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DOE OSTI2020

Novel Membrane and Electrodeposition-Based Separation and Recovery of Rare Earth Elements from Coal Combustion Residues (Final Report)

This project developed a hydrometallurgical-based technology to extract and concentrate rare earth elements (REEs) from a representative group of coal combustion residuals (CCRs) from major coal sources in the United States. The approach for REE recovery comprises two general components: (1) Leaching of REEs from CCRs and (2) Separation of REEs from other major ions in the CCR leachate. The leaching step entails acid leaching with nitric or hydrochloric acid, and in some cases, pretreatment of CCRs with alkaline roasting prior to acid leaching. The follow-up REE recovery steps involve a series of membrane-based methods to separate and concentrate rare earth ions from other major ions in acid leachates of CCRs. These methods include liquid emulsion membranes, supported liquid membranes and electrodeposition with carbon nanotube electrochemical filters. The advantage of these separation approaches (relative to conventional approaches such as solvent extraction) is a reduction in the usage of solvents and other costly chemicals and a reduction of hazardous waste products that are challenges in conventional REE separation methods. In the testing of our process, we selected a representative set of CCR samples from our collection and performed a suite of characterization measurements (total REE content and major mineralogy). The major results of this work demonstrated that heated acid leaching could efficiently extract REEs from PRB-based coal fly ashes while the Appalachian and Illinois Basin CCRs required alkaline roasting with NaOH prior to acid leaching at room temperature. The supported liquid membrane (SLM) configuration and electrochemical deposition (ED) system yielded the most promising methods of REE recovery from the acid leachates of fly ash. Our most concentrated product to date comprised 1 wt.% (dry basis) REEs produced from an Appalachian-based fly ash leached and purified by SLM. The SLM separation process was more selective for heavy REEs over the light REEs. When SLM was combined with ED, preliminary results demonstrated that a final product of >2 wt.% REEs was possible.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Application of Ionic Liquids for the Recycling and Recovery of Technologically Critical and Valuable Metals

Population growth has led to an increased demand for raw minerals and energy resources; however, their supply cannot easily be provided in the same proportions. Modern technologies contain materials that are becoming more finely intermixed because of the broadening palette of elements used, and this outcome creates certain limitations for recycling. The recovery and separation of individual elements, critical materials and valuable metals from complex systems requires complex energy-consuming solutions with many hazardous chemicals used. Significant pressure is brought to bear on the improvement of separation and recycling approaches by the need to balance sustainability, efficiency, and environmental impacts. Due to the increase in environmental consciousness in chemical research and industry, the challenge for a sustainable environment calls for clean procedures that avoid the use of harmful organic solvents. Ionic liquids, also known as molten salts and future solvents, are endowed with unique features that have already had a promising impact on cutting-edge science and technologies. This review aims to address the current challenges associated with the energy-efficient design, recovery, recycling, and separation of valuable metals employing ionic liquids.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Enabling Autonomous Electron Microscopy for Networked Computation and Steering

Advanced electron microscopy workflows require an ecosystem of microscope instruments and computing systems possibly located at different sites to conduct remotely steered and automated experiments. Current workflow executions involve manual operations for steering and measurement tasks, which are typically performed from control workstations co-located with microscopes; consequently, their operational tempo and effectiveness are limited. We propose an approach based on separate data and control channels for such an ecosystem of Scanning Transmission Electron Microscopes (STEM) and computing systems, for which no general solutions presently exist, unlike the neutron and light source instruments. We demonstrate automated measurement transfers and remote steering of Nion STEM physical instruments over site networks. We propose a Virtual Infrastructure Twin (VIT) of this ecosystem, which is used to develop and test our steering software modules without requiring access to the physical instrument infrastructure. Additionally, we develop a VIT for a multiple laboratory scenario, which illustrates the applicability of this approach to ecosystems connected over wide-area networks, for the development and testing of software modules and their later field deployment.

Al Najjar, Anees↗

DOE OSTI2026

Simulated moving bed-inspired method for continuous adsorptive denitrogenation of model fuel

Efficient, continuous routes for removing nitrogen-containing compounds from hydrothermal liquefaction-derived synthetic aviation fuel are needed to enable direct blending with conventional jet fuels. Here, we report a simulated moving bed-inspired process for adsorptive denitrogenation of a model fuel. Unlike conventional simulated moving bed systems, which are designed for sharp separations between similar solutes, this approach was run deliberately outside the classical separation region so that both pyridine and indole were removed together from the hydrocarbon stream. Alcohol solvents were used to regenerate the silica adsorbent, maintaining performance over extended operation and avoiding the downtime and energy demand associated with calcination. Under these conditions, the system demonstrates removal of more than 98% of nitrogen while cutting solvent use by 28% compared to batch operation. Classical modeling tools predicted column concentration profiles even in this nontraditional regime, suggesting a straightforward path to scaling. Together, these results motivate solvent-efficient, continuous denitrogenation strategies that could be integrated with biorefinery processes.

