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DOE OSTI2024

Chain-length-controllable upcycling of polyolefins to sulfate detergents

Escalating global plastic pollution and the depletion of fossil-based resources underscore the urgent need for innovative end-of-life plastic management strategies in the context of a circular economy. Thermolysis is capable of upcycling end-of-life plastics to intermediate molecules suitable for downstream conversion to eventually high-value chemicals, but tuning the molar mass distribution of the products is challenging. Here, in this study, we report a temperature-gradient thermolysis strategy for the conversion of polyethylene and polypropylene into hydrocarbons with tunable molar mass distributions. The whole thermolysis process is catalyst- and hydrogen-free. The thermolysis of polyethylene and polyethylene/polypropylene mixtures with tailored temperature gradients generated oil with an average chain length of ~C 14 . The oil featured a high concentration of synthetically useful α-olefins. Computational fluid dynamics simulations revealed that regulating the reactor wall temperature was the key to tuning the hydrocarbon distributions. Subsequent oxidation of the obtained α-olefins by sulfuric acid and neutralization by potassium hydroxide afforded sulfate detergents with excellent foaming behaviour and emulsifying capacity and low critical micelle concentration. Overall, this work provides a viable approach to producing value-added chemicals from end-of-life plastics, improving the circularity of the anthropogenic carbon cycle.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Observed and Imputed Volumetric Soil Water Content Timeseries for the New Mexico Elevation Gradient

Reliable soil water content (SWC) data are essential for understanding dryland ecosystem dynamics, but high-frequency SWC sensors often fail, creating gaps in critical datasets. To address this, we developed a Bayesian mixture model that imputes missing SWC using both linear interpolation and an ecosystem water balance model (SOILWAT2), tested across six AmeriFlux eddy covariance tower sites in the New Mexico Elevation Gradient, demonstrating its effectiveness in reconstructing SWC patterns while providing insights into the factors driving SWC variability. Daily volumetric soil water content (SWC) data are provided as csv-formatted spreadsheets for the six AmeriFlux sites (US-Seg, US-Ses, US-Wjs, US-Mpi, US-Vcp, and US-Vcs). For each site there is an observed SWC file (site_SWC_gapfill.csv) and a file that contains imputed SWC (imputed_SWC_site.csv). The observed SWC files contain temperature corrected sensor values, tower precipitation data, as well as outputs from SOILWAT2 simulations that were used to impute SWC. The imputed files contain the original observed SWC values and the imputed missing SWC values. When SWC was missing from the original data, the missing value was imputed based on the Bayesian imputation mixture model. The posterior mean of all imputed values is reported as "mean_X". When the observed SWC was NOT missing, mean_X = observed SWC value (original data). The standard deviation, 2.5th percentile and the 97.5th percentile for the imputed values are also reported in the imputed files. There are readme text files for each file type explaining the contents of each column.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Multi-GeV FFA beam transport test at CEBAF

Jefferson National Lab plans an upgrade project to reach 22 GeV high polarization electron beam by using Fixed Field Alternating-gradient (FFA) magnets. The utilization of the FFA magnets for 10-22 GeV beam energy range is unexampled, therefore those magnets need an experimental validation before their full installation to form an arc in the Continuous Electron Beam Accelerator Facility (CEBAF). For this reason, JLAB is also considering the design of an FFA magnet test bench, i.e. a half or full FFA cell, that would be deployed in the current CEBAF in order to serve as the highest energy demonstration for the FFA field uniformity, permanent magnet resiliency with the beam as well as enabling beam optics measurements with the 5-11 GeV range highly polarized beams which closely resembles the full energy range of the 22 GeV upgrade. In this report, we present the status of the planned beamline for the FFA beam transport test at CEBAF.

