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An open-hardware automated workflow for mesoporous colloidal silica synthesis is developed and applied to study a compositional parameter space.
This presentation describes fatigue experiments and discusses dynamic mechanical loading for Cu ribbon interconnects.
The erosion of the global security environment over the last two decades has been rapid and dramatic. It appears also to be accelerating. There are deep and widening concerns in the United States and among its allies and partners that deterrence too has eroded at both the conventional and nuclear levels. The Strategic Posture Commission captured the essence of the problem in its October 2023 report—the United States seeks to meet the challenges of complex multipolar nuclear rivalry in the 21 st century with a slimmed down version of a nuclear deterrent designed in the middle of the 20 th century. With great urgency, the Commission recommended that something more and/or different is needed than simply the replacement of legacy systems with modern variants. But the U.S. nuclear security enterprise was reshaped after the Cold War to support the stockpile stewardship mission—not renewed designed, engineering, and production at scale. It has long fallen short of the agility, flexibility, and adaptiveness periodically asked of it by national leaders. In recent years, however, the message of urgency has been clearly received in the enterprise. Its culture has begun to shift. It has also been received by those in Washington D.C. responsible for funding and guiding the enterprise. The result is an enterprise in transition. Stockpile stewardship has given way to stockpile transformation. Risk aversion is giving way to risk management. Innovation and adaptation have been embraced. Experience to date has yielded some important lessons about what works and what doesn’t in terms of accelerating and delivering the needed innovation and adaptation. It has revealed the enduring character of some challenges. But it has also revealed that a much more agile and effective enterprise is within the national reach—something that will pay important dividends for deterrence, assurance, strategic stability, and peace.
Genome editing is transforming plant biology by enabling precise DNA modifications. However, delivery of editing systems into plants remains challenging, often requiring slow, genotype-specific methods such as tissue culture or transformation1. Plant viruses, which naturally infect and spread to most tissues, present a promising delivery system for editing reagents. However, many viruses have limited cargo capacities, restricting their ability to carry large CRISPR-Cas systems. Here we engineered tobacco rattle virus (TRV) to carry the compact RNA-guided TnpB enzyme ISYmu1 and its guide RNA. This innovation allowed transgene-free editing of Arabidopsis thaliana in a single step, with edits inherited in the subsequent generation. By overcoming traditional reagent delivery barriers, this approach offers a novel platform for genome editing, which can greatly accelerate plant biotechnology and basic research.
The advent of next-generation hydrogen-based engines necessitates materials capable of withstanding temperatures beyond the reach of current superalloys. SiC-based ceramic matrix composites, augmented with environmental barrier coatings (EBCs), present a promising materials solution. Given the active search for effective and durable EBCs, there is a pressing need for modeling tools to understand and predict damage evolution in these materials to help accelerate their development. This study introduces a phase-field model (PFM) designed to simulate the critical role of thermally grown oxides (TGO) in the degradation and failure of EBCs. The model accounts for the severe volume expansion due to oxidation, alongside phase transformations and microstructural evolution during thermal cycling, offering a comprehensive view of the damage processes. Simulation results are validated against experimental findings reported in the literature, establishing the model's potential as a significant tool for understanding and improving the resilience of EBCs in cyclic oxidative environments.
The advent of next-generation engines necessitates materials capable of withstanding temperatures beyond the reach of current superalloys. SiC-based ceramic matrix composites, augmented with environmental barrier coatings (EBCs), present a promising materials solution. Given the active search for effective and durable EBCs, there is a pressing need for modeling tools to understand and predict damage evolution in these materials to help accelerate their development. This study introduces a phase-field model (PFM) designed to simulate the thermally grown oxides (TGO) and phase transformation in the degradation and failure of EBCs. The model accounts for the severe volume expansion due to oxidation, alongside phase transformations and porosity evolution during thermal cycling, offering a comprehensive view of the damage processes. Simulation results are validated against experimental findings reported in the literature, establishing the model's potential as a significant tool for understanding and improving the resilience of EBCs in cyclic oxidative environments.
We present a model of x-ray thermal diffuse scattering (TDS) from a cubic polycrystal with an arbitrary crystallographic texture, based on the classic approach of Warren [B. E. Warren, Acta Crystallogr. 6, 803 (1953)]. We compare the predictions of our model with femtosecond x-ray diffraction patterns gathered from ambient and dynamically compressed rolled copper foils obtained at the High Energy Density instrument of the European X-Ray Free-Electron Laser facility and find that the texture-aware TDS model yields more accurate results than does the conventional powder model owed to Warren. Nevertheless, we further show: with sufficient angular detector coverage, the TDS signal is largely unchanged by sample orientation and in all cases strongly resembles the signal from a perfectly random powder; shot-to-shot fluctuations in the TDS signal resulting from grain-sampling statistics are at the percent level, in stark contrast to the fluctuations in the Bragg-peak intensities (which are over an order of magnitude greater); and TDS is largely unchanged even following texture evolution caused by compression-induced plastic deformation. We conclude that TDS is robust against texture variation, making it a flexible temperature diagnostic applicable just as well to off-the-shelf commercial foils as to ideal powders.