Adsorption↗

DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗

DOE OSTI2023

Ion soft landing: A unique tool for understanding electrochemical processes

Ion soft landing (SL) is a preparative mass spectrometry approach that enables deposition of mass- and charge-selected gaseous ions onto surfaces with controlled kinetic energy. The unique capabilities of SL provide an opportunity to populate electrode–electrolyte interfaces (EEIs) with well-defined intact electroactive ions of known composition, thereby facilitating the characterization of their intrinsic electrochemical properties. Here, in this perspective, we describe the SL technique and discuss how it may be used to study the effect of the charge state, stoichiometry, and composition of large redox active molecules and clusters on their electron transfer kinetics. SL has enabled the characterization of redox-active species that cannot be purified and examined using conventional bulk-phase separation and deposition approaches. Furthermore, precise control over the deposition process provides an opportunity to prepare and characterize well-defined EEIs relevant to energy storage and conversion, catalysis, and sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Sulfonated polybenzimidazole membrane with graphene oxide additive for 2,3-butanediol/water separation: A molecular simulation

Membrane separation for 2,3-butanediol (2,3-BDO) recovery from fermentation broth is highly valued for sustainable and renewable processes, but it requires efficient membrane materials. Here, this work evaluates the sulfonated polybenzimidazole (sPBI) and its graphene oxide (GO) doped composite membrane for separating 2,3-BDO and water via atomistic simulations. Density functional theory calculations are applied to identify various forms of sPBI structures and quantify their binding interactions with 2,3-BDO and water. Classical molecular dynamic simulations are used to evaluate the structural changes, diffusivity, and selectivity of 2,3-BDO and water in different sPBI models, GO surfaces, and GO-doped sPBI composite models. Our results suggest that sPBI slightly increases the crystallinity of the membrane structures, enhances the adsorption strength for both 2,3-BDO and water, and improves the water/2,3-BDO selectivity by 2–3 times. The GO surfaces display a maximum selectivity at a surface coverage of 0.1–0.15 for both hydroxyl and epoxy surface groups. The addition of GO flakes to sPBI creates new interaction sites for 2,3-BDO and water at the interface of sPBI and GO, and the water/2,3-BDO selectivity of GO-doped sPBI models is further increased up to 3 times. This work illustrates how the integrated addition of sPBI and GO flakes offers a promising approach to selective separation of 2,3-BDO and water, providing theoretical guidance for polybenzimidazole-based membranes in the potential application of 2,3-BDO recovery.

2,3-butanediol↗

DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗

DOE OSTI2024

Electrolytic recovery of metals from lithium battery cathodes in moisture-tolerant molten hydroxide salt

Lithium-ion battery recycling offers an opportunity to develop innovative technologies to close the loop on the battery materials cycle and increase the resilience of the battery supply chain. Here, in this study, we demonstrate a two-step pyroelectrochemical method for producing mixed-metals from lithium-ion cathodes in a molten hydroxide salt. Mixed metal oxides in the form of insoluble lithium-ion cathode materials of LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) and spent lithium-ion battery materials (black mass) were electrochemically reduced to a soluble form and dissolved into a molten hydroxide salt bath. Electrochemical characterization of the process salt indicated accumulation of dissolved transition metals in the salt. A separate cathode was used to produce alloys of Ni, Mn, and Co electrochemically from the dissolved lithium-ion cathode materials. Characterization by scanning electron microscopy fitted with an energy dispersive X-ray spectrometer showed transition metals present in the cathode materials were recovered at the separate cathode. This approach represents a scalable, low temperature pyroelectrochemical process that can potentially reduce the cost and close the loop of battery cathode recycling.

25 ENERGY STORAGE↗

DOE OSTI2021

Unlocking bulk and surface oxygen transport properties of mixed oxide-ion and electron conducting membranes with combined oxygen permeation cell and oxygen probe method

Surface exchange kinetics and bulk diffusion of oxygen are of paramount importance to the activity of oxygen electrocatalysis and performance of electrochemical devices such as fuel cell, metal-air batteries, and oxygen separation membranes. Conventional approaches to obtaining these transport properties are often limited to single property under a specific non-operation related condition. Here we use a combined oxygen permeation cell and oxygen probe methodology to simultaneously attain rates of oxygen surface exchange and bulk conductivity/chemical diffusivity of three representative mixed oxide-ion and electron conductors, namely SrCo 0.9 Ta 0.1 O 3-δ (SCT), La 0.6 Sr 0.4 CoO 3-δ (LSC) and La 0.6 Sr 0.4 FeO 3-δ (LSF), operated under a steady-state oxygen flux. The results explicitly show that SCT exhibit the highest oxide-ion conductivity/chemical diffusivity, fastest rates of surface oxygen exchange kinetics, thus promising to be the best oxygen electrocatalyst. Here, we have also mapped out the distribution of oxygen chemical potential gradient across the membranes and applied B-transport number concept to illustrate the rate-limiting steps in the overall oxygen permeation process.

