The Race to Electronic Photography
A participant in some of the Kodak Research Lab's early efforts in CCD-based imaging shares a view of the company's contribution to the development of digital cameras.
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A participant in some of the Kodak Research Lab's early efforts in CCD-based imaging shares a view of the company's contribution to the development of digital cameras.
Driven by favorable economics, policy factors, and decarbonization goals, large-scale solar (LSS, defined here as ground-mounted photovoltaic projects ≥1 MWDC) has expanded rapidly in recent years, with more than 90 gigawatts (GW) now installed across the United States. Growth in LSS deployment is anticipated to accelerate in response to growing electricity demand and utility, state, and federal decarbonization goals. This continued expansion hinges, in part, on the continued support of local residents and decision-makers in communities hosting LSS projects. Understanding the perceptions and attitudes of existing LSS neighbors is critical to inform and enable future LSS deployment, and to improve outcomes for those host communities. As of 2022, there were more than 10 million U.S. homes within 3 miles of LSS plants. Yet, until recently, no comprehensive study had examined the experiences and perceptions of these LSS neighbors. In 2023, a Berkeley Lab team worked with the University of Michigan and Michigan State University to conduct the first-of-its-kind nationally representative survey of LSS neighbors as part of the Community-Centered Solar Development research project. The survey effort ultimately collected 984 responses from residents within 3 miles of existing LSS projects. The survey results are available for download via the full summary analysis slide deck report, as well as a shorter (~4 page) summary brief. The survey instrument (questionnaire) is also available for download for those interested in understanding the survey design or replicating questions in future research.
R&D efforts in support of the Oak Ridge National Laboratory (ORNL) Isotope Program Radioisotope Portfolio are led by the Radioisotope Science and Technology Division (RSTD). In addition to supporting the ORNL Isotope Program Radioisotope Portfolio, RSTD supports a portfolio of research related to fundamental properties of radioisotopes and radioisotope applications, including diagnostic and therapeutic uses of medical radioisotopes, radioisotopes for national security, and the production of 238 Pu for the National Aeronautics and Space Administration (NASA) and US Department of Energy (DOE) Office of Nuclear Energy. RSTD is organized into functional science and engineering groups, with most staff members supporting multiple programs. The goal of this organization is to enable synergy between programs such that R&D advances coming from other programs may provide benefit to the ORNL Isotope Program. R&D within RSTD is focused around addressing five grand challenges, as documented in the strategic plan for the DOE Office of Isotope R&D and Production, or DOE Isotope Program (IP), Radioisotope Production R&D activities at ORNL: 1. Maximizing the scientific output of radioisotope transmutation resources, 2. Maximizing the scientific output of radioisotope processing resources, 3. Minimizing waste and having optimal waste disposition, 4. Focusing on product quality and reliability, and 5. Expanding the use of beneficial isotopes. The ORNL Core R&D program, one of the primary R&D components within the ORNL Isotope Program Radioisotope Portfolio, ranges from benchtop to demonstration activities, with a focus on researching enhanced production techniques, developing emerging isotopes, and developing the talent pipeline for radioisotope science and technology. Projects within the Core R&D Program are led primarily by RSTD staff members. In supporting enhanced production techniques, the Core R&D program presents an opportunity to fund novel R&D that might not be tied to a specific radioisotope product but still presents a high potential for broad applicability in the longer term. In supporting the development of emerging isotopes, the Core R&D program develops high-priority isotopes that are not able to be fully supported through production funds.
A forward-looking research opportunity number (RON) is defined for photovoltaic reliability researchers. The RON enables researchers to prioritize their efforts toward the highest impact. For a given degradation mode, the RON is based on three factors: the effect on levelized cost of electricity, the susceptibility of future module products, and the maturity of accelerated tests that can detect and quantify the mode. Reporting bias is avoided because the RON does not rely on polls. The RON is derived for three example cases: light and elevated temperature degradation, backsheet cracking, and antireflective coating abrasion. Finally, these examples demonstrate that targeted research has reduced the risk for these modes over the last several years.