Initial engineering design studies examining the application of state-of-the-art carbon capture technology at industrial plants contain generally overlooked real-world design considerations for near-term deployment of point source capture (PSC). The implementation of PSC across a wide range of industrial applications presents unique challenges associated with fluctuating CO<sub>2</sub> concentrations, flue gas composition, and utility and land availability. In this article, seven recent industrial retrofit PSC projects are reviewed to investigate the impact of site-specific factors on project design and cost. Across the seven projects, three capture technology classes and four industrial applications are considered, allowing insight into industry-specific opportunities for PSC technology synergy. Common challenges across projects and proposed design solutions are highlighted to propagate ideas and solutions to close the technology gaps and accelerate learning rates.
A new open-source code for the simulation of the diffusion flamelet equations is proposed. Emphasis is placed on using an approximate Jacobian to reduce the computational cost of the matrix operations. Performance of the proposed solvers is tested by performing flamelet calculations with kinetic mechanisms of varying sizes. For the unity Lewis number equations, the present iterative Newton solver using an approximate Jacobian greatly outperforms direct Newton solvers using exact Jacobians. The computation cost scales linearly with the number of species, leading to a reduction in solution times by two orders of magnitude for mechanisms containing thousands of species. The applicability of the Jacobian approximations to the solution of the non-unity Lewis number flamelet equations is assessed. The approximations are generally inadequate to solve the full non-unity Lewis number equations but can be used in some applications depending on the balance of terms in the flamelet equations. As an example, the flamelet solver is applied to the study of sooting tendencies in laminar co-flow diffusion flames where modified non-unity Lewis number flamelet equations, previously shown to accurately reproduce experimentally-measured Yield Sooting Indices (YSI), are solved. Here, the accelerated flamelet solver is well suited for sensitivity analysis and uncertainty quantification with large detailed kinetic mechanisms, tasks for which the computational cost was previously prohibitive.
The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.
Highlights: Black-Right-Pointing-Pointer Engineered kinesin-M13 and calmodulin involving single cysteine were prepared. Black-Right-Pointing-Pointer CaM mutant was cross-linked to dimer by bifunctional thiol reactive reagent. Black-Right-Pointing-Pointer Kinesin-M13 was dimerized via CaM dimer in the presence of calcium. Black-Right-Pointing-Pointer Function of the engineered kinesin was regulated by a Ca{sup 2+}-calmodulin dimer linker. -- Abstract: The kinesin-microtubule system holds great promise as a molecular shuttle device within biochips. However, one current barrier is that such shuttles do not have 'on-off' control of their movement. Here we report the development of a novel molecular motor powered by an accelerator and brake system, using a kinesin monomer and a calmodulin (CaM) dimer. The kinesin monomer, K355, was fused with a CaM target peptide (M13 peptide) at the C-terminal part of the neck region (K355-M13). We also prepared CaM dimers using CaM mutants (Q3C), (R86C), or (A147C) and crosslinkers that react with cysteine residues. Following induction of K355-M13 dimerization with CaM dimers, we measured K355-M13 motility and found that it can be reversibly regulated in a Ca{sup 2+}-dependent manner. We also found that velocities of K355-M13 varied depending on the type and crosslink position of the CaM dimer used; crosslink length also had a moderate effect on motility. These results suggest Ca{sup 2+}-dependent dimerization of K355-M13 could be used as a novel molecular shuttle, equipped with an accelerator and brake system, for biochip applications.
We derive a set of ptychography phase-retrieval iterative engines based on proximal algorithms originally developed in convex optimization theory, and discuss their connections with existing ones. The use of proximal operator creates a simple frame work that allows us to incorporate the effect of noise from a maximum-likelihood (ML) principle. We focus on three particular algorithms, namely proximal minimization, alternating direction method of multiplier and accelerated proximal gradient (APG). We benchmark their performance with numerical simulations, and discuss their optimal conditions for convergence and accuracy. An experimental dataset is used to demonstrate their effectiveness as well, in which case an array of cubic Au nanoparticles with a size of 50 nm is imaged. We show that with the presence of Poisson noise, a dataset with photon counts up to 10 4 at one detector pixel already requires ML-based methods to achieve a stable convergence. Among the three algorithms derived in this work, APG method is reported first time for its application in ptychographic reconstruction and shows superior performance in terms of both accuracy and convergence rate with a noisy dataset.
