Advanced Research Projects Agency-Energy Research Articles

(Invited) In-Situ MOCVD Etching of GaN Using XeF2 for Selective-Area-Epitaxial-Regrowth of p-Type GaN for High Voltage PN Diodes

PN diodes with multi-kilovolt breakdown voltages have been demonstrated to great than 6 kV in GaN, validating the device potential predicted by the intrinsic material properties of III-nitride semiconductors. However, power control circuits also require switching transistors such as junction field effect transistors (JFETs) and practical diodes such as merged PIN Schottky (MPS) diodes. Such devices usually employ selective areas of p-type semiconductor surrounded by n-type material and require the formation of PN junctions with low reverse leakage current. Selective-area p-type doping in Si and SiC based power devices is typically achieved using implantation and thermal annealing to form the p-type areas. Ion implantation of Mg in GaN has demonstrated p-type conductivity, but this process requires specialized equipment for the high-pressure and high-temperature activation of Mg dopants. We have investigated selective-area-regrowth (SArG) to epitaxially grow p-type GaN as an alternative to dopant implantation. Successful PN diode formation by SArG requires a process to remove residual crystalline damage resulting from the ex-situ inductively coupled plasma (ICP) etch typically used to form the p-well, as well as a method to remove the elevated level of Si found at the regrowth interface of a surface that had been exposed to air. For the case of SArG of p-GaN on air-exposed, blanket ICP etched n-type GaN, we present the novel use of a fluorine-based precursor (XeF2) for in-situ etching of GaN in the MOCVD chamber. Unlike chlorine (TBCl and CCl4) and bromine (CBr4) -based precursors we have studied; we obtained a smooth surface following in-situ fluorine etching of air-exposed GaN. Schottky barrier diodes (SBDs) formed by shadow mask evaporation on in-situ fluorine etched n-GaN that had been previously ICP etched showed reverse leakage currents to -40 V that are equal to those of SBDs formed on as-grown n-GaN layers. The in-situ fluorine/ICP etched Schottky diodes had reverse leakage currents more than 3 orders of magnitude lower than those formed on n-GaN layers that had only experienced ICP etching. This suggests that the in-situ fluorine etch was effective at removing the residual damage from the ICP etch process. Furthermore, we discuss the use of in-situ fluorine etching to reduce the concentration of Si at the regrowth interface required to form PN junctions with low reverse leakage current. This work was supported in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy under the PNDIODES program directed by Dr. Isik Kizilyalli, and in part by the US Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Electric Drive Train Consortium. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. The views expressed in the presentation do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

Accelerating Electrocatalyst Innovation: High-Throughput Automated Microkinetic Modeling

To reduce energy-related emissions, we must create chemicals from carbon dioxide with renewable energy, instead of from petroleum. Designing such a process needs computer models that can describe all the chemical reactions that are happening, including those driven by electrochemistry. There may be thousands of such reactions, so we must build a tool to find them automatically. Our reaction mechanism generator for electrocatalysis will create detailed kinetic models for many electrochemical processes, but for this initial project we are targeting the reduction of carbon dioxide to produce propanol, a useful hydrocarbon product. The kinetic models will be used to simulate an electrochemical reactor, then analyzed both technically and economically to determine which catalyst materials and reactor designs are best. This will allow new catalyst materials to be discovered using powerful computers, instead of using very time-consuming and expensive experiments.Building on the state-of-the-art open-source Reaction Mechanism Generator (RMG) software [1,2], we are creating the first automated mechanism generator for electrochemical reactions on a catalyst. Supplied with a feedstock, catalyst, and conditions, it will propose a detailed kinetic model comprising hundreds of intermediate species and reactions. Reactor simulations will predict product yields as a function of feed, concentrations, and potential, allowing conditions to be optimized and the trade-off between conversion and selectivity to be investigated. In turn this could inform a detailed technoeconomic analysis, enabling catalyst materials to be screened in silico on an economic basis. Meanwhile sensitivity analysis of the simulations will select estimated parameters for refinement with DFT calculations.We must first extend RMG to allow charged species, and implement proton coupled electron transfer (PCET) reactions. We will at first use the computational hydrogen electrode model to determine the Gibbs energy of a species (and hence reaction) as a function of chemical potential. Potential-dependent kinetics will be estimated using a charge transfer coefficient model. In preliminary work with a framework like this we have constructed a reaction mechanism for carbon dioxide reduction on Cu(111) with 37 species and 292 reactions (23 of them electrochemical) [3]. The model discovered many key electrochemical pathways to reduce CO2 to experimentally observed products such as methane, methanol, formic acid, ethylene, and ethanol. Acknowledgements The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001786. The authors thank Dr. David Farina Jr. for his significant contributions to the proof-of-concept.

