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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Making the most of your electrons: Challenges and opportunities in characterizing hybrid interfaces with STEM

Inspired by the unique architectures composed of hard and soft materials in natural and biological systems, synthetic hybrid structures and associated hard-soft interfaces have recently evoked significant interest. Soft matter is typically dominated by structural fluctuations even at room temperature, while hard matter is governed by rigid mechanical behavior. This dichotomy offers considerable opportunities to leverage the disparate properties offered by these components across a wide spectrum spanning from basic science to engineering insights with significant technological overtones. Such hybrid structures, which include polymer nanocomposites, DNA functionalized nanoparticle superlattices, and metal organic frameworks to name a few, have delivered promising insights into the technologically relevant applications such as catalysis, environmental remediation, optoelectronics, and medicine. The interfacial structure between the hard and soft phases demonstrates features across a variety of length scales and often strongly influence the functionality of hybrid systems. While scanning/transmission electron microscopy (S/TEM) has proven to be a valuable tool for acquiring intricate molecular and nanoscale details of these interfaces, the unusual nature of hybrid composites presents a suite of challenges that make assessing or establishing structure–property relationships especially difficult. There are additional considerations at all stages of sample analysis from preparing electron-transparent samples to obtaining sufficient contrast to resolve the interface between dissimilar materials given the dose sensitivity of soft materials. Here, we discuss each of these challenges and supplement a review of recent developments in the field with additional experimental investigations and simulations to present solutions for attaining a nano or molecular-level understanding of these interfaces. These solutions present a host of opportunities for investigating the role interfaces play in this unique class of functional materials.

36 MATERIALS SCIENCE↗
NASA NTRS2013

SIM_EXPLORE: Software for Directed Exploration of Complex Systems

Physics-based numerical simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. While such codes may provide the highest- fidelity representation of system behavior, they are often so slow to run that insight into the system is limited. Trying to understand the effects of inputs on outputs by conducting an exhaustive grid-based sweep over the input parameter space is simply too time-consuming. An alternative approach called "directed exploration" has been developed to harvest information from numerical simulators more efficiently. The basic idea is to employ active learning and supervised machine learning to choose cleverly at each step which simulation trials to run next based on the results of previous trials. SIM_EXPLORE is a new computer program that uses directed exploration to explore efficiently complex systems represented by numerical simulations. The software sequentially identifies and runs simulation trials that it believes will be most informative given the results of previous trials. The results of new trials are incorporated into the software's model of the system behavior. The updated model is then used to pick the next round of new trials. This process, implemented as a closed-loop system wrapped around existing simulation code, provides a means to improve the speed and efficiency with which a set of simulations can yield scientifically useful results. The software focuses on the case in which the feedback from the simulation trials is binary-valued, i.e., the learner is only informed of the success or failure of the simulation trial to produce a desired output. The software offers a number of choices for the supervised learning algorithm (the method used to model the system behavior given the results so far) and a number of choices for the active learning strategy (the method used to choose which new simulation trials to run given the current behavior model). The software also makes use of the LEGION distributed computing framework to leverage the power of a set of compute nodes. The approach has been demonstrated on a planetary science application in which numerical simulations are used to study the formation of asteroid families.

Burl, Michael↗
DOE OSTI2025

Multiplexing Focusing Analyzer for Efficient Stress-Strain Measurements

Statement of the problem or situation that is being addressed. Although thermal and cold neutron scattering is widely used and is critical for success in many areas of materials science and engineering, relatively low neutron fluxes severely limit applications of not only laboratory neutrons generators, but also large national neutron facilities. State-of-the-art thermal and cold neutron sources are large expensive national facilities, which serve diverse community of scientific and industrial users. The constant need to improve the instruments performance, stems from the fact that neutron methods are gaining in popularity, and becoming more and more powerful, while new neutron sources are not being constructed to keep pace with the developments and needs of the scientific community. Small research reactors at universities and National Labs, and laboratory-based neutron generators, are necessary not only for education and training, but also when samples cannot be transported to other facilities. However, the standard neutron techniques, which were developed for high-flux facilities, require much higher efficiencies to be used effectively with the low fluxes of small sources. Thus, the efficient use of neutron sources, such as with our proposed analyzer, is important for the progress and broader use of these neutron techniques. General statement of how this problem is being addressed. We propose to design and demonstrate novel diffractive optical device, which will enable very efficient residual stress neutron diffractometers. The proposed device will be a multi-foil analyzer, where each foil is constructed of focusing bent single crystals of Si. Such device will enable polychromatic residual stress neutron diffraction. At large national facilities, such as at Oak Ridge National Laboratory, these analyzers would enable very fast measurements for determining residual stress tensors, raster large samples or screen multiple samples. Commercial Applications and Other Benefits The outcome of this project would be the demonstration of commercial devices, novel neutron optical components, which could be utilized to improve the performance of existing instruments or build novel neutron scattering instruments at DOE neutron facilities and commercial laboratory neutron sources. These new devices will widen the scope of research conducted using neutrons and enable measurements not feasible at present. Summary for Members of Congress Thermal and cold neutron beams are a powerful materials science probe, which provide unique information about the structure of matter. The proposed innovations expand the reach of neutron-based investigations to new materials and industries by enabling new instrumentation capabilities, thereby greatly enhancing and expanding the role of small, laboratory-based neutron instrumentation, and improving education and training of neutron users.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Multiscale embedded printing of engineered human tissue and organ equivalents