Microbial conversion of plant biomass into fuels and chemicals offers a practical solution to global concerns over limited natural resources, environmental pollution, and climate change. Pursuant to these goals, researchers have put tremendous efforts and resources towards engineering the yeast Saccharomyces cerevisiae to efficiently convert xylose, the second most abundant sugar in lignocellulosic biomass, into various fuels and chemicals. Here, we summarize recent advances in metabolic engineering of yeast to address bottlenecks on xylose assimilation, and to enable simultaneous co-utilization of xylose and other carbon sources in lignocellulosic hydrolysates. We also highlight distinct characteristics of xylose metabolism that can be harnessed to produce advanced biofuels and chemicals. Although many challenges remain, recent research investments have facilitated efficient fermentation of xylose and simultaneous co-consumption of xylose and glucose. In particular, understanding of xylose-induced metabolic rewiring in engineered yeast has encouraged use of xylose as a carbon source for producing various non-ethanol bioproducts. Furthermore, to boost the lignocellulosic biomass-based bioeconomy, much attention is expected to promote xylose-utilizing efficiency via reprogramming cellular regulatory networks, to attain robust co-fermentation of xylose and other cellulosic carbon sources under industrial conditions, and to exploit the advantageous traits of yeast xylose metabolism for producing diverse fuels and chemicals.
Researchers at Los Alamos National Laboratory have developed SUCCESS, a process to convert uranium metal to uranium tetrachloride enabling production of these valuable compounds for actinide science, molten salt nuclear reactors, and energy research. Conventional methods are problematic due to the oxide reduction processes use rare reagents and generate significant amounts of waste and hydride conversion process using chlorine at high temperature is hazardous. This new method using inexpensive reagents chemically transform uranium metal into uranium halide compounds under mild conditions. The facile conversion to stable uranium tetrachloride could reduce the need to mine uranium for uses other than nuclear, by recycling the uranium without oxidation. The Laboratory is interested in supporting further scale-up efforts using a Cooperative Research and Development Agreement (CRADA) or by granting a license to a qualified entity
Human population growth and accelerated climate change necessitate agricultural improvements using designer crop ideotypes (idealized plants that can grow in niche environments). Diverse and highly skilled research groups must integrate efforts to bridge the gaps needed to achieve international goals toward sustainable agriculture. Given the scale of global agricultural needs and the breadth of multiple types of omics data needed to optimize these efforts, explainable artificial intelligence (AI with a decipherable decision making process that provides a meaningful explanation to humans) and exascale computing (computers that can perform 1018 floating-point operations per second, or exaflops) are crucial. Accurate phenotyping and daily-resolution climatype associations are equally important for refining ideotype production to specific environments at various levels of granularity. In this article, we review advances toward tackling technological hurdles to solve multiple United Nations Sustainable Development Goals and discuss a vision to overcome gaps between research and policy.
With the eve of Exascale computing, performance and portability are at the forefront of all scientific codes. Adding more cores and more energy to a system is no longer a sustainable way to achieve performance, and extra effort must now be made to improve performance in all areas of code and code development. Using hydrodynamic codes as a basis, this work explores numerous techniques to achieve performance in different ways. Adaptive mesh refinement (AMR) is a necessary technique to improve memory optimization in mesh-based simulations. However it is invasive and conventionally difficult to integrate into existing applications, so we present a new branch of AMR to create a smooth transition to these optimizations, which not only improves performance, but also greatly reduces developer effort. We introduce the concept of this improvement as Phantom-Cell AMR, and assess theoretically the improvements, as well as present an application of its use. Other work included involves and investigation into an efficient data structure that ensures optimal memory layout for cache performance, with a target of making codes performant and portable across all architectures. All of the work targets both performance and portability, not just on CPU hardware, but specifically across GPU architectures. Parallel performance is key to all of the methods presented, but the research makes a great effort to improve the portability of all applications to prepare for current high performance computing systems and those on the horizon.