On March 11, 2011, the magnitude 9.0 Great East Japan earthquake, Tohoku, hit off the Fukushima coast of Japan. This was one of the most powerful earthquakes in recorded history and the most powerful one known to have hit Japan. The ensuing tsunami devastated a huge area resulting in some 25,000 persons confirmed dead or missing. The perfect storm was complete when the tsunami then found the four reactor, Fukushima-Daiichi Nuclear Station directly in its destructive path. While recovery systems admirably survived the powerful earthquake, the seawater from the tsunami knocked the emergency cooling systems out and did extensive damage to the plant and site. Subsequent hydrogen generation caused explosions which extended this damage to a new level and further flooded the buildings with highly contaminated water. Some 2 million people were evacuated from a fifty mile radius of the area and evaluation and cleanup began. Teams were assembled in Tokyo the first week of April to lay out potential plans for the immediate treatment of some 63 million gallons (a number which later exceeded 110 million gallons) of highly contaminated water to avoid overflow from the buildings as well as supply the desperately needed clean cooling water for the reactors. A system had to be deployed with a very brief cold shake down and hot startup before the rainy season started in early June. Joined by team members Toshiba (oil removal system), AREVA (chemical precipitation system) and Hitachi-GE (RO system), Kurion (cesium removal system following the oil separator) proposed, designed, fabricated, delivered and started up a one of a kind treatment skid and over 100 metric tons of specially engineered and modified Ion Specific Media (ISM) customized for this very challenging seawater/oil application, all in seven weeks. After a very short cold shake down, the system went into operation on June 17, 2011 on actual waste waters far exceeding 1 million Bq/mL in cesium and many other isotopes. One must remember that, in addition to attempting to do isotope removal in the competition of seawater (as high as 18,000 ppm sodium due to concentration), some 350,000 gallons of turbine oil was dispersed into the flooded buildings as well. The proposed system consisted of a 4 guard vessel skid for the oil and debris, 4 skids containing 16 cesium towers in a lead-lag layout with removable vessels (sent to an interim storage facility), and a 4 polishing vessel skid for iodine removal and trace cesium levels. At a flow rate of at least 220 gallons per minute, the system has routinely removed over 99% of the cesium, the main component of the activity, since going on line. To date, some 50% of the original activity has been removed and stabilized and cold shutdown of the plant was announced on December 10, 2011. In March and April alone, 10 cubic feet of Engineered Herschelite was shipped to Seabrook Nuclear Power Plant, NPP, to support the April 1, 2011 outage cleanup; 400 cubic feet was shipped to Oak Ridge National Laboratory (ORNL) for strontium (Sr-90) ground water remediation; and 6000 cubic feet (100 metric tons, MT, or 220,400 pounds) was readied for the Fukushima Nuclear Power Station with an additional 100 MT on standby for replacement vessels. This experience and accelerated media production in the U.S. bore direct application to what was to soon be used in Fukushima. How such a sophisticated and totally unique system and huge amount of media could be deployable in such a challenging and changing matrix, and in only seven weeks, is outlined in this paper as well as the system and operation itself. As demonstrated herein, all ten major steps leading up to the readiness and acceptance of a modular emergency technology recovery system were met and in a very short period of time, thus utilizing three decades of experience to produce and deliver such a system literally in seven weeks: - EPRI - U.S. Testing and Experience Leading to Introduction to EPRI - Japan and Subsequently TEPCO Emergency Meetings - Three Mile Island (TMI) Media and Vitrification Experience by PNNL - Commercial Nuclear Power Plant Media Experience (including long term Cs removal) - DOE Low Active Waste (LAW) and High Level Waste (HLW) in High Salt and pH Conditions Media and Vitrification Experience - National Laboratory (e.g. Oak Ridge National Laboratory, ORNL) Ground Water Media Experience - Gulf Oil Spill Media Experience in Seawater - All Media Had to be Fully Tested at High Rad Levels in Seawater and Oil Before Arriving in Japan - Final Waste Form and Disposal Experience (e.g., vitrification) - 100 Metric Tons (6000 cubic feet or 220,400 pounds) of Media had to be Immediately Available with the same amount in production as replacement media. [To date, for 2011, 400 MT of media have been prepared for Japan alone.] - Remote Operation, Modular Water Treatment Equipment Design and Fabrication in both Commercial NPP and DOE Canyon Operations. (authors)
Microbes regulate their dynamic behaviors using the chemical and physical characteristics of their environment. The ability of microbes to continuously convert this physicochemical information into biochemical information and to use organic matter in the environment as a power source makes these organisms attractive as chassis for building sensors. However, most biosensors have severe limitations when considering applications in hard-to-image settings like soils, sediments, and wastewater. Emerging technologies at the interface of biomolecular design, microbiome engineering, and synthetic biology offer new tools to program cells and communities as biosensors for these settings. Here, in this review, we describe innovations in biosensor outputs that are enabling new applications in complex environments, including reporters that are read out using electrochemical, gas chromatography, hyperspectral imaging, and next-generation sequencing methods. We also discuss computational advances that are accelerating the diversification of sensing components by mining metagenomics data for new transcriptional regulators and by designing allosteric protein switches that directly regulate reporter outputs using analytes. We highlight emerging opportunities for programming undomesticated microbes in communities to function as distributed sensors in the environment. Finally, we discuss the need for responsible biosensor development and to modernize regulatory frameworks to support evidence-based assessment of environmental biosensors.
