Idaho National Laboratory

The magnitude 7.3 Landers earthquake of 28 June 1992 triggered a remarkably sudden and widespread increase in earthquake activity across much of the western United States. The triggered earthquakes, which occurred at distances up to 1250 kilometers (17 source dimensions) from the Landers mainshock, were confined to areas of persistent seismicity and strike-slip to normal faulting. Many of the triggered areas also are sites of geothermal and recent volcanic activity. Static stress changes calculated for elastic models of the earthquake appear to be too small to have caused the triggering. The most promising explanations involve nonlinear interactions between large dynamic strains accompanying seismic waves from the mainshock and crustal fluids (perhaps including crustal magma).

Experimental and commercial microvolume fluorimeters with rapid temperature control are described. Fluorescence optics adopted from flow cytometry were used to interrogate 1-10-microL samples in glass capillaries. Homogeneous temperature control and rapid change of sample temperatures (10 degrees C/s) were obtained by a circulating air vortex. A prototype 2-color, 32-sample version was constructed with a xenon arc for excitation, separate excitation and emission paths, and photomultiplier tubes for detection. The commercial LightCycler, a 3-color, 24-sample instrument, uses a blue light-emitting diode for excitation, paraxial epi-illumination through the capillary tip and photodiodes for detection. Applications include analyte quantification and nucleic acid melting curves with fluorescent dyes, enzyme assays with fluorescent substrates and techniques that use fluorescence resonance energy transfer. Microvolume capability allows analysis of very small or expensive samples. As an example of one application, rapid cycle DNA amplification was continuously monitored by three different fluorescence techniques, Which included using the double-stranded DNA dye SYBR Green I, a dual-labeled 5'-exonuclease hydrolysis probe, and adjacent fluorescein and Cy5z-labeled hybridization probes. Complete amplification and analysis requires only 10-15 min.

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.

Preface. Introduction. 1. Background material. 2. Experimental setup. 3. Spectrum analysis and energy measurements. 4. Efficiency calibration and emission-rate measurements. 5. Applications. 6. Atomic and nuclear data. References. Indexes.

The deep subseafloor biosphere is among the least-understood habitats on Earth, even though the huge microbial biomass therein plays an important role for potential long-term controls on global biogeochemical cycles. We report here the vertical and geographical distribution of microbes and their phylogenetic diversities in deeply buried marine sediments of the Pacific Ocean Margins. During the Ocean Drilling Program Legs 201 and 204, we obtained sediment cores from the Peru and Cascadia Margins that varied with respect to the presence of dissolved methane and methane hydrate. To examine differences in prokaryotic distribution patterns in sediments with or without methane hydrates, we studied >2,800 clones possessing partial sequences (400-500 bp) of the 16S rRNA gene and 348 representative clone sequences (approximately 1 kbp) from the two geographically separated subseafloor environments. Archaea of the uncultivated Deep-Sea Archaeal Group were consistently the dominant phylotype in sediments associated with methane hydrate. Sediment cores lacking methane hydrates displayed few or no Deep-Sea Archaeal Group phylotypes. Bacterial communities in the methane hydrate-bearing sediments were dominated by members of the JS1 group, Planctomycetes, and Chloroflexi. Results from cluster and principal component analyses, which include previously reported data from the West and East Pacific Margins, suggest that, for these locations in the Pacific Ocean, prokaryotic communities from methane hydrate-bearing sediment cores are distinct from those in hydrate-free cores. The recognition of which microbial groups prevail under distinctive subseafloor environments is a significant step toward determining the role these communities play in Earth's essential biogeochemical processes.