Accelerator Physics↗

DOE OSTI2025

Cathode Upcycling for Direct Recycling of Lithium‐Ion Batteries Using a Precipitation Approach

With the increased production of electric vehicles to reduce carbon emissions, the lithium-ion battery market to supply those vehicles has grown dramatically. To enhance battery sustainability and circularity, direct recycling methods aim to recover intact cathode materials. However, end-of-life cathode materials are typically 15–20 years old and often have lower energy density compared to current cathode materials. To address this challenge, a rapid precipitation process is developed to boost energy density by converting low Ni-compositions, LiNi 0.33 Co 0.33 Mn 0.33 O 2 (NMC111), into higher Ni-compositions (NMC622). This process forms a Ni-rich coating on cathode particles that diffuses into the core upon high-temperature relithiation, increasing compositional homogeneity. The upcycling process leverages existing infrastructure, offering low capital cost and minimal additional chemical input. Through ex situ tomographic transmission X-ray microscopy (TXM), 3D Ni:Co elemental mixing is quantified, confirming that elemental content evens at the secondary particle level with a mean NMC622 composition upon relithiation. However, elemental gradients remain in the single crystalline primary particles. Ex situ high-resolution and in situ wide-angle X-ray diffraction reveals concurrent structural changes during the relithiation process. These findings provide key insights into the structural and chemical mechanisms of elemental diffusion to obtain further improvements of increased capacity and retention through compositional conversion of cathode materials.

Cathode Upcycling↗

DOE OSTI2025

From subsidies to stressors: Positively skewed ecological gradients alter biological responses to nutrients in streams

Abstract Subsidy–stress gradients offer a useful framework for understanding ecological responses to perturbation and may help inform ecological metrics in highly modified systems. Historic, region‐wide shifts from bottomland hardwood forest to row crop agriculture can cause positively skewed impact gradients in alluvial plain ecoregions, resulting in tolerant organisms that typically exhibit a subsidy response (increased abundance in response to environmental stressors) shifting to a stress response (declining abundance at higher concentrations). As a result, observed biological tolerance in modified ecosystems may differ from less modified regions, creating significant challenges for detecting biological responses to restoration efforts. Using the agriculturally dominated Mississippi Alluvial Plain (MAP) ecoregion in Mississippi, USA, as a case study, we tested the hypothesis that macroinvertebrate taxa that typically display a subsidy response to nutrient enrichment in less modified ecoregions (i.e., nutrient‐tolerance) shift to a stress response to increasing nutrients in highly modified watersheds with elevated baseline nutrient conditions (i.e., nutrient intolerance). The abundance and diversity of MAP‐specific intolerant taxa identified with threshold indicator taxa analysis were either unresponsive or exhibited a subsidy response to increasing nutrients in less modified ecoregions in Mississippi with less land alteration and lower nutrient concentrations, but declined at higher concentrations, providing evidence for a stress response to elevated nutrients in the MAP. Additionally, MAP‐specific tolerant and intolerant taxa richness responded to increased nutrients predictably and consistently across space and time within the MAP. However, in MAP streams, elevated specific conductance was predicted to dampen the response of tolerant and intolerant taxa richness to increasing nutrient concentrations, highlighting the importance of considering multistressor interactions when interpreting biological data. Lastly, we demonstrate the efficacy of this approach with sediment bacterial communities characterized with amplicon sequencing, which lack sufficient life history characteristics necessary for the development of multimetric indices. Both macroinvertebrate and bacterial communities responded similarly to increasing nutrient concentrations, suggesting DNA‐based approaches may provide an efficient biological assessment tool for monitoring water quality improvements in highly modified watersheds.

DeVilbiss, Stephen E. [U.S. Geological Survey Lowe↗

DOE OSTI2021

Solid-Density Ion Temperature from Redshifted and Double-Peaked Stark Line Shapes