(Invited) Selective Area Growth, Etching, and Doping of GaN By MOCVD for Power Electronics

Gallium Nitride (GaN) possesses a wide band gap and excellent transport properties, making it a highly promising material for high-power and high-frequency applications. Microwave power amplification for commercial and military purposes has already benefited from GaN-based high electron mobility transistors (HEMTs). However, currently available GaN optoelectronic and electronic devices use planar junctions and heterostructures prepared by epitaxy, typically via metalorganic chemical vapor deposition (MOCVD). To enable more sophisticated device configurations, in-plane lateral junctions are necessary. The most effective method for achieving this is through selective area etching (SAE) followed by selective area growth (SAG). In this talk, we will discuss the progress made at Yale in the selective-area etching, growth, and doping of GaN.Selective-area etching of GaN is the most challenging and critical step in this process. Instead of using conventional ICP etching, we have employed tertiarybutylchloride (TBCl) as an in-situ chemical etchant of GaN in MOCVD to create smooth trenches and remove plasma etching-generated damages on the surface. With electrical and material characterizations, we have confirmed that TBCl etching is a low-damage and clean etching process. We will also describe our recent efforts to bypass the constraint of dielectric masking during in-situ etching and perform maskless selective area etching (SAE) to create low-defect lateral p-n junctions for GaN power electronics. Additionally, we will discuss non-planar selective-area growth and doping, including the principles of growth evolution, non-uniform Mg distribution, and parasitic impurities.This work is supported by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. DOE, under DE-AR0000871 as part of the PNDIODES program managed by Dr. Isik Kizilyalli.

Comments: The New “Gold Rush” Hunts for Subsurface Hydrogen

_ A gold strike was announced in late October. But in this case the gold was naturally occurring hydrogen in a former coal basin. Known as “white” or “gold” hydrogen, the reported discovery of naturally formed underground hydrogen was made in northeastern France’s Lorraine coal basin, a region between France and Germany. The last coal mine was shut down 20 years ago. Researchers at the French National Centre of Scientific Research (CNRS) were testing a probe designed to analyze gases dissolved in the water of deep underground rock formations, looking for methane, when they detected hydrogen concentrations at depths of 1100 m (14%) and 1250 m (20%). Their calculations estimated the deposits’ potential as between 6 and 250 million metric tons of hydrogen. This wasn’t their first discovery of underground hydrogen in the area. Philippe de Donato and Jacques Pironon made a similar finding “by chance” as part of the Regalor research project in collaboration with Française de l’Energie (FDE), an independent multienergy company, the University of Lorraine, and CNRS. FDE announced the discovery in a press release in May, saying the measurements of hydrogen were made in its previously drilled Folschviller wellsite in the carboniferous aquifer of the Lorraine basin. Launched in 2018, the project’s aim was to confirm an assessment carried out in 2012 by France’s IFPEN petroleum and new energies institute. After analyzing a sample of the soil under the basin, the institute concluded that it contained 370 billion m3 of methane, which represents 8 years of gas consumption in France. De Donato and Pironon said, “The work carried out within the framework of the Regalor project has made it possible to demonstrate that the fluids within the carboniferous formations of the Lorraine mining basin are very significantly enriched in hydrogen, with a measured concentration of 15% at 1093 m depth and estimated at 98% at 3000 m depth.” FDE said in May that it applied for an exclusive mining exploration permit for the exploration of natural hydrogen in the basin. The permit covers an area of 2254 km² in the Grand Est region. The company said a site for a pilot will be identified based on the results obtained and then built to initiate local production and recovery of natural hydrogen in the Grand Est Region. In its October investor update, FDE said measurements will be performed in three of its existing wells by the end of the year to determine the extent of the hydrogen deposit. Reservoirs have also been discovered in the US, Canada, Finland, the Philippines, Australia, Brazil, Oman, Turkey, and Mali. In Mali, the Bourakebougou water well, Bougou-1, was drilled in 1987 in the Taoudeni Basin (https://doi.org/10.1016/j.ijhydene.2018.08.193), a large sedimentary system present mainly in Algeria, Mauritania, and Mali. The well was cemented after a gas explosion occurred during drilling operations at a measured depth of 112 m. Unplugged in 2011 for use as a pilot well for local hydrogen production, gas was reported comprising 98% hydrogen, 1% nitrogen, and 1% methane. Hydrogen was then produced as an energy resource to supply local electricity through a company named Petroma, renamed Hydroma. From 2017 to 2019, the company drilled 24 wells. Among the new breed of gold prospectors are several startup companies including Natural Hydrogen Energy, Koloma, Helios Aragon, Gold Hydrogen, HyTerra, and H2Au. Helios Aragon owns exploration permits in northern Spain’s Aragon region and will begin drilling the Monzon-2 appraisal well in 2024 at a cost of $12 million. Estimates for the well are 1.1 million tons of hydrogen, and the company claims the Monzon field holds 5 to 10 million tons within its permits and more than 100 million tons in the region. Natural Hydrogen Energy and HyTerra claim the “first wildcat well targeting natural hydrogen in Nebraska,” the Hoarty well at Project Geneva. HyTerra also holds leases in the Nemaha Ridge in Kansas. The cost advantages of subsurface hydrogen are frequently cited by the early prospectors. For example, the wells in Mali have the potential to generate hydrogen gas at a cost of 50 cents/kg (https://doi.org/10.1016/j.ijhydene.2018.08.193), which is only one-tenth the cost of producing hydrogen via electrolysis using solar, wind, geothermal, or other renewable energy sources. If commercialization and economies of scale pan out, this may become the gold standard for hydrogen energy. For Further Reading The Curious Case of Geologic Hydrogen: Assessing its Potential as a Near-Term Clean Energy Source (https://doi.org/10.1016/j.joule.2022.01.005) by E.M. Yedinak, US Department of Energy, Advanced Research Projects Agency-Energy. The Occurrence and Geoscience of Natural Hydrogen: A Comprehensive Review (https://doi.org/10.1016/j.earscirev.2020.103140) by V. Zgonnik, Natural Hydrogen Energy LLC.