Abstract Developing uniformly formatted, densified feedstock from lignocellulosic biomass is of interest to achieve consistent physical properties such as size and shape, bulk and unit density, and durability, which significantly influence storage, transportation and handling characteristics, and, by extension, feedstock cost and quality. A variety of densification systems are considered for producing a uniform format feedstock commodity for bioenergy applications, including (i) pellet mill, (ii) cuber, (iii) screw extruder, (iv) briquette press, (v) roller press, (vi) tablet press, and (vii) agglomerator. Each of these systems has varying impacts on feedstock chemical and physical properties, and energy consumption. This review discusses the suitability of these densification systems for biomass feedstocks and the impact these systems have on specific energy consumption and end‐product quality. For example, a briquette press is more flexible in terms of feedstock variables where higher moisture content and larger particles are acceptable for making good quality briquettes; or among different densification systems, a screw press consumes the most energy because it not only compresses but also shears and mixes the material. Pre‐treatment options like pre‐heating, grinding, steam explosion, torrefaction, and ammonia fiber explosion (AFEX) can also help to reduce specific energy consumption during densification and improve binding characteristics. Binding behavior can also be improved by adding natural binders, such as proteins, or commercial binders, such as lignosulfonates. The quality of the densified biomass for both domestic and international markets is evaluated using PFI (United States standard) or CEN (European standard). Published in 2011 by John Wiley & Sons, Ltd Re‐use of this article is permitted in accordance with the Terms and Conditions set out at http://wileyonlinelibrary.com/onlineopen#OnlineOpen_Terms

Renewable transportation fuels from biomass have the potential to substantially reduce greenhouse gas emissions and diversify global fuel supplies. Thermal conversion by fast pyrolysis converts up to 75% of the starting plant material (and its energy content) to a bio-oil intermediate suitable for upgrading to motor fuel. Woody biomass, by far the most widely-used and researched material, is generally preferred in thermochemical processes due to its low ash content and high quality bio-oil produced. However, the availability and cost of biomass resources, e.g. forest residues, agricultural residues, or dedicated energy crops, vary greatly by region and will be key determinates in the overall economic feasibility of a pyrolysis-to-fuel process. Formulation or blending of various feedstocks, combined with thermal and/or chemical pretreatment, could facilitate a consistent, high-volume, lower-cost biomass supply to an emerging biofuels industry. However, the impact of biomass type and pretreatment conditions on bio-oil yield and quality, and the potential process implications, are not well understood. This literature review summarizes the current state of knowledge regarding the effect of feedstock and pretreatments on the yield, product distribution, and upgradability of bio-oil.

Torrefaction of biomass can be described as a mild form of pyrolysis at temperatures typically ranging between 200 and 300°C in an inert and reduced environment. Common biomass reactions during torrefaction include devolatilization, depolymerization, and carbonization of hemicellulose, lignin, and cellulose. The torrefaction process produces a brown to black uniform solid product, as well as condensable (water, organics, and lipids) and Noncondensable gases (CO 2 , CO, and CH 4 ). Typically during torrefaction, 70% of the mass is retained as a solid product, containing 90% of the initial energy content, while 30% of the lost mass is converted into condensable and noncondensable products. The system’s energy efficiency can be improved by reintroducing the material lost during torrefaction as a source of heat. Torrefaction of biomass improves its physical properties like grindability; particle shape, size, and distribution; pelletability; and proximate and ultimate composition like moisture, carbon and hydrogen content, and calorific value. Compared to raw biomass, the carbon content and calorific value of torrefied biomass increases by 15–25% wt, while the moisture content decreases to <3% (w.b.). Torrefaction decreases the grinding energy by about 70%, and the ground torrefied biomass has improved sphericity, particle surface area, and particle size distribution. Torrefied biomass pelletization at temperatures of 225°C decreases the specific energy consumption and increases the capacity of the mill by a factor of 2. The loss of the OH functional group during torrefaction makes the material hydrophobic (i.e., loses the ability to attract water molecules) and more stable against chemical oxidation and microbial degradation. These improved properties make torrefied biomass particularly suitable for cofiring in power plants and as an upgraded feedstock for gasification.

Harnessing modern parallel computing resources to achieve complex multiphysics simulations is a daunting task. The Multiphysics Object Oriented Simulation Environment (MOOSE) aims to enable such development by providing simplified interfaces for specification of partial differential equations, boundary conditions, material properties, and all aspects of a simulation without the need to consider the parallel, adaptive, nonlinear, finite element solve that is handled internally. Through the use of interfaces and inheritance, each portion of a simulation becomes reusable and composable in a manner that allows disparate research groups to share code and create an ecosystem of growing capability that lowers the barrier for the creation of multiphysics simulation codes. Included within the framework is a unique capability for building multiscale, multiphysics simulations through simultaneous execution of multiple sub-applications with data transfers between the scales. Other capabilities include automatic differentiation, scaling to a large number of processors, hybrid parallelism, and mesh adaptivity. To date, MOOSE-based applications have been created in areas of science and engineering such as nuclear physics, geothermal science, magneto-hydrodynamics, seismic events, compressible and incompressible fluid flow, microstructure evolution, and advanced manufacturing processes.