Heβ spectral line shapes are important for diagnosing temperature and density in many dense plasmas. This work presents Heβ line shapes measured with high spectral resolution from solid-density plasmas with minimized gradients. The line shapes show hallmark features of Stark broadening, including quantifiable redshifts and double-peaked structure with a significant dip between the peaks; these features are compared to models through a Markov chain Monte Carlo framework. Line shape theory using the dipole approximation can fit the width and peak separation of measured line shapes, but it cannot resolve an ambiguity between electron density n e and ion temperature T i , since both parameters influence the strength of quasistatic ion microfields. Here a line shape model employing a full Coulomb interaction for the electron broadening computes self-consistent line widths and redshifts through the monopole term; redshifts have different dependence on plasma parameters and thus resolve the n e – T i ambiguity. The measured line shapes indicate densities that are 80–100% of solid, identifying a regime of highly ionized but well-tamped plasma. This analysis also provides the first strong evidence that dense ions and electrons are not in thermal equilibrium, despite equilibration times much shorter than the duration of x-ray emission; cooler ions may arise from nonclassical thermalization rates or anomalous energy transport. Furthermore, the experimental platform and diagnostic technique constitute a promising new approach for studying ion-electron equilibration in dense plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Modeling Structured Electrodes and Graded Porosity for Improving Discharge Rate Capability in Ultra-Thick Graphite|LiNi 0.6 Mn 0.2 Co 0.2 O 2 Batteries

Long-range electric vehicles (EVs) require high-energy-density batteries that also meet the power demands of high current charge and discharge. Ultra-thick (>100 μm) Lithium-ion battery electrodes are critical to enable this need, but slow ion transport in conventional uniform electrodes (UEs) reduces battery capacity at increasing charge/discharge rates. We present a 3D computational analysis on the impact of structured electrode (SE) and graded electrode (GE) geometries on the discharge rate capability of ultra-thick graphite|LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC-622) battery cells based on the footprint of a commercial EV pouch cell. SE cathodes with either a “grid” or “line” geometry and GEs with two layers of porosity were modeled. Based on the results of 230 models, we found that the electrolyte volume fraction is a key parameter that impacts capacity improvements in UEs, GEs, and SEs at 2 C–6 C discharge rates. SEs have the greatest discharge rate capability, outperforming GEs and UEs due to reduced Lithium-ion concentration gradients across the electrode thickness, which mitigates electrolyte depletion at high rates. The best SE model has a “grid” geometry with gravimetric and volumetric energy density improvements of 0.9%–4% at C/2–2 C and 18%–24% at 4 C–6 C relative to UEs.

25 ENERGY STORAGE↗

DOE OSTI2025

Transition Zones at the Changing Coastal Terrestrial‐Aquatic Interface

Coastal soils are a significant but highly uncertain component of global biogeochemical cycles. These systems experience spatial and temporal variability in biogeochemical processes, driven by marsh‐to‐upland gradients and hydrological fluctuations. These fluctuations make it difficult to understand and predict biogeochemical processes in these highly dynamic systems. We studied coastal soil biogeochemistry and its variability (a) at regional scales and (b) across transects from upland forest to marsh, in two contrasting regions—Lake Erie, a freshwater lacustrine system, and Chesapeake Bay, a saltwater estuarine system. Salinity‐related analytes were a key source of variability in soil biogeochemistry, not just in the saltwater system, but surprisingly, also in the freshwater system. We had hypothesized linear trends in biogeochemical parameters along the TAI—however, contrary to expectations, transition soils were not consistently intermediate between upland and marsh endmembers; the non‐monotonic trends of C, P, Fe along our transects suggest that these do not behave as expected and may be difficult to model and predict—thus these are key analytes to study in our regions. Rapidly changing soil factors across coastal gradients (e.g., Ca, K, CEC, and TS) may act as precursors to ecosystem shifts. Our comprehensive soil characterization represents a snapshot of a single timepoint of surface soils and provides essential data for mechanistic modeling of ecosystem dynamics across coastal transects.