Creating tissue and organ equivalents with intricate architectures and multiscale functional feature sizes is the first step toward the reconstruction of transplantable human tissues and organs. Existing embedded ink writing approaches are limited by achievable feature sizes ranging from hundreds of microns to tens of millimeters, which hinders their ability to accurately duplicate structures found in various human tissues and organs. In this study, a multiscale embedded printing (MSEP) strategy is developed, in which a stimuli-responsive yield-stress fluid is applied to facilitate the printing process. A dynamic layer height control method is developed to print the cornea with a smooth surface on the order of microns, which can effectively overcome the layered morphology in conventional extrusion-based three-dimensional bioprinting methods. Since the support bath is sensitive to temperature change, it can be easily removed after printing by tuning the ambient temperature, which facilitates the fabrication of human eyeballs with optic nerves and aortic heart valves with overhanging leaflets on the order of a few millimeters. The thermosensitivity of the support bath also enables the reconstruction of the full-scale human heart on the order of tens of centimeters by on-demand adding support bath materials during printing. Here, the proposed MSEP demonstrates broader printable functional feature sizes ranging from microns to centimeters, providing a viable and reliable technical solution for tissue and organ printing in the future.

3D bioprinting↗
DOE OSTI2023

In-Situ Applied Coatings for Mitigating Gas Hydrate Deposition in Deepwater Operations (Final Scientific/Technical Report)

The Project objectives of this work were focused on the need to develop, investigate, and validate for field and commercial deployment robust pipeline surface treatments to prevent / minimize hydrate and other flow assurance solids deposition in subsea oil flowlines. A robust pipeline treatment that can prevent hydrate and wax/asphaltene adhesion/deposition will be a major fundamental breakthrough in hydrate and flow assurance science and engineering, and to critical Deepwater field operations. The project addressed the current major outstanding issue of mitigating gas hydrate and other solids deposition in different pipeline conditions, a major outstanding issue that has not been solved to-date, despite previous attempts/studies. The major goal of this project was to develop and investigate for deployment robust pipeline coatings to mitigate gas hydrate deposition in Deepwater oil flowlines, which will be critical in offshore leak and spill prevention. As oil and gas production wells mature, the water content and oil composition can change and increase the risk of gas hydrate, wax, and asphaltene deposition and blockages in flowlines, with the cost of complete inhibition becoming prohibitive. The ability to mitigate flow assurance risks in maturing flowlines can extend the life of the field, significantly reduce operational costs from inhibitor injection and prevent potential safety hazards to personnel and equipment and can prevent potential environmental hazards leading to catastrophic hydrocarbon emissions. This research allows for a novel, cost-effective method of mitigating gas hydrate and other pipeline solids deposition/blockages in flowlines, extending the life of the field while being minimally disruptive to normal flowline operation. The omniphobic surface treatment developed in this work can significantly improve the economics of energy transport by providing flow assurance and limiting catastrophic blowouts.