Diversity Subsampling: Custom Subsamples from Large Data Sets

Subsampling from a large unlabeled (i.e., no response values are available yet) data set is useful in many supervised learning contexts to provide a global view of the data based on only a fraction of the observations. In this paper, we borrow concepts from the well-known sampling/importance resampling technique, which samples from a specified probability distribution, to develop a diversity subsampling approach that selects a subsample from the original data with no prior knowledge of its underlying probability distribution. The goal is to produce a subsample that is independently and uniformly distributed over the support of distribution from which the data are drawn, to the maximum extent possible. We give an asymptotic performance guarantee of the proposed method and provide experimental results to show that the proposed method performs well for typical finite-size data. We also compare the proposed method with competing diversity subsampling algorithms and demonstrate numerically that subsamples selected by the proposed method are closer to a uniform sample than subsamples selected by other methods. The proposed diversity subsampling (DS) algorithm is more efficient than known methods. It takes only a few minutes to select tens of thousands of subsample points from a data set of size one million. Our DS algorithm easily generalizes to select subsamples following distributions other than uniform. We provide a Python package (FADS) that implements the proposed method. History: Kwok-Leung Tsui served as the senior editor for this article. Funding: This work was supported by the National Science Foundation [Grant CMMI-1436574], Northwestern University, the Advanced Research Projects Agency-Energy, and the U.S. Department of Energy [Award DE-AR0001209]. Data Ethics & Reproducibility Note: No data ethics considerations are foreseen related to this article. The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.8309237.v3 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.00017 ).

Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition

Intro to the ARPA-E Grid Optimization Competition In “Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition,” we review the state of the art in practical algorithms for scheduling power-systems operations in the short term and the results of the recent competition organized by the U.S. Advanced Research Projects Agency–Energy. We explain the mixed-integer nonlinear formulation used in the competition for nonspecialists in electrical engineering, the context and organization of the competition, and the performance of competitors. We find that the collective approaches and results of competitors provide support for efforts to move nonlinear optimization techniques into industrial applications, as they have proven to be a robust and efficient alternative to current linear approximation techniques.

Optimally Scheduling Public Safety Power Shutoffs

In an effort to reduce power system-caused wildfires, utilities carry out public safety power shutoffs (PSPSs), in which portions of the grid are deenergized to mitigate the risk of ignition. The decision to call a PSPS must balance reducing ignition risks and the negative impact of service interruptions. In this work, we consider three PSPS scheduling scenarios, which we model as dynamic programs. In the first two scenarios, we assume that N PSPSs are budgeted as part of the investment strategy. In the first scenario, a penalty is incurred for each PSPS declared past the Nth event. In the second, we assume that some costs can be recovered if the number of PSPSs is below N while still being subject to a penalty if above N. In the third, the system operator wants to minimize the number of PSPSs such that the total expected cost is below a threshold. We provide optimal or asymptotically optimal policies for each case, the first two of which have closed-form expressions. Lastly, we establish the applicability of the first PSPS model’s policy to critical peak pricing and obtain an optimal scheduling policy to reduce the peak demand based on weather observations. Funding: This work was funded in part by the Natural Sciences and Engineering Research Council of Canada, the Institute for Data Valorization, the National Science Foundation [Award 1351900], the Advanced Research Projects Agency-Energy [Award DE-AR0001061], and the University of California Office of the President Laboratory Fees Program [Grant LFR-20-652467].

Optimally Scheduling Public Safety Power Shutoffs

In an effort to reduce power system-caused wildfires, utilities carry out public safety power shutoffs (PSPSs), in which portions of the grid are deenergized to mitigate the risk of ignition. The decision to call a PSPS must balance reducing ignition risks and the negative impact of service interruptions. In this work, we consider three PSPS scheduling scenarios, which we model as dynamic programs. In the first two scenarios, we assume that N PSPSs are budgeted as part of the investment strategy. In the first scenario, a penalty is incurred for each PSPS declared past the Nth event. In the second, we assume that some costs can be recovered if the number of PSPSs is below N while still being subject to a penalty if above N. In the third, the system operator wants to minimize the number of PSPSs such that the total expected cost is below a threshold. We provide optimal or asymptotically optimal policies for each case, the first two of which have closed-form expressions. Lastly, we establish the applicability of the first PSPS model’s policy to critical peak pricing and obtain an optimal scheduling policy to reduce the peak demand based on weather observations. Funding: This work was funded in part by the Natural Sciences and Engineering Research Council of Canada, the Institute for Data Valorization, the National Science Foundation [Award 1351900], the Advanced Research Projects Agency-Energy [Award DE-AR0001061], and the University of California Office of the President Laboratory Fees Program [Grant LFR-20-652467].