The modern power system is progressing from a synchronous machine-based system towards an inverter-dominated system, with large-scale penetration of renewable energy sources (RESs) like wind and photovoltaics. RES units today represent a major share of the generation, and the traditional approach of integrating them as grid following units can lead to frequency instability. Many researchers have pointed towards using inverters with virtual inertia control algorithms so that they appear as synchronous generators to the grid, maintaining and enhancing system stability. This paper presents a literature review of the current state-of-the-art of virtual inertia implementation techniques, and explores potential research directions and challenges. The major virtual inertia topologies are compared and classified. Through literature review and simulations of some selected topologies it has been shown that similar inertial response can be achieved by relating the parameters of these topologies through time constants and inertia constants, although the exact frequency dynamics may vary slightly. The suitability of a topology depends on system control architecture and desired level of detail in replication of the dynamics of synchronous generators. A discussion on the challenges and research directions points out several research needs, especially for systems level integration of virtual inertia systems.

In addition to identifying the proteins that have a role in underwater adhesion by marine mussels, research efforts have focused on identifying the genes responsible for the adhesive proteins, environmental factors that may influence protein production, and strategies for producing natural adhesives similar to the native mussel adhesive proteins. The production-scale availability of recombinant mussel adhesive proteins will enable researchers to formulate adhesives that are water-impervious and ecologically safe and can bind materials ranging from glass, plastics, metals, and wood to materials, such as bone or teeth, biological organisms, and other chemicals or molecules. Unfortunately, as of yet scientists have been unable to duplicate the processes that marine mussels use to create adhesive structures. This study provides a background on adhesive proteins identified in the blue mussel, Mytilus edulis, and introduces our research interests and discusses the future for continued research related to mussel adhesion.

Attack graphs are important tools for analyzing security vulnerabilities in enterprise networks. Previous work on attack graphs has not provided an account of the scalability of the graph generating process, and there is often a lack of logical formalism in the representation of attack graphs, which results in the attack graph being difficult to use and understand by human beings. Pioneer work by Sheyner, et al. is the first attack-graph tool based on formal logical techniques, namely model-checking. However, when applied to moderate-sized networks, Sheyner's tool encountered a significant exponential explosion problem. This paper describes a new approach to represent and generate attack graphs. We propose logical attack graphs, which directly illustrate logical dependencies among attack goals and configuration information. A logical attack graph always has size polynomial to the network being analyzed. Our attack graph generation tool builds upon MulVAL, a network security analyzer based on logical programming. We demonstrate how to produce a derivation trace in the MulVAL logic-programming engine, and how to use the trace to generate a logical attack graph in quadratic time. We show experimental evidence that our logical attack graph generation algorithm is very efficient. We have generated logical attack graphs for fully connected networks of 1000 machines using a Pentium 4 CPU with 1GB of RAM.

In this article, we examine prognostics and health management (PHM) issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case. We will show where advanced regression, classification, and state estimation algorithms have an important role in the solution of the problem and in the data collection scheme for battery health management that we used for this case study.

Steam cracking is a well-established commercial technology for ethylene production. Despite decades of optimization efforts, the process is, nevertheless, highly energy and carbon intensive. This review covers the recent advances in alternative approaches that hold promise in the intensification of ethylene production from hydrocarbon feedstocks ranging from methane to naphtha. Oxidative as well as nonoxidative approaches using conventional, chemical looping, membrane, electrochemical, and plasma-assisted systems are discussed. We note that catalysts, electrocatalysts, and/or redox catalysts play critical roles in the performance of these alternative ethylene production technologies. Meanwhile, the complexity in producing polymer-grade ethylene also requires comprehensive considerations of not only (catalytic) reactions for ethylene formation but also feedstock preparation (e.g., air separation for oxidative conversion) and product separations. Although these alternative technologies have yet to be commercially implemented, a number of oxidative approaches have shown promise for close to order-of-magnitude reduction in energy consumption and CO2 emissions in comparison to steam cracking. Given the substantial progress in these research areas and the significant increase in C1 and C2 supplies resulting from the US shale gas revolution, we are excited by the enormous opportunities and potential impacts in the advancement and eventual implementation of significantly intensified ethylene production technologies.