The future of wind power systems deployment is in the form of wind farms comprised of scores of such large turbines, most likely at offshore locations. Individual turbines have grown in span from a few tens of meters to today’s large turbines with rotor diameters that dwarf even the largest commercial aircraft. These massive dynamical systems present unique challenges at scales unparalleled in prior applications of wind science research. Fundamental to this effort is the understanding of the wind turbine wake and its evolution. Furthermore, the optimization of the entire wind farm depends on the evolution of the wakes of different turbines and their interactions within the wind farm. In this article, we use the capabilities of the Common ODE Framework (CODEF) model for the analysis of the effects of wake–rotor and wake-to-wake interactions between two turbines situated in a tandem layout fully and partially aligned with the incoming wind. These experiments were conducted in the context of a research project supported by the National Rotor Testbed (NRT) program of Sandia National Labs (SNL). Results are presented for a layout which emulates the turbine interspace and relative turbine emplacement found at SNL’s Scaled Wind Technologies Facility (SWiFT), located in Lubbock, Texas. The evolution of the twin-wake interaction generates a very rich series of secondary transitions in the vortex structure of the combined wake. These ultimately affect the wake’s axial velocity patterns, altering the position, number, intensity, and shape of localized velocity-deficit zones in the wake’s cross-section. This complex distribution of axial velocity patterns has the capacity to substantially affect the power output, peak loads, fatigue damage, and aeroelastic stability of turbines located in subsequent rows downstream on the farm.
Hydrogen has the potential to play an important role in decarbonizing energy-intensive sectors, and credible and open-source protocols are needed to vet the environmental credibility of a specific production pathway, given the variability even among the same technologies. Stakeholders and markets lack consistent, transparent, technical tools and protocols to assess the carbon intensity of hydrogen production at the asset level. Gas Technology Institute (GTI) and S&P Global Platts have launched the Open Hydrogen Initiative (OHI), a new collaboration focused on brining industry within the hydrogen marketplace together to provide further transparency into the environmental greenhouse gas (GHG) impact of hydrogen production. As part of this initiative, GTI has invited NETL to participate in the effort because of NETL’s deep expertise and capabilities in Life Cycle Analysis. Therefore, NETL is joining forces with the Gas Technology Institute (GTI) to build a measurement tool, accompanying protocols, and confidence score of the quality of the measurement. This CRADA effort provides the technical research collaboration support with GTI that will be used in the broader Open Hydrogen Initiative that can provide stakeholders the technical tools and protocols to decarbonize energyintensive sectors.
Femto-second (fs) lasers offer state-of-the art non-intrusive diagnostic capabilities in plasma flow environments. The diagnostics in plasma flows using a fs-laser that are relevant to this proposal include Two-photon Absorption Laser Induced Fluorescence (TALIF) for measuring the number density of an atomic species, and Femto-second Laser Electronic Excitation Tagging (FLEET) for performing velocity measurements in plasma flows. Various aspects of these diagnostics using fs-lasers are still an active domain of research. The TALIF technique has been used to measure the number density of atomic species in various plasma flow environments like atmospheric pressure plasma jet (APPJ) and arc-jet flows. This technique utilizes a fs-laser to excite an atomic species and the subsequent relaxation process through fluorescence is recorded to make measurements. A narrow band-pass filter is used to isolate the fluorescence signal that is in the infra-red regime. However, broad-band background emissions can potentially pass through the filter and induce noise in the TALIF signal. Particularly, arc-jet plasma flows inherently contain background emissions. In such plasma flow environments, in addition to the filter, the background-subtraction technique will play an important role in reducing the noise that is induced in the number density measurements using TALIF. The proposed research focuses on evaluating the effectiveness of a background-subtraction technique by measuring atomic oxygen using a femtosecond (fs) TALIF in APPJ with induced background emissions and comparing measurements obtained without inducing the emissions. Therefore, by quantifying the changes in the signal-to-noise ratio caused by a background-subtraction technique applied for APPJ, it will enable a better understanding of the uncertainties in the background subtracted fs-TALIF measurements for arc-jet plasma flows. In addition to this, FLEET will be used to obtain velocity in the APPJ, and the impact of background emissions will be analyzed. Under this effort, the experimenters and research personnel will gain the technical know-how in the operational and theoretical aspects of the advanced fs-laser-based diagnostic techniques. The knowledge gained in this process will then also directly be applied to a separate ONR funded project at the Aerodynamics Research Center (ARC), UT Arlington, related to arc-jet flow characterization.