02 PETROLEUM↗
DOE OSTI2023

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Selective laser melting of oxide dispersion strengthened MA956 alloy and its surface finishing by magnetic field assisted finishing

Selective laser melting (SLM) is a prominent metal additive manufacturing (AM) capable of processing a myriad of engineering materials with high precision and design freedom. However, similar to other AM processes, poor surface finishing has been an omnipresent problem in SLM technology. In this study, magnetic field assisted finishing (MAF) was used to finish SLM fabricated oxide dispersion strengthened (ODS) MA956, an iron-chromium-aluminum alloy. The effect of laser processing parameters on part density and surface roughness was first studied. Using a multi-objective optimization technique, the optimal parameters to obtain the highest density and lowest surface roughness were determined. Finally, MAF was applied to the parts built with the obtained optimal SLM parameters, but without significant improvement on the surfaces of as-printed samples. Hence, the surfaces of as-printed samples were post-processed (ground) to yield better initial surface conditions prior to MAF. The effect of initial roughness, iron particles size, and abrasive size on MAF performance was studied. Initial roughness had the most dominant effect followed by abrasive size. The underlying mechanism behind the dependency on initial roughness on final surface quality was analyzed by studying the change on the surface profiles with different starting initial roughness. The initial roughness required for MAF to be effective was determined. In conclusion, using the optimal processing conditions, MAF was applied to the post-processed samples to attain the final average surface roughness (R a ) as little as 0.36 μm starting from initial average roughness (R a ) of 1.53 μm.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Recent H - ion source research and development at the Oak Ridge National Laboratory

The U.S. Spallation Neutron Source (SNS) is a state-of-the-art neutron scattering facility delivering the world's most intense pulsed neutron beams to a wide array of instruments which are used to conduct investigations in many fields of science and engineering. Neutrons are produced from spallation of liquid Hg by bombardment of short (~1 μs), intense (~35 A) pulses of protons delivered at 60 Hz by a storage ring which is fed by a high-intensity, ~1 GeV H - LINAC. This facility has operated almost continuously since 2006, with ion source performance increasing over those years, and currently providing 50–60 mA of H - ions with a duty-factor of 6% for maintenance-free runs of several months with near 100% availability. Ion source research and development at ORNL has played a key role in enabling and supporting this success: this report provides an update on some of the ongoing ion source research and development efforts which have been undertaken since the previous Negative Ion Beams and Sources (NIBS) conference in 2020. These include significant improvements to H - beam current by extraction from a larger source outlet aperture and improvements to the electron dumping system which should eliminate the gradual loss of electrode voltage over the course of a run which has occasionally impacted SNS operations. Improvement and simplification of the plasma ignition system for the external antenna ion source, a long-standing problem, was also realized. Lastly, RF coupling efficiency was measured for both the SNS internal and external antenna ion sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2015

Robotic Reconnaissance Missions to Small Bodies and Their Potential Contributions to Human Exploration

Introduction: Robotic reconnaissance missions to small bodies will directly address aspects of NASA's Asteroid Initiative and will contribute to future human exploration. The NASA Asteroid Initiative is comprised of two major components: the Grand Challenge and the Asteroid Mission. The first component, the Grand Challenge, focuses on protecting Earth's population from asteroid impacts by detecting potentially hazardous objects with enough warning time to either prevent them from impacting the planet, or to implement civil defense procedures. The Asteroid Mission involves sending astronauts to study and sample a near- Earth asteroid (NEA) prior to conducting exploration missions of the Martian system, which includes Phobos and Deimos. The science and technical data obtained from robotic precursor missions that investigate the surface and interior physical characteristics of an object will help identify the pertinent physical properties that will maximize operational efficiency and reduce mission risk for both robotic assets and crew operating in close proximity to, or at the surface of, a small body. These data will help fill crucial strategic knowledge gaps (SKGs) concerning asteroid physical characteristics that are relevant for human exploration considerations at similar small body destinations. Small Body Strategic Knowledge Gaps: For the past several years NASA has been interested in identifying the key SKGs related to future human destinations. These SKGs highlight the various unknowns and/or data gaps of targets that the science and engineering communities would like to have filled in prior to committing crews to explore the Solar System. An action team from the Small Bodies Assessment Group (SBAG) was formed specifically to identify the small body SKGs under the direction of the Human Exploration and Operations Missions Directorate (HEOMD), given NASA's recent interest in NEAs and the Martian moons as potential human destinations [1]. The action team organized the SKGs into four broad themes: 1) Identify human mission targets; 2) Understand how to work on and interact with the small body surface; 3) Understand the small body environment and its potential risk/benefit to crew, systems, and operational assets; and 4) Understand the small body resource potential. Each of these themes were then further subdivided into categories to address specific SKG issues. Robotic Precursor Contributions to SKGs: Robotic reconnaissance missions should be able to address specific aspects related to SKG themes 1 through 4. Theme 1 deals with the identification of human mission targets within the NEA population. The current guideline indicates that human missions to fastspinning, tumbling, or binary asteroids may be too risky to conduct successfully from an operational perspective. However, no spacecraft mission has been to any of these types of NEAs before. Theme 2 addresses the concerns about interacting on the small body surface under microgravity conditions, and how the surface and/or sub-surface properties affect or restrict the interaction for human exploration. The combination of remote sensing instruments and in situ payloads will provide good insight into the asteroid's surface and subsurface properties. SKG theme 3 deals with the environment in and around the small body that may present a nuisance or hazard to any assets operating in close proximity. Impact and surface experiments will help address issues related to particle size, particle longevity, internal structure, and the near-surface mechanical stability of the asteroid. Understanding or constraining these physical characteristics are important for mission planning. Theme 4 addresses the resource potential of the small body. This is a particularly important aspect of human exploration since the identification and utilization of resources is a key aspect for deep space mission architectures to the Martian system (i.e., Phobos and Deimos). Conclusions: Robotic reconnaissance of small bodies can provide a wealth of information relevant to the science and planetary defense of NEAs. However, such missions to investigate NEAs can also provide key insights into small body strategic knowledge gaps and contribute to the overall success for human exploration missions to asteroids.

Abell, P. A.↗
NASA NTRS2018

Coupled Thermomechanical Micromechanics Modeling of the Influence of Thermally Grown Oxide Layer in an Environmental Barrier Coating System

Environmental Barrier Coatings (EBCs) have emerged as a promising means of protecting critical components for high temperature applications (e.g., aircraft engines). EBCs are often used to protect an underlying material (substrate) from extreme thermal/chemical environments. However, systems that utilize EBCs are susceptible to a number of failure modes including oxidation/delamination. Environmental Barrier Coatings (EBCs) have emerged as a promising means of protecting silicon based ceramic matrix composite (CMC) components for high temperature applications (e.g., aircraft engines). EBCs are often used to protect an underlying material (substrate) such as silicon carbide from extreme thermal/chemical environments. In a typical CMC/EBC system, an EBC may or may not be adhered to an underlying substrate with a bond coat (e.g., silicon). Irrespective, systems that utilize EBCs are susceptible to a number of failure modes including oxidation/delamination, recession, chemical attack and dissolution, thermo-mechanical degradation, erosion, and foreign object damage. Current work at NASA Glenn Research Center is aimed at addressing these failure modes in EBC systems and developing robust analysis tools to aid in the design process. The Higher-Order Theory for Functionally Graded Materials (HOTFGM), a precursor to the High-Fidelity Generalized Method of Cells micromechanics approach, was developed to investigate the coupled thermo-mechanical behavior of functionally graded composites and will be used herein to assess the development and growth of a low-stiffness thermally grown oxide (TGO) layer in EBC/CMC systems without a silicon bond coat. To accomplish this a sensitivity study is conducted to examine the influence of uniformly and non-uniformly grown oxide layer on the associated driving forces leading to mechanical failure (spallation) of EBC layer when subjected to isothermal loading, recession, chemical attack and dissolution, thermomechanical degradation, erosion, and foreign object damage. Current work at NASA Glenn Research Center is aimed at addressing these failure modes in EBC systems and developing robust analysis tools to aid in the design process. The Higher-Order Theory for Functionally Graded Materials (HOTFGM), a precursor to the High-Fidelity Generalized Method of Cells micromechanics approach, was developed to investigate the coupled thermo-mechanical behavior of functionally graded composites (Aboudi et al., 1999, Composites B). For example, HOTFGM was previously used (Arnold et al, 1995, NASA CP 10178, paper 34), to assess interlaminar stresses (including free edge effects) in a substrate with a thermal barrier coating (TBC). In this study, HOTFGM micromechanics analyses will be used to assess the development and growth of a low-stiffness thermally grown oxide (TGO) layer between a silicon carbide substrate and a ytterbium disilicate EBC. In order to realistically simulate TGO growth, an evolution law will be incorporated into HOTFGM. In addition, the effect of TGO roughness will be explored consistent with previous TBC work (Pindera et al., 2000. Material Science and Engineering, A284, pp. 158-175). This model represents a first step in developing a robust analysis tool that can ultimately be used to design durable EBC systems. Additional failure modes will be considered as part of a future work.