Government cash for next-generation batteries

The Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E), which funds early-stage energy technologies, has awarded $42 million to start-ups and research labs developing next-generation batteries for cars. Among them, 24M Technologies received $3.2 million to develop sodium metal batteries, and Project K got $2.6 million for potassium-ion batteries. Both companies aim to improve charging speed and cold-weather performance. Ampcera received $2.1 million for research on solid-state batteries; Solid Power got $5.6 million to develop lithium-metal batteries.

Machine Learning Control for Floating Offshore Wind Turbine Individual Blade Pitch Control

Offshore wind energy is a promising option for emission-free power generationbecause its potential can satisfy the entire US energy demand. However, deep offshore wind energy is mostly left untapped due to the high levelized cost of energy (LCOE) of floating offshore wind turbines (FOWTs). To address the challenge, individual blade pitch control (IPC) of each blade is necessary to reduce fatigue due to the nonlinear dynamics involving unbalanced nonstationary wind/wave loading. In this paper, a machine learning control (MLC) method is proposed that utilizes a genetic program to selectively evolve promising control law candidates from simulated FOWT sensor data. The proposed method utilizes unique MLC features including an efficient selection of design load cases (DLCs) to accelerate the evolution, DLC-based elitism to identify promising candidates, and the use of internal state feedback to learn effective controller properties across all the target DLCs. These features are used to reduce evaluation time on the complex nonlinear FOWT model. The feasibility of the method is demonstrated by reducing fatigue and ultimate loading by 41% under the representative DLCs provided by the Aerodynamic Turbines with Load Attenuation Systems (ATLAS) competition hosted by the Advanced Research Projects Agency–Energy (ARPA-E). Unlike the methods founded upon black-box models [e.g., artificial neural networks (ANNs)], the proposed method provides interpretable results; thus, it can contribute to learning important design features to guide the future controller design. For example, the best individual driven during the feasibility study exhibits a nonlinear proportional controller to a sigmoidlike platform pitch signal, which can be a starting point for more advanced IPC design for FOWTs. The proposed method will improve the cost-effectiveness of FOWTs and be applied to similar nonlinear control problems, such as unmanned aerial vehicle (UAV) control.

Portable diagnostic package for Thomson scattering and optical emission spectroscopy on Princeton field-reversed configuration 2 (PFRC 2).

An Advanced Research Projects Agency-Energy funded diagnostic system has been deployed to the Princeton field-reversed configuration 2 (PFRC-2) device, located at Princeton Plasma Physics Laboratory. The Portable Diagnostic Package (PDP), designed at Oak Ridge National Laboratory, allows for the measurement of Thomson Scattering (TS) for electron density and temperature and Optical Emission Spectroscopy (OES) for ion temperature, impurity density, and ion velocity. A tunable spectrometer on the PDP with three gratings provides the flexibility to measure low (1eV) and high (1000eV) electron temperature ranges from TS. Additionally, using a second spectrometer, the OES diagnostic can survey light emission from various ion excitation levels for wide wavelength ranges. The electron density (<2 × 1019m-3) of plasmas generated in PFRC-2 has been below the PDP TS discrimination threshold, which has made TS signal detection challenging against a high-background of laser stray light. The laser stray light was iteratively reduced by making modifications to the entrance and exit geometry on PFRC-2. Rayleigh scattering experiments on PFRC have yielded the TS discrimination sensitivity to be >1 × 1020m-3 for the PDP. A recently implemented narrow-band notch spectral filter that masks the second harmonic 532nm Nd:YAG laser wavelength has increased the system's TS light discrimination sensitivity 65 times compared to the instance when the notch filter was not implemented. The hardware implementation including design changes to the flight tubes and Brewster windows will be discussed, along with results from Rayleigh and rotational Raman scattering sensitivity analyses, which were used to establish a quantitative figure of merit on the system performance. The Raman scattering calibration with the notch filter has improved the PDP electron density threshold to 1 ± 0.5 × 1018m-3.