The global effort to curb carbon emissions is accelerating demand for clean energy technologies and the materials they rely on. Demand for these materials will only continue to grow, especially as some nations aim to achieve net zero emissions by 2050. While some major materials like steel, copper, and aluminum are already powering the fossil fuel economy, others are more minor materials with potential supply risks. These risks could jeopardize the ability to reduce greenhouse gas emissions within the desirable timeframe to avoid significant climate change. In some cases, it may be necessary to take action to improve the resilience of material supply chains and mitigate supply risks. Understanding the importance of individual materials to clean energy and the supply risks associated with them is necessary to identify which materials may serve as potential roadblocks to a clean energy future. The U.S. Department of Energy (DOE) issued a series of 13 supply chain deep dive assessment reports on various energy technologies in 2022 in response to President Biden’s Executive Order on America’s Supply Chains (E.O. 14017). These reports emphasized that supply chain bottlenecks can occur at any stage of the value chain from mining and refining to component and even sub-system manufacturing. The bottlenecks are a combination of factors such as material availability, equipment availability, work force availability and quality, logistics, regulatory framework, and market conditions. These bottlenecks were worsened during the global Covid-19 pandemic. Its lingering impacts have hindered capacity expansion for material supply chains and prevented product lead-time recovery. One approach to reduce supply chain risks for the United States is to have a strong domestic manufacturing sector with a diverse set of producers. Boosting responsible domestic production would require leveraging the latest science not only in material extraction but also in developing substitutes, recycling, reuse, and remanufacturing. This report is an updated analysis of previous Critical Materials Strategy (CMS) reports published by the DOE in 2010, 2011, and 2019 based on national and global priorities, technology advancement, and technology adoption trends. Like the CMS reports, this analysis presents the results of a formal material criticality assessment to identify which materials are critical to the continued deployment of clean energy technologies globally. The analysis in this report leveraged the DOE supply chain deep dive assessments to develop the initial list of materials to evaluate. This DOE Critical Materials Assessment (CMA) is conducted independently of criticality assessments performed by other U.S. government agencies, such as that conducted by the U.S. Geological Survey (USGS). This analysis complements the USGS critical minerals determination in three aspects. First, the DOE assessment is performed from a global perspective, while the USGS analysis focusses on the importance of minerals to the U.S. economy. Second, this report focuses on the importance of materials to clean energy technologies, rather than to the economy in general. Lastly, this study is forward looking to 2035 based on clean energy deployment scenarios, whereas the USGS assessment is retrospective. Materials evaluated in this report that do not appear in the USGS Critical Minerals List include copper, uranium, electrical steel, and SiC. A draft version of this report received ~80 public comments related to supporting data and methodological improvement. Those comments have been incorporated as much as possible where appropriate. Highlights of findings from this 2023 CMA include: Rare earth materials (neodymium, praseodymium, dysprosium, and terbium) used in magnets in electric vehicle (EV) motors and wind turbine generators continue to be critical. While dysprosium (Dy) and terbium (Tb) are both heavy rare earth elements that serve the same function in magnets, the criticality of Tb is slightly lower than that for Dy in the short term due to the widespread use of Dy in high-grade magnets and Tb’s present role as a substitute. Similarly, praseodymium (Pr) is critical in the medium term but only near critical in the short term because it is more substitutable in magnets than neodymium (Nd); Materials used in batteries for EVs and stationary storage are now considered to be critical. While cobalt (Co) was found to be critical in this and previous reports, lithium (Li) becomes critical in the medium term due to its broader use in various battery chemistries and the rampant growth of the EV industry. Natural graphite is a new addition in this assessment and is also found to be critical; Platinum group metals used in hydrogen electrolyzers, such as platinum (Pr) and iridium (Ir), are critical due to an increased focus on hydrogen technologies to achieve net zero carbon emissions, while those used in catalytic converters, such as rhodium (Rh) and palladium (Pd), were screened out due to the decreased importance of catalytic converters in the medium term; Gallium (Ga) continues to be critical due to its use in light-emitting diodes (LEDs). In addition, the use of Ga has increased in magnet manufacturing and in semiconductor in forms such as gallium arsenide (GaAs) or gallium nitride (GaN); Major materials like Aluminum (Al), copper (Cu), nickel (Ni), and silicon (Si) move from noncritical in the short term to near critical in the medium term due to their importance in electrification; Electrical steel is near critical due to its use in transformers for the grid and electric motors in EVs.