environmental barrier coating↗
DOE OSTI2023

Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels

Abstract A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP’s analytical tractability, robustness, and natural inclusion of uncertainty quantification. Unfortunately, the use of exact GPs is prohibitively expensive for large datasets due to their unfavorable numerical complexity of $$O(N^3)$$ O ( N 3 ) in computation and $$O(N^2)$$ O ( N 2 ) in storage. All existing methods addressing this issue utilize some form of approximation—usually considering subsets of the full dataset or finding representative pseudo-points that render the covariance matrix well-structured and sparse. These approximate methods can lead to inaccuracies in function approximations and often limit the user’s flexibility in designing expressive kernels. Instead of inducing sparsity via data-point geometry and structure, we propose to take advantage of naturally-occurring sparsity by allowing the kernel to discover—instead of induce—sparse structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel definitions that provide enough flexibility to learn and encode not only non-zero but also zero covariances. This principle of ultra-flexible, compactly-supported, and non-stationary kernels, combined with HPC and constrained optimization, lets us scale exact GPs well beyond 5 million data points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Annealing temperature variation and its influence on the self-cleaning properties of TiO 2 thin films

Titanium dioxide (TiO 2 ) is an important material in science and engineering because of its basic and synthetic properties. Nevertheless, there is a dearth of reports in the open literature focusing on its ability to self-clean under temperature changes. In this study, we used the spin coating technique to produce TiO 2 thin films to evaluate its self-cleaning ability after annealing at different temperatures. The TiO 2 sol was obtained through an endothermal sol-gel process, and the gel was coated on a glass substrate using a spin coater. The deposited films were then annealed at 400 °C, 600 °C, and 800 °C for 1 h. The influence of annealing temperature variation on the self-cleaning properties of the thin film was characterized using X-ray diffraction, scanning electron microscope; Fourier transformed infrared spectrometric analysis and UV-vis spectrophotometer. A test to ascertain self-cleaning was conducted using the degradation of methylene blue, and the different films were tested for durability. The durability test confirmed the connection between solid coating and substrate at all annealing temperatures. Thin films annealed at 600 °C revealed the best self-cleaning properties. The morphological analysis revealed snowflake shapes uniformly distributed over the substrate at 400 °C, and agglomeration improved as the annealing temperature increased. Structural analysis showed an increase in crystallinity with an increase in annealing temperature for both rutile and anatase phases. At three different temperatures, the chemical bond and the absorption band pattern followed the same path, although the peak intensity declined with temperature rise. Finally, the optical bandgap of the thin coated TiO 2 declined from 3.39 eV to 3.20 eV as the binding temperature increased from 400 to 800 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Elucidating hydrogen isotope transport mechanisms in proton-conducting ceramics with trapping effects using TMAP8

Hydrogen isotopes play an central role in many science and engineering applications such as fuel cells, hydrogen production, and fusion energy. For these applications, hydrogen separation and extraction applications are pivotal aspects of hydrogen transports, where proton-conducting ceramics (PCCs) have shown great potential. In this study, we propose a new model for hydrogen isotope transport in PCC materials, BaZr 0.9 Y 0.1 O 2.95 (BZY) in particular, which captures behavior in both dry and wet environments. The model expands previous efforts and considers diffusion, trapping, and surface reactions (i.e., dissociation and recombination). We then validate and calibrate the model using deuterium transport measurements from experiments in both dry and wet environments. This study highlights the key role of trapping, often neglected, on hydrogen isotope transport in BZY and other PCC materials. It also explains how the commonly observed discrepancy between dry and wet behavior can be attributed to more active surface reactions and saturated traps due to the increased hydrogen presence under the wet environment. These results provide insights to optimize PCC manufacturing and usage as a hydrogen separation and extraction technology in various fields, emphasizing that lowering the trapping can reduce hydrogen isotope retention. These modeling and calibration efforts are performed using the tritium migration analysis program, version 8 (TMAP8), an open-source application designed for hydrogen isotope transport.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

The ATTO-Campina site: A new observatory for tropical convection and gas-aerosol-cloud-precipitation interactions in the Amazon