Machine Learning and High Throughput Synthesis Acceleration of the Discovery of Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts

Accelerating the development and discovery of new catalysts is vital for advancing many electrochemical energy conversion technologies (EECT) required to achieve a sustainable future utilizing carbon-free fuel, a circular economy, and to meet the grand energy challenges of the 21st century. The oxygen evolution reaction (OER) is at the heart of many EECT such as water and carbon dioxide electrolyzers, fuel cells, and metal-oxygen batteries. The sluggish kinetics of oxygen electrocatalysis, resulting high overpotential necessary to attain practical current densities, and the high cost of the state-of-the-art OER platinum group metal (PGM) and precious metals catalysts (i.e., IrO2 and RuO2) limit the cost-effective implementation and development of several promising electrolysis technologies.1-3 The development of alternative PGM-free OER catalysts, with comparable or superior activity and durability to the PGM catalysts and derived from earth-abundant materials has thus been an active research area for decades.The application of perovskite oxides as PGM-free electrocatalysts for the OER in alkaline environments has seen significant research interest in the last decade, with tri-metallic and tetra-metallic compounds showing activities comparable to PGM-based catalysts.4,5 The chemical space of these compounds is exceptionally large, yet the development of new perovskite oxides with high OER performance (activity and durability) has been limited and often discovered through trial and error, a time and cost inefficient route that restricted the discovery of more advanced materials. Recent advances in high-performance computing, machine learning (ML), and high throughput material synthesis and screening technologies have enabled high-throughput catalyst design and discovery.4-10 This presentation will describe how the machine learning and high throughput synthesis technologies worked synergistically to accelerate the discovery of alkaline oxygen evolution reaction electrocatalysts. The role of ML in accelerating the materials synthesis and the role of high throughput synthesis in optimizing the ML model predictions will be discussed.AcknowledgmentsThis work was supported by the U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) under the DIFFERENTIATE program. This work was authored in part by Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. DE-AC02-06CH11357.References Katsounaros, Ioannis, Serhiy Cherevko, Aleksandar R. Zeradjanin, and Karl JJ Mayrhofer. "Oxygen electrochemistry as a cornerstone for sustainable energy conversion." Angewandte Chemie International Edition53, no. 1 (2014): 102-121.Lee, Youngmin, Jin Suntivich, Kevin J. May, Erin E. Perry, and Yang Shao-Horn. "Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions." The journal of physical chemistry letters3, no. 3 (2012): 399-404.Cherevko, S. et al. Oxygen and hydrogen evolution reactions on Ru, RuO2, Ir, and IrO2 thin film electrodes in acidic and alkaline electrolytes: A comparative study on activity and stability. Today 262, 170–180 (2016).Nahar, Lamia, Ahmed A. Farghaly, Richard J. Alan Esteves, and Indika U. Arachchige. "Shape controlled synthesis of Au/Ag/Pd nanoalloys and their oxidation-induced self-assembly into electrocatalytically active aerogel monoliths." Chemistry of Materials29, no. 18 (2017): 7704-7715.Farghaly, Ahmed A., Rezaul K. Khan, and Maryanne M. Collinson. "Biofouling-resistant platinum bimetallic alloys." ACS applied materials & interfaces10, no. 25 (2018): 21103-21112.Khan, Rezaul K., Ahmed A. Farghaly, Tiago A. Silva, Dexian Ye, and Maryanne M. Collinson. "Gold-Nanoparticle-Decorated Titanium Nitride Electrodes Prepared by Glancing-Angle Deposition for Sensing Applications." ACS Applied Nano Materials2, no. 3 (2019): 1562-1569.Farghaly, Ahmed A., Mai Lam, Christopher J. Freeman, Badharinadh Uppalapati, and Maryanne M. Collinson. "Potentiometric measurements in biofouling solutions: comparison of nanoporous gold to planar gold." Journal of The Electrochemical Society163, no. 4 (2015): H3083.Suntivich, Jin, Kevin J. May, Hubert A. Gasteiger, John B. Goodenough, and Yang Shao-Horn. "A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles." Science334, no. 6061 (2011): 1383-1385.Hwang, Jonathan, Zhenxing Feng, Nenian Charles, Xiao Renshaw Wang, Dongkyu Lee, Kelsey A. Stoerzinger, Sokseiha Muy et al. "Tuning perovskite oxides by strain: electronic structure, properties, and functions in (electro) catalysis and ferroelectricity." Materials Today31 (2019): 100-118.Gómez-Bombarelli, Rafael, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. "Automatic chemical design using a data-driven continuous representation of molecules." ACS central science4, no. 2 (2018): 268-276.

Platinum Group Metal-Free Oxygen Evolution Electrocatalysts for Alkaline Water Electrolysis