Abstract The protonic ceramic electrochemical cell (PCEC) is an emerging and attractive technology that converts energy between power and hydrogen using solid oxide proton conductors at intermediate temperatures. To achieve efficient electrochemical hydrogen and power production with stable operation, highly robust and durable electrodes are urgently desired to facilitate water oxidation and oxygen reduction reactions, which are the critical steps for both electrolysis and fuel cell operation, especially at reduced temperatures. In this study, a triple conducting oxide of PrNi 0.5 Co 0.5 O 3-δ perovskite is developed as an oxygen electrode, presenting superior electrochemical performance at 400~600 °C. More importantly, the self-sustainable and reversible operation is successfully demonstrated by converting the generated hydrogen in electrolysis mode to electricity without any hydrogen addition. The excellent electrocatalytic activity is attributed to the considerable proton conduction, as confirmed by hydrogen permeation experiment, remarkable hydration behavior and computations.

The safe and economical operation of any nuclear power system relies to a great extent, on the success of the fuel and the materials of construction. During the lifetime of a nuclear power system which currently can be as long as 60 years, the materials are subject to high temperature, a corrosive environment, and damage from high-energy particles released during fission. The fuel which provides the power for the reactor has a much shorter life but is subject to the same types of harsh environments. This article reviews the environments in which fuels and materials from current and proposed nuclear systems operate and then describes how the creation of the Advanced Test Reactor National Scientific User Facility is allowing researchers from across the United States to test their ideas for improved fuels and materials.

Thermochemical Pathway by Indirect Gasification and Mixed Alcohol Synthesis

The multiscale coarse-graining (MS-CG) method [S. Izvekov and G. A. Voth, J. Phys. Chem. B 109, 2469 (2005); J. Chem. Phys. 123, 134105 (2005)] employs a variational principle to determine an interaction potential for a CG model from simulations of an atomically detailed model of the same system. The companion paper proved that, if no restrictions regarding the form of the CG interaction potential are introduced and if the equilibrium distribution of the atomistic model has been adequately sampled, then the MS-CG variational principle determines the exact many-body potential of mean force (PMF) governing the equilibrium distribution of CG sites generated by the atomistic model. In practice, though, CG force fields are not completely flexible, but only include particular types of interactions between CG sites, e.g., nonbonded forces between pairs of sites. If the CG force field depends linearly on the force field parameters, then the vector valued functions that relate the CG forces to these parameters determine a set of basis vectors that span a vector subspace of CG force fields. The companion paper introduced a distance metric for the vector space of CG force fields and proved that the MS-CG variational principle determines the CG force force field that is within that vector subspace and that is closest to the force field determined by the many-body PMF. The present paper applies the MS-CG variational principle for parametrizing molecular CG force fields and derives a linear least squares problem for the parameter set determining the optimal approximation to this many-body PMF. Linear systems of equations for these CG force field parameters are derived and analyzed in terms of equilibrium structural correlation functions. Numerical calculations for a one-site CG model of methanol and a molecular CG model of the EMIM(+)NO(3) (-) ionic liquid are provided to illustrate the method.

The estimation of remaining useful life (RUL) of a faulty component is at the centre of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. RUL prediction needs to contend with multiple sources of errors, like modelling inconsistencies, system noise and degraded sensor fidelity, which leads to unsatisfactory performance from classical techniques like autoregressive integrated moving average (ARIMA) and extended Kalman filtering (EKF). The Bayesian theory of uncertainty management provides a way to contain these problems. The relevance vector machine (RVM), the Bayesian treatment of the well known support vector machine (SVM), a kernel-based regression/classification technique, is used for model development. This model is incorporated into a particle filter (PF) framework, where statistical estimates of noise and anticipated operational conditions are used to provide estimates of RUL in the form of a probability density function (pdf). We present here a comparative study of the above-mentioned approaches on experimental data collected from Li-ion batteries. Batteries were chosen as an example of a complex system whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions.