data correspond to published and/or properly described certified results, with enough details provided for prospective data reproduction. Additionally, the stability test energy yield is included as an analysis parameter among state‐of‐the‐art emerging PVs.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A Study of Model-Based Protective Fast-Charging and Associated Degradation in Commercial Smartphone Cells: Insights on Cathode Degradation as a Result of Lithium Depositions on the Anode

The ever expanding mobile consumer electronic market has accelerated the need for safe and efficient fast-charging approaches that improve the overall speed of battery charging without hastened deterioration of the battery performance. Herein, the impact of a resource inexpensive, physics-based, electrochemically optimized fast-charging algorithm (charging time < 2 h) for mobile devices is investigated. A critical difference in the amount and morphology of lithium deposits on the anode for cells fast-charged without an optimized algorithm is observed and found to be the main cause of capacity decay. Furthermore, an in-depth study of the LiCoO 2 cathode regions opposite to pronounced lithium deposits on the anode reveals a “mirroring” phenomenon, i.e., a frozen monoclinic phase, and inactivity to relithiation. In operando hard X-ray absorption spectroscopy reveals that degraded spots on harvested cathodes seem to be activated again and participate in the intercalation process when lithiated at low rates from lithium foil counter electrodes. On the other hand, tests at higher C-rates, closer to the actual fast-charging rate, reveal only negligible oxidation state changes and therefore poor performance.

25 ENERGY STORAGE↗

DOE OSTI2020

An Arduino based automatic pressure evaluation system to quantify growth of non‐model anaerobes in culture

Abstract Measuring the growth rate of non‐model anaerobic microbes typically requires the use of time‐consuming and often destructive manual measurements. Here, an Arduino based automatic pressure evaluation system (A‐APES) was developed to automatically measure the rate of fermentation gas production as a proxy for microbial growth in anaerobic systems. The A‐APES system measures accumulated gas pressure in sealed cultures accurately at high‐resolution, while venting the system at programmed intervals to prevent over pressurization. The utility of A‐APES is demonstrated in this study by quantifying the growth rate and phases of a biomass‐degrading anaerobic gut fungus, which cannot be otherwise measured via conventional techniques due to its association with particulate substrates. Given the utility of the A‐APES approach, we provide a complete construction guide to fabricate the device, which is three times less expensive compared to existing commercial alternatives.

Wilken, St. Elmo↗

DOE OSTI2020

Coupling reduced‐order modeling and coarse‐grained CFD‐DEM to accelerate coal gasifier simulation and optimization

Abstract High fidelity three‐dimensional (3‐D) numerical simulations of commercial‐scale coal gasifiers are very time consuming and expensive. This article proposes a reduced‐order modeling approach that uses quasi one‐dimensional (1‐D) CFD–DEM simulation results to serve as an accurate initial condition for the 3‐D simulation, which significantly shortens the physical time from several hours to minutes to achieve steady states. The 3‐D simulation employs a previously validated coarse‐grained CFD‐DEM approach, which further accelerates the simulation. Both 1‐D and 3‐D simulations adopt a particle shrinkage scheme to correctly account for the particle volume change due to chemical reaction and thus provides an accurate bed height. Comparison with available experimental measurements of the bed temperature and synthesis gas composition is used to validate the simulation. The final syngas composition and flowrates are strongly affected by the fuel or gasification agent rate and process operating conditions as expected. This approach is then used to optimize two important operating conditions (air and steam flows) to achieve specific gasifier performance goals for the production of Fischer–Tropsch synthesis gas and production of fuel gas for gas turbines.

Yu, Jia↗

DOE OSTI2021

Effects of surface species and homogeneous reactions on rates and selectivity in ethane oxidation on oxide catalysts

Selective alkane oxidations on metal oxide catalysts involve complex mechanisms with multiple reactions in series and parallel, different types of reduced and oxidized surface species, and potential contributions from gas-phase reactions. We report kinetics and thermodynamics of elementary steps involved in C 2 H 6 -O 2 reactions on SiO 2 -supported small vanadium oxide domains are determined using density functional theory. These surface reactions together with gas-phase mechanisms are incorporated in kinetic simulations to determine how surface and gaseous reactions interact and contribute to rates and selectivity. The results show that gas-phase reactions within pore volumes in contact with the catalyst contribute significantly to C 2 H 6 activation rates, even at conditions where gas-phase reactions in empty volumes without catalyst are negligible. The majority of C 2 H 6 activations occur on the surface, via H abstraction by vanadium oxo species present at terminal lattice oxygens. The gas-phase activations via H-abstraction by OH radicals also exhibit significant contributions. The reduced centers formed by reactions at vanadium oxo species are re-oxidized rapidly and, therefore, are present in very small concentrations at reaction conditions. The re-oxidation steps lead to the formation of HO 2 radicals and surface peroxo species that are also rapidly consumed and are present in small concentrations. The peroxo species preferentially convert C 2 H 4 to its epoxide product and influence selectivity even at low concentrations. The gas-phase reactions decrease the concentrations of peroxo species and improve selectivity slightly. The effects of reaction conditions and catalyst site density provide further insights into how factors beyond conversions at lattice oxygens influence rates and selectivity in alkane oxidation reactions of significant industrial importance.

42 ENGINEERING↗

DOE OSTI2024

Nonlinear model predictive control for mode‐switching operation of reversible solid oxide cell systems

Abstract Solid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation.