The sluggish kinetics of oxygen electrocatalysis and the resulting high overpotentials necessary to achieve useful current densities limit the development of promising technologies, such as fuel cells, water, and carbon dioxide electrolyzers, and metal-oxygen batteries.1 The best catalysts for both the oxygen reduction and oxygen evolution reactions (ORR and OER, respectively) are based on precious, platinum group metals (PGMs), such as platinum and iridium, leading to limitations in the cost-effective implementation of these technologies.2,3 The development of alternative catalysts, with comparable or higher activity and durability to the PGM catalysts and derived from earth-abundant materials has thus been an active research area for decades.Incredible progress has been made in developing PGM-free electrocatalysts for the OER in alkaline environments, with perovskite oxides showing activities comparable to PGM-based catalysts.4,5 Perovskite oxides are a very broad class of materials with the general formula of ABO3, where the B site is occupied by smaller transition metal ions and the A site by larger cations which have 12-fold coordination with O.4 Both the A sites and B sites can be occupied by multiple metal ions, leading to an even more expansive design space for this class of materials. Another interesting class of catalysts is Fe and Ni oxides derived from the electrochemical oxidation of metal-organic frameworks (MOFs), with the advantages of this material over the perovskites being high electronic conductivity and high surface area.This presentation will describe the development and application of a high-throughput methodology to accelerate the exploration of the effects of composition and synthesis parameters on the activity of perovskite oxide and metal-organic framework-derived alkaline electrolyte OER catalysts. The evolution of the oxidation state and atomic structure of the MOF materials in the electrochemical environment, as determined using in situ X-ray absorption spectroscopy (XAS), as well as the evolution of the morphology of the catalyst, as determined using electron microscopy, will be described.References Yang, X. Han, A.I. Douka, L. Huang, L. Gong, C. Xia, H.S. Park, and B.Y. Xia, Adv. Func. Mater., 31 (2021) 2007602.Pivovar, Nature Catalysis, 2 (2019) 562.Thompson and D. Papageorgopoulos, Nature Catalysis, 2 (2019) 558.Hwang, R.R. Rao, L. Giordano, Y. Katayama, Y. Yu, and Y. Shao-Horn, Science 358 (2017) 751.Suntivich, K.J. May, H.A. Gasteiger, J.B. Goodenough, Y. Shao-Horn, Science, 334 (2011) 1383. AcknowledgementsThis work was supported by the U.S. Department of Energy (DOE), Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office (HFTO) under the auspices of the Electrocatalysis Consortium (ElectroCat 2.0). This work was also supported by DOE, Advanced Research Projects Agency-Energy (ARPA-E) under the DIFFERENTIATE program. This work utilized the resources of the Advanced Photon Source, a DOE Office of Science user facility operated by Argonne National Laboratory for DOE Office and was authored by Argonne, a DOE Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. DE-AC02-06CH11357.

Skinny kelp (Saccharina angustissima) provides valuable genetics for the biomass improvement of farmed sugar kelp (Saccharina latissima)

Saccharina latissima (sugar kelp) is one of the most widely cultivated brown marine macroalgae species in the North Atlantic and the eastern North Pacific Oceans. To meet the expanding demands of the sugar kelp mariculture industry, selecting and breeding sugar kelp that is best suited to offshore farm environments is becoming necessary. To that end, a multi-year, multi-institutional breeding program was established by the U.S. Department of Energy's (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program. Hybrid sporophytes were generated using 203 unique gametophyte cultures derived from wild-collected Saccharina spp. for two seasons of farm trials (2019–2020 and 2020–2021). The wild sporophytes were collected from 10 different locations within the Gulf of Maine (USA) region, including both sugar kelp (Saccharina latissima) and the skinny kelp species (Saccharina angustissima). We harvested 232 common farm plots during these two seasons with available data. We found that farmed kelp plots with skinny kelp as parents had an average increased yield over the mean (wet weight 2.48 ± 0.90 kg m−1 and dry weight 0.32 ± 0.10 kg m−1) in both growing seasons. We also found that blade length positively correlated with biomass in skinny kelp x sugar kelp crosses or pure sugar kelp crosses. The skinny x sugar progenies had significantly longer and narrower blades than the pure sugar kelp progenies in both seasons. Overall, these findings suggest that sugar x skinny kelp crosses provide improved yield compared to pure sugar kelp crosses.

Designing publicly funded organisations for accelerated low carbon innovation: A case study of the ETI, UK and ARPA-E, US

The role of publicly funded organisations in implementing accelerated low carbon innovation policy is receiving increasing attention. Due to different national contexts and priorities however, policy makers face high levels of complexity in understanding what organisational approaches might best suit their aims. This paper develops a set of ten principles for accelerated innovation organisation design, which seek to provide policy makers with a tool to better understand the interplay of different design features on innovation outputs. The principles are applied to a comparative case study of the Energy Technologies Institute (ETI), United Kingdom (UK), and the Advanced Research Projects Agency– Energy (ARPA-E), United States (US). Results demonstrate that the design of the ETI, which embedded incumbent industry actors and engaged a narrower set of staff competencies, caused it to prioritise un transformative innovation, contrary to its initial mission. Conversely, the operational approach of ARPA-E has created an entrepreneurial, flexible approach to pursuing transformative innovation. The organisation however lacks long term stability in a changing political environment. Conclusions explore the implications of these results for policy makers seeking to design organisations that are effective in accelerating low carbon innovation.