This presentation begins by providing an overview of the research and discusses recent efforts in validating thermal neutron scattering cross sections, including differential cross section measurements at ORNL for evaluation and validation, total cross section measurements at RPI, pulsed-neutron die-away experiments at LLNL, and integral criticality experiments by LLNL. The presentation also proposes a methodology not only for validating thermal scattering files that utilizes all available experimental data, but also for evaluating new libraries as demonstrated on polystyrene. In conclusion, polystyrene evaluation was conducted using multiple experiments, including differential and integral. The proposed methodology has been shown to improve neutron transport and files have been submitted to NNDC for inclusion in ENDF/B-VIII.1 release. MCT-012 & PCM-002 has been relatively insensitive to changes in $\mathcal{S}(α,β)$. The research found that differences in differential results don’t always propagate to differences in integral results, hence why evaluation of differential data important. Future work on the project is also discussed.
The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein–ligand docking have recently been introduced, so far no previous works have systematically studied the behaviour of the latest docking and structure prediction methods within the broadly applicable context of: (1) using predicted (apo) protein structures for docking (for example, for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (for example, for enzyme design); and (3) having no previous knowledge of binding pockets (for example, for generalization to unknown pockets). To enable a deeper understanding of the real-world utility of docking methods, we introduce PoseBench, a comprehensive benchmark for broadly applicable protein–ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein–ligand docking and protein–ligand structure prediction using both primary ligand and multiligand benchmark datasets, the latter of which we introduce to the DL community. Empirically, using PoseBench, we find that: (1) DL cofolding methods generally outperform comparable conventional and DL docking baseline algorithms, but popular methods such as AlphaFold 3 are still challenged by prediction targets with new protein–ligand binding poses; (2) certain DL cofolding methods are highly sensitive to their input multiple sequence alignments, whereas others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting new or multiligand protein targets.
Lithium/sodium metal batteries (LMBs/SMBs) possess immense potential for various applications due to their high energy density. Nevertheless, LMBs/SMBs are highly susceptible to the detrimental effects of an unstable solid electrolyte interphase (SEI) and dendrites during practical applications, particularly pronounced in low-temperature environments. Furthermore, sluggish ion transportation further compromises the cycling stability of LMBs/SMBs at low temperatures. To achieve stable operation of LMBs/SMBs at low temperatures, researchers have made numerous efforts including electrolyte optimization aimed at creating stable SEIs and suppressing the metal dendrites under low temperature conditions. Despite the significant advancements made recently in the liquid electrolyte design, there remains considerable hurdle in electrolyte engineering for practical low-temperature, high energy density LMBs/SMBs, calling for a profound comprehension of the intricate interplay between the electrochemical reaction kinetics and electrolyte compositions. Here, this review provides a thorough overview of various strategies in optimizing liquid electrolytes covering weakly solvating electrolytes, concentration-designed electrolytes, and solvation structure-designed electrolytes, to address the challenges faced by LMBs/SMBs at low temperatures, including slow reaction kinetics and the difficulties in Li + /Na + solvation/desolvation. Furthermore, this review discusses future prospects for the advancement of this field, intending to provide valuable insights and support for subsequent research undertakings.