Patel, Kaizad F. [Pacific Northwest National Labor↗

DOE OSTI2020

Mechanical design of the highly porous cuttlebone: A bioceramic hard buoyancy tank for cuttlefish

Cuttlefish, a unique group of marine mollusks, produces an internal biomineralized shell, known as cuttlebone, which is an ultra-lightweight cellular structure (porosity, ~93 vol%) used as the animal’s hard buoyancy tank. Although cuttlebone is primarily composed of a brittle mineral, aragonite, the structure is highly damage tolerant and can withstand water pressure of about 20 atmospheres (atm) for the species Sepia officinalis . Currently, our knowledge on the structural origins for cuttlebone’s remarkable mechanical performance is limited. Combining quantitative three-dimensional (3D) structural characterization, four-dimensional (4D) mechanical analysis, digital image correlation, and parametric simulations, here we reveal that the characteristic chambered “wall–septa” microstructure of cuttlebone, drastically distinct from other natural or engineering cellular solids, allows for simultaneous high specific stiffness (8.4 MN∙m/kg) and energy absorption (4.4 kJ/kg) upon loading. We demonstrate that the vertical walls in the chambered cuttlebone microstructure have evolved an optimal waviness gradient, which leads to compression-dominant deformation and asymmetric wall fracture, accomplishing both high stiffness and high energy absorption. Moreover, the distribution of walls is found to reduce stress concentrations within the horizontal septa, facilitating a larger chamber crushing stress and a more significant densification. Overall, the design strategies revealed here can provide important lessons for the development of low-density, stiff, and damage-tolerant cellular ceramics.

42 ENGINEERING↗

DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗

DOE OSTI2023

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE (FY23 Version, Rev. 1)

This report summarizes the development of neutron imaging capabilities and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature-driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. Yttrium hydride is the leading candidate to serve as a solid neutron moderator in microreactor cores, owing to its high density of hydrogen atoms as well as its superior thermal stability compared to all other metal hydrides. The experimental results and technique developments reported herein support the U.S. Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, it addresses the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in high-temperature microreactor designs by means of high-spatial-resolution neutron imaging. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. In principle, neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metal hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on three in-situ heating measurements of YH x performed over the 2020 to 2022 LANSCE operation cycles. The aims of these experiments ranged from furnace commissioning, determining sample quality, i.e., hydrogen uniformity via neutron computed tomography, and studying the effects of applied temperature-gradients on YH x pellets. Analyses and results from these neutron imaging measurements are given along with outlooks and guidelines for optimal future hydrogen diffusion measurements. Our conclusions are as follows. Image analyses indicate that centimeter-sized yttrium hydride cylindrical pellets exhibit uniform, whole-body hydrogen desorption and absorption without clear temperature dependence as reflected in the image attenuation at the opposing ends of each sample. This suggests that despite the large magnitude in temperature gradients applied by the furnace heating elements, the sample equilibrates to an unknown intermediate temperature. The origin of this result is likely the combination of short sample length (∼1cm) and use of a TZM can for containment where the latter created a thermal short across the sample. Nevertheless, the results from the most recent measurements indicate that neither significant concentration gradients of hydrogen were formed in centimeter-sized samples through the entire temperature range (25 °C to 950 °C) nor any formed due to temperature gradients on the order of 50 °C/cm up to 700 °C/cm. Furthermore, images from the FY2021 and FY2022 measurements indicate that samples of YH x , fabricated from either the direct hydride or powder metallurgy methods, are highly uniform in their hydrogen concentration to within the measurements’ spatial resolutions. The following questions arise from these latest results: 1) What is the intermediate temperature of the pellets in the TZM cans? 2) How quickly does the temperature equilibrate within the sample? and, 3) Do the observed changes in image attenuation follow known pressure-composition-temperature relations of yttrium hydride?

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

High‐Throughput Study of Amorphous Stability and Optical Properties of Superlattice‐Like Ge–Sb–Te Thin Films

A high‐throughput ion beam sputtering system is used to synthesize compositional gradient superlattice‐like (SLL) thin film libraries of Ge–Sb–Te alloys over the entire phase diagram. Here, the optical properties and structural evolution of the Ge–Sb–Te combinatorial SLL thin film are investigated. A systematic screening over the annealing temperature, annealing time, and modulation period has elucidated the critical factors that affect the stability of the metastable phase and optical properties. It is found that amorphous stability and optical constant are highly dependent on the modulation period and chemical composition of the thin film. This data‐driven approach offers new perspectives for accelerating the development of new materials with excellent optical and amorphous stability and for exploring their mechanisms, by greatly expanding the dataset of Ge–Sb–Te alloys with SLL structures through high‐throughput experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Leveraging differentiable programming in the inverse problem of neutron stars