High Throughput Synthesis of Perovskite Oxides for Alkaline Oxygen Evolution Reaction Electrocatalysis: Nature of the a and B Sites

Electrochemical energy conversion technologies based on the oxygen evolution reaction (OER) are at the heart of many efforts to achieve a sustainable future, carbon-free fuel, and a circular economy. The sluggish kinetics of oxygen electrocatalysis, as well as the high overpotential required to attain practical current densities, limit the efficiency of several promising electrochemical technologies, including water and carbon dioxide electrolyzers, metal–oxygen batteries, and fuel cells. The most efficient OER catalysts are precious metals such as iridium- and ruthenium-based materials (i.e., IrO2 and RuO2). This fact represents a challenge against the cost-effective implementation of these electrolysis technologies.1-3 As a result, there is a necessity for the development of cost effective PGM-free OER catalysts, with equivalent or superior activity and durability to the PGM catalysts.This presentation will discuss the development and implementation of high-throughput synthesis methodology for PGM-free OER catalysts exploiting the rich chemistry of nanoporous materials4-7 and Argonne National Laboratory's High-Throughput Research Facility. The developed high-throughput methodology will expedite the assessment of the effect of composition and synthesis parameters on the activity of perovskite oxide alkaline electrolyte OER catalysts.AcknowledgementsThis work was supported by the U.S. Department of Energy, Advanced Research Projects Agency Energy (ARPA-E) under the DIFFERENTIATE program. This work was authored in part by Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. DE-AC02-06CH11357.References Katsounaros, Ioannis, Serhiy Cherevko, Aleksandar R. Zeradjanin, and Karl JJ Mayrhofer. "Oxygen electrochemistry as a cornerstone for sustainable energy conversion." Angewandte Chemie International Edition53, no. 1 (2014): 102-121.Lee, Youngmin, Jin Suntivich, Kevin J. May, Erin E. Perry, and Yang Shao-Horn. "Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions." The journal of physical chemistry letters3, no. 3 (2012): 399-404.Cherevko, S. et al. Oxygen and hydrogen evolution reactions on Ru, RuO2, Ir, and IrO2 thin film electrodes in acidic and alkaline electrolytes: A comparative study on activity and stability. Today 262, 170–180 (2016).Nahar, Lamia, Ahmed A. Farghaly, Richard J. Alan Esteves, and Indika U. Arachchige. "Shape controlled synthesis of Au/Ag/Pd nanoalloys and their oxidation-induced self-assembly into electrocatalytically active aerogel monoliths." Chemistry of Materials29, no. 18 (2017): 7704-7715.Farghaly, Ahmed A., Rezaul K. Khan, and Maryanne M. Collinson. "Biofouling-resistant platinum bimetallic alloys." ACS applied materials & interfaces10, no. 25 (2018): 21103-21112.Khan, Rezaul K., Ahmed A. Farghaly, Tiago A. Silva, Dexian Ye, and Maryanne M. Collinson. "Gold-Nanoparticle-Decorated Titanium Nitride Electrodes Prepared by Glancing-Angle Deposition for Sensing Applications." ACS Applied Nano Materials2, no. 3 (2019): 1562-1569.Farghaly, Ahmed A., Mai Lam, Christopher J. Freeman, Badharinadh Uppalapati, and Maryanne M. Collinson. "Potentiometric measurements in biofouling solutions: comparison of nanoporous gold to planar gold." Journal of The Electrochemical Society 163, no. 4 (2015): H3083.

(Invited) Machine Learning Driven Discovery and Optimization of Perovskite Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts

Electrochemical energy conversion technologies based on the oxygen evolution reaction (OER) are at the heart of many efforts to achieve a sustainable future, carbon-free fuel, and a circular economy. The sluggish kinetics of oxygen electrocatalysis, as well as the high overpotential required to attain practical current densities, limit the efficiency of several promising electrochemical technologies, including water and carbon dioxide electrolyzers, metal–oxygen batteries, and fuel cells. The most efficient OER catalysts are precious metals such as iridium- and ruthenium-based materials (i.e., IrO2 and RuO2). This fact represents a challenge against the cost-effective implementation of these electrolysis technologies.1-3 As a result, there is a necessity for the development of cost effective PGM-free OER catalysts, with equivalent or superior activity and durability to the PGM catalysts.This presentation will describe the application of machine learning (ML)-guided materials discovery and high-throughput synthesis to address these concerns, taking advantage of the intriguing properties and rich chemistry of nanoporous materials, the demonstrated capability of machine learning (ML)-guided materials discovery, and the high OER electrocatalytic activity of perovskites especially in alkaline media.4-10 Simulation of over 8,000 perovskites across a variety of cell sizes, space groups, and compositions using density functional theory (DFT) has been performed. Mining of the simulation data indicated that experimentally-known perovskites are characterized by low energy above the thermodynamic convex hull. Efficient search algorithms, deep learning-based models, and DFT calculations have been used to explore the space of perovskite oxides to produce novel compositions with tailored electronic descriptors. Promising compositions designed for high activity and stability are then selected for high throughput automated synthesis using the High-Throughput Research Facility at Argonne National Laboratory. A correlation between the phase purity, annealing temperature and OER activity has been identified.AcknowledgementsThis work was supported by the U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) under the DIFFERENTIATE program. This work was authored in part by Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. DE-AC02-06CH11357.References Katsounaros, Ioannis, Serhiy Cherevko, Aleksandar R. Zeradjanin, and Karl JJ Mayrhofer. "Oxygen electrochemistry as a cornerstone for sustainable energy conversion." Angewandte Chemie International Edition53, no. 1 (2014): 102-121.Lee, Youngmin, Jin Suntivich, Kevin J. May, Erin E. Perry, and Yang Shao-Horn. "Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions." The journal of physical chemistry letters3, no. 3 (2012): 399-404.Cherevko, S. et al. Oxygen and hydrogen evolution reactions on Ru, RuO2, Ir, and IrO2 thin film electrodes in acidic and alkaline electrolytes: A comparative study on activity and stability. Today 262, 170–180 (2016).Nahar, Lamia, Ahmed A. Farghaly, Richard J. Alan Esteves, and Indika U. Arachchige. "Shape controlled synthesis of Au/Ag/Pd nanoalloys and their oxidation-induced self-assembly into electrocatalytically active aerogel monoliths." Chemistry of Materials29, no. 18 (2017): 7704-7715.Farghaly, Ahmed A., Rezaul K. Khan, and Maryanne M. Collinson. "Biofouling-resistant platinum bimetallic alloys." ACS applied materials & interfaces10, no. 25 (2018): 21103-21112.Khan, Rezaul K., Ahmed A. Farghaly, Tiago A. Silva, Dexian Ye, and Maryanne M. Collinson. "Gold-Nanoparticle-Decorated Titanium Nitride Electrodes Prepared by Glancing-Angle Deposition for Sensing Applications." ACS Applied Nano Materials2, no. 3 (2019): 1562-1569.Farghaly, Ahmed A., Mai Lam, Christopher J. Freeman, Badharinadh Uppalapati, and Maryanne M. Collinson. "Potentiometric measurements in biofouling solutions: comparison of nanoporous gold to planar gold." Journal of The Electrochemical Society163, no. 4 (2015): H3083.Suntivich, Jin, Kevin J. May, Hubert A. Gasteiger, John B. Goodenough, and Yang Shao-Horn. "A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles." Science334, no. 6061 (2011): 1383-1385.Hwang, Jonathan, Zhenxing Feng, Nenian Charles, Xiao Renshaw Wang, Dongkyu Lee, Kelsey A. Stoerzinger, Sokseiha Muy et al. "Tuning perovskite oxides by strain: electronic structure, properties, and functions in (electro) catalysis and ferroelectricity." Materials Today31 (2019): 100-118.Gómez-Bombarelli, Rafael, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. "Automatic chemical design using a data-driven continuous representation of molecules." ACS central science 4, no. 2 (2018): 268-276.

Guest Editorial: Situational awareness of integrated energy systems

Guest Editorial: Situational awareness of integrated energy systems

Electric Propulsors for Zero-Emission Aircraft: Partially superconducting machines

Be it through combustion in a turbine or via direct conversion with fuel cells, hydrogen electric is considered the most attractive path toward a future with zero-emission aircraft. An internal combustion engine, with hydrogen as the fuel, results in very low amounts of nitrogen oxide emissions, providing an attractive option for a turboelectric propulsion system. Additionally, new lean combustion strategies show promising results to further minimize these emissions. On the other hand, a fuel cell-based electrical architecture, with no direct emissions, can be used to power a fully electric propulsion system. Whichever method is used, electric machines play a key role in hydrogen-powered aircraft as it is the unit that efficiently converts electrical power to mechanical power. With promising trends pointing toward hydrogen as the key to electrification of large transport aircraft, efficient and “flightweight” megawatt (MW)-class motors and, in some cases, tens of MW are needed. NASA’s Advanced Air Transport Technology (AATT) program anticipated this and launched several NASA Research Announcements, leading to the continued development and availability of enabling technologies. Other agencies are also stepping up to the challenge with integral R&D, such as thermal management-related work supported by Advanced Research Projects Agency–Energy. In this article, we discuss technology options, developments, challenges, and trends toward an electric propulsor that will power the future of aviation.

Discovery of High-Performing Metal-Organic Frameworks for On-Board Methane Storage and Delivery via LNG-ANG Coupling: High-Throughput Screening, Machine Learning, and Experimental Validation.

Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG–ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG–ANG to attain the advanced research projects agency‐energy (ARPA‐E) target for onboard methane storage has not been fully investigated. In this work, large‐scale computational screening is performed for 5446 metal–organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm3(STP) cm−3) are identified. Furthermore, structure–performance relationships are realized under the LNG–ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT‐23(Cu) and DUT‐23(Co) show higher working capacities (≈373 cm3(STP) cm−3) than that of any other porous material under ANG or LNG–ANG conditions, and excellent stability during cyclic LNG–ANG operation.