42 ENGINEERING↗

DOE OSTI2024

Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning

Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets. Our RL strategy utilizes decision trees coupled with a hierarchical reward strategy to accelerate convergence in high-dimensional continuous search spaces. Unlike the Stillinger-Weber approach, which demands separate formalisms for buckled and puckered forms, the ML-Tersoff model concurrently captures multiple properties of the two polymorphs by effectively representing the local environment, thereby avoiding the need for different atomic types. Here, we apply the ML model to understand the mechanical and thermal properties of the arsenene polymorphs and nanostructures. We observe an inverse relationship between the critical strain and temperature in arsenene. Thermal conductivity calculations in nanosheets show good agreement with ab initio data, reflecting a decrease in thermal conductivity attributable to increased anharmonic effects at higher temperatures. We also apply the model to predict the thermal behavior of arsenene nanotubes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Computational and Experimental Study for the Denitrification of Biomass-Derived Hydrothermal Liquefaction Oil

Hydrothermal liquefaction (HTL) is a promising method for processing wet biomass and waste feedstock to produce biofuels. During the HTL process, proteins and other biomolecules in certain feedstock get converted into nitrogenous compounds in produced biocrude, which represents a major challenge to further upgrading them into fuels. One promising approach is to separate nitrogenous compounds from the biocrude using polymeric resins. In this study, experiments were conducted to down-select sorbent and resin systems using a nitrogen-compound-containing surrogate biocrude. We model the binding interactions between an Amberlyst polymeric resin with various compounds present in the biocrude mixture such as nitrogenous compounds like pyrrole, pyridine, hexanamide, and representative co-existing compounds like phenol and dodecanoic acid. To ascertain the efficiency of various resins in the denitrogenation process, we have developed a quantitative structure–function model for the interacting components in the mixture. Our results suggest that the Amberlyst resin is a viable candidate for efficient removal of target nitrogen-containing compounds (such as pyridine) from the biocrude as a result of favorable interactions. The computational studies provide some insight into how and why the identified resin (Amberlyst) works in selective extraction of nitrogenous compound(s).

09 BIOMASS FUELS↗

DOE OSTI2021

Temperature and salt controlled tuning of protein clusters

The formation of molecular assemblies in protein solutions is of strong interest both from a fundamental viewpoint and for biomedical applications. While ordered and desired protein assemblies are indispensable for some biological functions, undesired protein condensation can induce serious diseases. As a common cofactor, the presence of salt ions is essential for some biological processes involving proteins, and in aqueous suspensions of proteins can also give rise to complex phase diagrams including homogeneous solutions, large aggregates, and dissolution regimes. Here, we systematically study the cluster formation approaching the phase separation in aqueous solutions of the globular protein BSA as a function of temperature (T), the protein concentration (c p ) and the concentrations of the trivalent salts YCl 3 and LaCl 3 (c s ). As an important complement to structural, i.e. time-averaged, techniques we employ a dynamical technique that can detect clusters even when they are transient on the order of a few nanoseconds. By employing incoherent neutron spectroscopy, we unambiguously determine the short-time self-diffusion of the protein clusters depending on c p , c s and T. We determine the cluster size in terms of effective hydrodynamic radii as manifested by the cluster center-of-mass diffusion coefficients D. For both salts, we find a simple functional form D(c p , c s , T) in the parameter range explored. The calculated inter-particle attraction strength, determined from the microscopic and short-time diffusive properties of the samples, increases with salt concentration and temperature in the regime investigated and can be linked to the macroscopic behavior of the samples.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Selective Biosorption of Valuable Rare Earth Elements Among Co-Occurring Lanthanides

To meet the increasing demand for rare earth elements (REEs) essential for low carbon-intensity energy technologies, new methods are needed for selective REE extraction from unconventional resources. Only a few REEs have significant economic value, but isolation of target rare earths from co-occurring lanthanides is challenging due to their similar chemical behavior. We present a novel approach to enhance separation of specific REEs from lanthanide mixtures. Escherichia coli cells engineered with lanthanide binding tags (LBTs) were immobilized in nonadsorbing, permeable polyethylene glycol diacrylate beads and packed into continuous flow, fixed-bed columns. Breakthrough of 15 rare earths in the +3 oxidation state resulted in notable differences in adsorption selectivity, with greatest separation between europium (Eu) and lanthanum (La) due to competitive displacement. REE adsorption onto fixed-bed columns was predicted by coupling a surface complexation model to a calibrated one-dimensional dual porosity transport model that accounts for interbead advective and intrabead diffusive transport. A tradeoff between high-abundance, low-affinity native carboxyl sites and low-abundance, high-affinity engineered LBT sites dictates process recovery efficiency and selectivity. Key chemical and operational parameters are identified to maximize selective extraction of high-value lanthanides, achieving a threefold enhancement of Eu recovery relative to La in a mixed REE solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry–mass spectrometry