Neutron stars (NSs) probe the high-density regime of the nuclear equation of state (EOS). However, inferring the EOS from observations of NSs is a computationally challenging task. Here, in this work, we efficiently solve this inverse problem by leveraging differential programming in two ways. First, we enable full Bayesian inference in under one hour of wall time on a GPU by using gradient-based samplers, without requiring pretrained machine learning emulators. Moreover, we demonstrate efficient scaling to high-dimensional parameter spaces. Second, we introduce a novel gradient-based optimization scheme that recovers the EOS of a given NS mass-radius curve. We demonstrate how our framework can reveal consistencies or tensions between nuclear physics and astrophysics. First, we show how the breakdown density of a metamodel description of the EOS can be determined from NS observations. Second, we demonstrate how degeneracies in EOS modeling using nuclear empirical parameters can influence the inverse problem during gradient-based optimization. Looking ahead, our approach opens up new theoretical studies of the relation between NS properties and the EOS, while effectively tackling the data analysis challenges brought by future detectors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Towards determination of the strong coupling $α_s(m_Z)$ from four-flavor lattice QCD using the continuous $β$-function method

The precise value of the strong coupling $α_s(m_{Z})$ at the $Z$-boson mass $m_{Z}$ is essential for high-energy phenomenology and precision tests of quantum chromodynamics (QCD). We present the status of a program targeting a $\sim 0.3\%$ determination of $α_s(m_{Z})$ using the renormalization group $β$-function in the infinite volume gradient flow scheme based on lattice QCD simulations of degenerate four-flavor highly improved staggered quark (HISQ) ensembles. In particular, we analyze both tree-level cutoff effects and finite-mass effects. We also outline the next steps of the analysis, including the infinite-volume and continuum extrapolations required for a precise determination of $α_s(m_Z)$.

Mandlecha, Yash (ORCID:000000020587962X)↗

DOE OSTI2026

Towards determination of the strong coupling $α_s(m_Z)$ from four-flavor lattice QCD using the continuous $β$-function method

Mandlecha, Yash [Michigan State U.; Michigan State↗

DOE OSTI2023

High absorption photovoltaic material and methods of making the same

A high absorption photovoltaic material and method of making the material for use in a solar cell are disclosed. The photovoltaic material includes a surface modified with a layer of repeating photonic crystal structures. The photonic crystal structures are approximately inverse conically shaped and have a curved sidewall that has an approximately Gaussian shape. The photonic crystal structures generally have a high vertical depth and sidewall angle. The structures also have a gradient refractive index profile and exhibit the parallel-to-interface refraction light trapping effect. An anti-reflective coating is disposed over the photonic crystal structure layer. The photovoltaic material exhibits near unity light absorption over a broad range of visible and near infrared wavelengths and incidence angles, even at reduced thicknesses. The photovoltaic structures are formed via a combined photolithography and reactive-ion etching method at low power with a gas mixture having a high ratio of an etchant component to a passivation component.

Kuang, Ping↗

DOE OSTI2024

Inverse-designed metasurfaces with facile fabrication parameters

Abstract Optical metasurfaces are planar nanostructured devices that are industrially attractive in part because they utilize high-throughput microelectronic fabrication techniques for implementation. It is therefore critical to develop design paradigms that can balance the realization of highly efficient wavefront responses together with device manufacturability. We introduce a gradient-based design framework for freeform metasurfaces in which nanoscale elements are explicitly constrained to feature basic shapes, nearly uniform feature sizes, and exceptionally low aspect ratios. In spite of the apparent uniformity of the metasurface geometric features, the devices are able to utilize nonlocal near-field optical coupling to achieve highly efficient and extreme wavefront scattering beyond conventional design methodologies. Utilizing this approach, we design facile high-numerical-aperture devices such as beam deflectors and large-area metalenses capable of diffraction-limited focusing. We anticipate that these concepts can facilitate the design and integration of metasurfaces into monolithic optical systems.

Optics↗