08 HYDROGEN↗

DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Kim, Yoon G.↗

DOE OSTI2022

Experimental validation of multiphysics model simulations of the thermal response of a cement clinker rotary kiln at laboratory scale

Abstract An increasing demand for buildings, transportation systems and civil infrastructure development has driven expansion of cement consumption world‐wide, producing a significant increase in related global energy demand. With approximately 7% of the world‐wide industrial energy consumption (10.7 exajoules [EJ]), the cement industry is the third most energy intensive industrial processes and a key component for concrete, the most consumed composite material in the global construction industry. In cement manufacturing, the cement kiln accounts for most of the energy consumption in the production process. As the heart of a cement plant, the cement kiln is where the kiln feed primarily containing calcium oxide (CaO), silica (SiO 2 ), alumina (Al 2 O 3 ), and iron (Fe 2 O 3 ) are thermally and chemically transformed into clinker minerals. The presented work developed a multiphysics model, designed and built a laboratory‐scale rotary cement clinker kiln, and produced cement clinker at laboratory‐scale. The model was developed to study the interaction between the various thermal, fluid dynamic and chemical interactions involved in the sintering process used to form Portland cement clinker in an effort to reduce energy use. The analytical model was validated through experimental testing using a unique laboratory‐scale rotary cement kiln developed during the investigation. Also demonstrated was the feasibility of producing clinker at laboratory scale. This modeling and lab scale tests were designed to better understand the clinker sintering process so that operational and quality decisions can be made to optimize energy consumption without compromising cement clinker quality. The computational fluid dynamics modeling was developed in COMSOL Multiphysics 6.0. The characteristics of the combustion fluid flow, concentration of species, temperature and heat transfer were studied for a turbulent flow of methane (CH 4 ) gas and oxygen (O 2 ). Theory suggests that heat transfer impacts the cement production process but the multiphysics model more accurately describes the convection, conduction, and radiant heat transfer in the kilning process and thus allows for a better understanding of the energy exchange driving the chemical reactions that produce Portland cement. Clinker minerals were formed because of appropriate burning conditions implemented during experimental model validation.

Tabares, Juan David↗

DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗

DOE OSTI2015

Superior Na‐Storage Performance of Low‐Temperature‐Synthesized Na 3 (VO 1− x PO 4 ) 2 F 1+2 x (0≤ x ≤1) Nanoparticles for Na‐Ion Batteries

Abstract Na‐ion batteries are becoming comparable to Li‐ion batteries because of their similar chemical characteristics and abundant sources of sodium. However, the materials production should be cost‐effective in order to meet the demand for large‐scale application. Here, a series of nanosized high‐performance cathode materials, Na 3 (VO 1− x PO 4 ) 2 F 1+2 x (0≤ x ≤1), has been synthesized by a solvothermal low‐temperature (60–120 °C) strategy without the use of organic ligands or surfactants. The as‐synthesized Na 3 (VOPO 4 ) 2 F nanoparticles show the best Na‐storage performance reported so far in terms of both high rate capability (up to 10 C rate) and long cycle stability over 1200 cycles. To the best of our knowledge, the current developed synthetic strategy for Na 3 (VO 1− x PO 4 ) 2 F 1+2 x is by far one of the least expensive and energy‐consuming methods, much superior to the conventional high‐temperature solid‐state method.

Qi, Yuruo↗

DOE OSTI2020

Overcoming Fundamental Limitations in Adsorbent Design: Alkene Adsorption by Non‐porous Copper(I) Complexes

Abstract Purifying alkenes from alkanes requires cryogenic distillation. This consumes energy equivalent to countries of ca. 5 million people. Replacing distillation with adsorption processes would significantly increase energy efficiency. Trade‐offs between kinetics, selectivity, capacity, and heat of adsorption have prevented production of an optimal adsorbent. We report adsorbents that overcome these trade‐offs. [Cu‐Br] 3 and [Cu‐H] 3 are air‐stable trinuclear complexes that undergo reversible solid‐state inter‐molecular rearrangements to produce dinuclear [Cu‐Br⋅(alkene)] 2 and [Cu‐H⋅(alkene)] 2 . The reversible solid‐state rearrangement, confirmed in situ using powder X‐ray diffraction, allows adsorbent design trade‐offs to be overcome, coupling low heat of adsorption (−10 to −17 kJ mol −1 alkene ), high alkene:alkane selectivity (47; 29), and uptake capacity (>2.5 mol alkene mol −1 Cu3 ). Most remarkably, [Cu‐H] 3 displays fast uptake and regenerates capacity within 10 minutes.

Parasar, Devaborniny↗

DOE OSTI2021

Molecular Brush Surfactants: Versatile Emulsifiers for Stabilizing and Structuring Liquids

Abstract Using amphiphilic molecular brushes to stabilize emulsions usually requires the synthesis of specific side chains, which can be a time‐consuming and difficult challenge to meet. By taking advantage of the electrostatic interactions between water‐soluble molecular brushes and oil‐soluble oligomeric ligands, the in situ formation, assembly and jamming of molecular brush surfactants (MBSs) at the oil‐water interface is described. With MBSs, stable emulsions including o/w, w/o and o/w/o can be easily prepared by varying the molar ratios of the molecular brushes to the ligands. Moreover, when jammed, the binding energy of MBSs at the interface is sufficiently strong to allow the stabilization of liquids in nonequilibrium shapes, i.e., structuring liquids, producing an elastic film at the interface with exceptional mechanical properties. These structured liquids have numerous potential applications, including chemical biphasic reactions, liquid electronics, and all‐liquid biomimetic system.

Wang, Beibei↗