As of 2021, the biothreat policy and research communities organize their efforts around lists of priority agents, which elides consideration of novel pathogens and biotoxins. For example, the Select Agents and Toxins list is composed of agents that historic biological warfare programs had weaponized or that have previously caused great harm during natural outbreaks. Similarly, lists of priority agents promulgated by the World Health Organization and the National Institute of Allergy and Infectious Diseases are composed of previously known pathogens and biotoxins. To fill this gap, we argue that the research/scientific and biodefense/biosecurity communities should categorize agents based on how they impact their hosts to augment current list-based paradigms. Specifically, we propose integrating the results of multi-omics studies to identify bioagent-agnostic signatures (BASs) of disease—namely, patterns of biomarkers that accurately and reproducibly predict the impacts of infection or intoxication without prior knowledge of the causative agent. Here, we highlight three pathways that investigators might exploit as sources of signals to construct BASs and their applicability to this framework. The research community will need to forge robust interdisciplinary teams to surmount substantial experimental, technical, and data analytic challenges that stand in the way of our long-term vision. However, if successful, our functionality-based BAS model could present a means to more effectively surveil for and treat known and novel agents alike.
Abstract Magnetic skyrmions are topologically wound nanoscale textures of spins whose ambient stability and electrical manipulation in multilayer films have led to an explosion of research activities. While past efforts focused predominantly on isolated skyrmions, recently ensembles of chiral spin textures, consisting of skyrmions and magnetic stripes, are shown to possess rich interactions with potential for device applications. However, several fundamental aspects of chiral spin texture phenomenology remain to be elucidated, including their domain wall (DW) structure, thermodynamic stability, and morphological transitions. Here the evolution of these textural characteristics are unveiled on a tunable multilayer platform—wherein chiral interactions governing spin texture energetics can be widely varied—using a combination of full‐field electron and soft X‐ray microscopies with numerical simulations. With increasing chiral interactions, the emergence of Néel helicity, followed by a marked reduction in domain compressibility, and finally a transformation in the skyrmion formation mechanism are demonstrated. Together with an analytical model, these experiments establish a comprehensive microscopic framework for investigating and tailoring chiral spin texture character in multilayer films.
Abstract Over the past decades, superconducting qubits have emerged as one of the leading hardware platforms for realizing a quantum processor. Consequently, researchers have made significant effort to understand the loss channels that limit the coherence times of superconducting qubits. A major source of loss has been attributed to two level systems that are present at the material interfaces. It is recently shown that replacing the metal in the capacitor of a transmon with tantalum yields record relaxation and coherence times for superconducting qubits, motivating a detailed study of the tantalum surface. In this work, the chemical profile of the surface of tantalum films grown on c‐plane sapphire using variable energy X‐ray photoelectron spectroscopy (VEXPS) is studied. The different oxidation states of tantalum that are present in the native oxide resulting from exposure to air are identified, and their distribution through the depth of the film is measured. Furthermore, it is shown how the volume and depth distribution of these tantalum oxidation states can be altered by various chemical treatments. Correlating these measurements with detailed measurements of quantum devices may elucidate the underlying microscopic sources of loss.
Lithium metal batteries (LMBs) enable much higher energy density than lithium–ion batteries (LIBs) and thus hold great promise for future transportation electrification. However, the adoption of lithium metal (Li) as an anode poses serious concerns about cell safety and performance, which has been hindering LMBs from commercialization. To this end, extensive effort has been invested in understanding the underlying mechanisms theoretically and experimentally and developing technical solutions. In this review, we devote to providing a comprehensive review of the challenges, characterizations, and interfacial engineering of Li anodes in both liquid and solid LMBs. Finally, we expect that this work will stimulate new efforts and help peer researchers find new solutions for the commercialization of LMBs.