We present results from ATTO-Campina, a new permanent observational site in central Amazon, about 4 km from the ATTO towers. Operational since 2020, ATTO-Campina characterizes atmospheric, cloud and rainfall properties through remote sensing. The goal is to provide continuous, complementary measurements to the ATTO towers, addressing the rainforest’s complex gas-aerosol-cloud-precipitation dynamics. Using a 3.5-year dataset, we classified convective clouds into three types: shallow cumulus (ShCu), congestus (Con) or (Deep) clouds. The shallow-to-deep transition takes about three hours, starting with ShCu formation at 11:00 local time. The accumulated rainfall peak follows at about 16:00. Only weak downdrafts are present in the upper troposphere where previous studies indicate new particle formation (NPF) occurrence. Strong downdrafts are mostly limited to heights below 5 km. Con and Deep convective days have higher concentrations of ultrafine aerosol and lower concentrations of accumulation-mode particles compared to ShCu. Convective clouds also significantly modify gas mixing ratios. Deep convective clouds are associated with high near-surface O3, consistent with downward transport from the midtroposphere. Our results showcase the added detail achieved by integrating data from the ATTO towers and ATTO-Campina sites. Together, these sites support better understanding of interconnected gas-aerosol-cloud-precipitation processes in the Amazon and their evolution under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Solving Large‐Scale Linear Systems of Equations by a Quantum Hybrid Algorithm

Abstract Today's intermediate‐scale quantum computers, although imperfect, already perform computational tasks that are manifestly beyond the capabilities of modern classical supercomputers. However, so far, quantum‐enabled large‐scale solutions have been realized only for limited set of problems. Here a hybrid algorithm based on phase estimation and classical optimization of the circuit width and depth is employed for solving a specific class of large linear systems of equations ubiquitous to many areas of science and engineering. A classification of linear systems based on the entanglement properties of the associated phase‐estimation unitary operation is introduced, enabling a highly efficient search for solutions that is facilitated by a straightforward matrix‐to‐circuit map. A 2 17 ‐dimensional problem is implemented on several IBM quantum computer superconducting quantum processors, a record‐breaking result for a linear system solved by a quantum computer. Demonstrated realisation sets a clear benchmark in the quest for the future quantum speedup in the linear systems of equations solution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Cobalt–Nickel Exchange in Exfoliated Battery Layered Cathode Sheets with Application to Recycling

With the emerging dominance of electric vehicles (EV) in the transportation sector, recycling or upcycling spent battery materials will be required to reduce EV costs, lessen waste, and ease critical material supply chain issues for EV batteries. Here, motivated by work in the literature describing the exfoliation of layered oxides, first‐principles calculations are performed to show that Li x CoO 2 , if exfoliated into nanosheets, can readily undergo transition metal cation exchange in aqueous media. The substitution of Co 3+ or Co 4+ cations inside the sheet by Ni 2+ is associated with modest reaction barriers (Δ G* ≈ 0.3–0.7 eV) and is at most mildly endothermic (Δ G ≈ 0.2–0.3 eV). In contrast, previous battery degradation studies have shown that Co 3+ diffusion is strongly inhibited inside bulk layered oxides. This suggests that processing spent layered oxides as nanosheets can provide a potentially low‐energy‐cost pathway to altering the transition metal and/or dopant stoichiometry, which can be used toward developing new room‐temperature upcycling routes for cathodes from end‐of‐life batteries.

battery recycling↗
DOE OSTI2025

Biobased Poly(dodecylene Furanoate) with Inherent Advantages in Performance and Circularity

Biobased polymers are gaining traction toward more sustainable flexible-film packaging, yet overcoming trade-offs between their performance properties and end-of-life (EoL) options still remains a challenge. Here, it is shown that biobased poly(dodecylene 2,5-furanoate) (PDDF), synthesized via both step-growth polycondensation and chain-growth ring-opening polymerization methods, exhibits advantages not only in gas barrier properties but also in EoL options due to its biodegradability and closed-loop chemical circularity. Specifically, PDDF displays significantly lower oxygen and carbon dioxide permeability than commercial poly(butylene adipate-co-terephthalate) (PBAT) and linear low-density polyethylene , alongside a markedly higher modulus (by ≈3 ×) and reduced water vapor transmission rate compared to PBAT. This superior performance is attributed to the inherently rigid, polar, H-bonding furan rings that enhance chain interaction, packing and crystallinity and thus reduce free volume impeding gas diffusion, while the long hydrophobic dodecylene segments inhibit water permeation. Furthermore, PDDF can be recycled back to its cyclic monomer by base-catalyzed depolymerization or diester and diol monomers by simple methanolysis. These superior barrier properties, coupled with biodegradation and closed-loop circularity, highlight the potential of the biobased PDDF as a more sustainable alternative for packaging.

Poly(dodecylene 2,5-furanoate) (PDDF)↗