Laser Direct Energy Deposition (DED) is a metal additive manufacturing technique with the ability to fabricate large and complex parts through deposition of metal powders. However, achieving high-density parts and targeted build heights using DED can be challenging due to the large number of highly sensitive process variables. This work proposes a robust fabrication parameter optimization framework to generate process maps for primary parameters in DED, including laser power, scan speed, mass flow rate, hatch spacing, and layer height. Simple single-track experiments were utilized to map out the parameter space, and a combination of geometric criteria for hatch spacing and layer height were proposed to determine parameter sets that achieve both targeted build heights and mitigate porosity formation. Using this framework, specimens with >99 % density and consistent mechanical properties were successfully fabricated over a wide range of process parameters for an Fe-9wt.%Cr (Fe9Cr) alloy, a surrogate for radiation damage-resistant reduced activation ferritic/martensitic (RAFM) steels. Processing these materials using DED is of particular interest in the development of plasma facing components for nuclear fusion applications. The microstructure and mechanical properties of as-printed Fe9Cr were characterized using optical and electron microscopy, X-ray diffraction, and uniaxial tensile tests. As-printed Fe9Cr displayed ~25 % elongation and ultimate tensile strengths of up to 475 MPa which is comparable to similar wrought alloys. Finally, the proposed framework will allow for accelerated DED parameter optimization for novel alloy systems, as well as open the possibility for local microstructure control while simultaneously mitigating defect formation.
The heavy-duty transportation sector has primarily relied on conventional diesel combustion engines given their reliability and high thermal efficiency relative to spark ignition engines, but increased focus on reducing greenhouse gas emissions has led to investigation into alternative fuels. Gaseous hydrogen fuel has garnered a great deal of recent interest in the engine community given it has zero carbon, but hydrogen is not available at the scale and cost that petroleum fuels are currently available, and this is a barrier to adoption for industries that are looking to decarbonize their operations. Because of the fuel flexibility provided, dual fuel technology offers a pathway for some industries to adopt hydrogen as a fuel source while maintaining sufficient flexibility in times and locations where the new fuel is not yet available. This computational study investigates dual fuel combustion in a large bore locomotive engine architecture using direct injected diesel and port injected gaseous hydrogen fuel. With an optimal port fuel injection configuration from previous work, simulations of varying substitution ratio, compression ratio, manifold air temperature, diesel injection timing, and diesel injection pressure were performed to understand their effect on combustion performance. Results indicated that both increased substitution ratio and higher intake air temperature accelerates hydrogen flame propagation and can result in high peak cylinder pressures. Additionally, diesel injection timing and injection pressure were demonstrated as effective methods for controlling dual fuel combustion heat release rates.
The theme of this year’s meeting was “Predictivity: Now and in the Future”. After welcoming remarks, Erik Draeger gave a talk on the NNSA Labs’ history of predictive simulation and the new challenges faced by upcoming architecture changes. He described an example where the volume of analysis data produced by a set of inertial confinement fusion (ICF) simulations on the Trinity machine was too large to store or transfer, and the steps needed to reduce it to a manageable size. He also described the software re-engineering plan for LLNL’s suite of multiphysics codes and physics packages with a new push toward common components, making collaboration with teams like the CCMSC who already have experience trying to architect complex multiphysics code infrastructure on next-generation architectures all the more important. Phil Smith then gave an overview outlining the goals of the project, namely to accelerate development of new technology in the form of high efficiency carbon capture pulverized coal power generation as well as further optimize existing state of the art designs. He then presented a summary of the Center’s top-down uncertainty quantification approach, in which ultimate target predictivity informs uncertainty targets for lower-level components, and gave data on how close all the different components currently are to their targets. Most components still need an approximately two-fold reduction in uncertainty to hit the ultimate predictivity target, but the current accuracy is already rather impressive.