Abstract Motivation Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion’s collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. Results We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. Availability and implementation https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. Supplementary information Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Real-Time GPU-Accelerated OFDR With an Integrated Auxiliary Interferometer

A GPU-accelerated optical frequency domain reflectometry (OFDR) system with an improved integrated auxiliary interferometer is proposed. Unlike conventional approaches that require separate auxiliary interferometers and multiple detection channels, the proposed OFDR system embeds this functionality directly into the signal via an intentional beat component. This enables self-calibration of laser nonlinearity while maintaining a cost-effective hardware configuration. Building on this simplified configuration, the system leverages GPU acceleration with an NVIDIA RTX 4070 Ti to achieve real-time performance, delivering high-throughput signal processing for continuous OFDR interrogation. The signal processing pipeline comprises signal capture, resampling for nonlinearity compensation, and frequency shift computation, all optimized for parallel execution. Hardware benchmarking demonstrates substantial acceleration over CPU implementations, achieving up to a 45× speedup for resampling and frequency shift computations and enabling processing latencies below 30 ms. Thermal response validation is conducted under two complementary scenarios: localized heating using a water bath and cryogenic-temperature conditions using liquid nitrogen. Under localized heating, the system achieves an accuracy of 0.249 °C with a thermal sensitivity of 5.971 GHz/°C, while cryogenic-temperature validation demonstrates a frequency shift response with a sensitivity of 2.383 GHz/°C and an accuracy of 2.04 °C. The high acceleration of the proposed GPU-accelerated OFDR system and its accuracy are achieved by exploiting CUDA-based stride indexing, enabling efficient parallel segmentation and processing of large datasets without additional memory copies. The benchmarking results confirm the robustness, accuracy, and deployability of the proposed OFDR system across a wide temperature range, establishing it as a practical platform for real-time distributed fiber sensing in structurally dynamic environments.

Harb, Salah [Lawrence Berkeley National Laboratory↗

DOE OSTI2025

A new way to unravel the $^{12}$C($\alpha $,$\gamma $)$^{16}$O cross section components using neural networks

The $^{12}$C($\alpha $,$\gamma $)$^{16}$O reaction rate is crucial in determining the carbon-to-oxygen abundance ratio in stellar nucleosynthesis. Measuring this reaction’s cross section at stellar energies is challenging due to its extremely small value, approximately 10 -17 barn at E c.m. = 300 keV. To address this, R-matrix calculations are employed to extrapolate data to lower energies, requiring a comprehensive understanding of each contribution to the cross section. The dominant contributions to the cross section at stellar energies arise from electric dipole (E1) and electric quadrupole (E2) transitions to the ground state of 16 O, along with a significant cascade contribution. Traditionally, these contributions have been separated using the γ-ray angular distribution. In this work, we propose a novel technique using the energy distribution of the 16 O recoils at the focal plane. This method involves a neural network trained on detailed Monte Carlo simulations of the energy distribution of recoils transported through the recoil mass separator ERNA. This approach enables the simultaneous determination of all three contributions with errors around 10% in the energy range E c.m. = 1.0–2.2 MeV. In conclusion, by employing this new technique, we aim to significantly improve the accuracy of determining the cross section of the $^{12}$C($\alpha $,$\gamma $)$^{16}$O reaction at astrophysical energies.

Astrochemistry↗

DOE OSTI2025

Extremely Durable Concrete using Methane Decarbonization Nanofiber Co-products with Hydrogen

This project successfully developed a scalable, low-cost co-production process for hydrogen (H₂) and carbon nanofibers (CNFs) using methane through catalytic chemical vapor deposition (CVD). CNFs are high-value materials with exceptional mechanical, electrical, and chemical properties, while hydrogen is a critical clean energy resource. By leveraging vapor deposition to create highly dispersed metal catalysts, this work addressed the dual challenge of producing low-cost hydrogen and enhancing the durability of concrete using co-produced CNFs. Unlike prior approaches, which required separating the carbon product from the catalyst, this innovative method integrated the carbon product, catalyst, and silica support as a crack-bridging additive for Portland cement. This novel application aimed to advance the performance of high-performance concrete (HPC) by improving its flexural toughness, durability, and resistance to environmental degradation.

08 HYDROGEN↗