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At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Electrochemical leaching of critical materials from lithium-ion batteries: A comparative life cycle assessment

The manufacturing of lithium-ion batteries (LIB) requires critical materials such as cobalt (Co) and lithium (Li) that are essential for clean-energy products including electric vehicles. Because of their rapidly increasing demand and limited supply, the recycle and reuse of these materials from end-of-life LIB have garnered a lot of interest. Electrochemical leaching has emerged as a sustainable method to extract critical materials out of LIBs, so life cycle assessment was conducted to compare the environmental impacts with traditional peroxide-based leaching and another emerging technology – SO 2 -based leaching. The results showed that electrochemical leaching reduces the global warming potential (GWP) by 80%-87% compared to peroxide-based leaching due to a lower acid consumption, avoidance of hydrogen peroxide, and regeneration of reducing agent iron (II) sulfate and compares well with SO 2 -based leaching in most impact categories. Furthermore, the analysis suggested renewable energy can further reduce the environment footprint of electrochemical leaching.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Microwave-Assisted Synthesis of Cu@IrO 2 Core-Shell Nanowires for Low-Temperature Methane Conversion

A facile microwave-assisted synthesis was developed for the tunable fabrication of a novel Cu@IrO 2 core@shell nanowire motif. Experimental parameters, such as (i) reaction time, (ii) the method of addition of the Ir precursor, (iii) capping agent, (iv) reducing agent, as well as (v) the capping agent-to-reducing agent ratio, were subsequently optimized. The viability of other methods based on previously reported literature, such as refluxing, stirring, and physical sonication, was studied and compared with our optimized microwave-assisted protocol in creating our as-prepared materials. It should be noted that the magnitude of the IrO 2 shell could be tailored based on varying the Cu: Ir ratio coupled with judicious variations in the amounts of capping agent and reducing agent. Structural characterization techniques, such as XRD, XPS, and HRTEM (including HRTEM-EDS), were used to analyze our Cu@IrO 2 motifs. Specifically, the shell could be reliably tailored from sizes of 10 nm, 8 nm, 6 nm, and 3.5 nm with corresponding Cu: Ir ratios of 10:1, 15:1, 20:1, and 25:1, respectively. Moreover, the structural integrity of the motifs was probed and found to have been maintained after not only heat treatment but also the post-methane conversion process, indicative of an intrinsically high stability. Both components within the CuO-IrO 2 interface were able to activate methane at temperatures between 400 to 500 K with a reduction of the associated metal cations (Cu 2+ → Cu 1+ ; Ir 4+ → Ir 3+ ) and the deposition of CH x fragments on the surface, as clearly observed in the ambient-pressure XPS results. Thus, on the basis of their stability and chemical activity, these core-shell materials could be very useful for the catalytic conversion of methane into 'higher value" chemicals.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Artificial-intelligence-driven shot reduction in quantum measurement

Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots), incurring significant costs as accuracy increases. Optimizing shot allocation is thus critical for improving the efficiency of VQE. Current strategies rely heavily on hand-crafted heuristics requiring extensive expert knowledge. This paper proposes a reinforcement learning (RL)-based approach that automatically learns shot assignment policies to minimize total measurement shots while achieving convergence to the minimum of the energy expectation in VQE. The RL agent assigns measurement shots across VQE optimization iterations based on the progress of the optimization. This approach reduces VQE's dependence on static heuristics and human expertise. When the RL-enabled VQE is applied to a small molecule, a shot reduction policy is learned. The policy demonstrates transferability across systems and compatibility with other wavefunction Ansätze. In addition to these specific findings, this work highlights the potential of RL for automatically discovering efficient and scalable quantum optimization strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Towards Resilient Design of Leader-following Consensus with Attack Identification and Privacy Preservation Capabilities

This paper considers a leader-following consensus in the presence of unknown but bounded cyber-attacks. Specifically, we consider the following cyber-attack scenarios: (i) an attacker aims to destabilize the consensus dynamics by injecting exogenous signals to both the actuators of the followers and/or the communication network, (ii) an eavesdropper adversary aims to obtain information on the physical state of the agents. To this end, a novel resilient leader-following consensus algorithm based on a competitive interaction method is proposed. In addition, it is demonstrated that by appropriately choosing the information exchanged between the agents, the proposed control framework also enables the cooperative system to either distributively identify the compromised communication links in real-time or to protect the privacy of the physical state of the agents from the eavesdropper. Here, a numerical example is provided to illustrate the proposed resilient control algorithms.

Gusrialdi, Azwirman↗

DOE OSTI2022

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure‐Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

Abstract A novel ab initio methodology based on high‐throughput simulations has permitted designing unique biselective organic structure‐directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well‐established commercial CHA‐type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.

Bello‐Jurado, Estefanía↗

DOE OSTI2022

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure‐Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Why Black Flowers? An Extreme Environment and Molecular Perspective of Black Color Accumulation in the Ornamental and Food Crops

Pollinators are attracted to vibrant flower colors. That is why flower color is the key agent to allow successful fruit set in food or ornamental crops. However, black flower color is the least attractive to pollinators, although a number of plant species produce black flowers. Cyanidin-based anthocyanins are thought to be the key agents to induce black color in the ornamental and fruit crops. R2R3-MYB transcription factors (TFs) play key roles for the tissue-specific accumulation of anthocyanin. MYB1 and MYB11 are the key TFs regulating the expression of anthocyanin biosynthesis genes for black color accumulation. Post-transcriptional silencing of flavone synthase II ( FNS ) gene is the technological method to stimulate the accumulation of cyanidin-based anthocyanins in black cultivars. Type 1 promoter of DvIVS takes the advantage of FNS silencing to produce large amounts of black anthocyanins. Exogenous ethylene application triggers anthocyanin accumulation in the fruit skin at ripening. Environment cues have been the pivotal regulators to allow differential accumulation of anthocyanins to regulate black color. Heat stress is one of the most important environmental stimulus that regulates concentration gradient of anthocyanins in various plant parts, thereby affecting the color pattern of flowers. Stability of black anthocyanins in the extreme environments can save the damage, especially in fruits, caused by abiotic stress. White flowers without anthocyanin face more damages from abiotic stress than dark color flowers. The intensity and pattern of flower color accumulation determine the overall fruit set, thereby controlling crop yield and human food needs. This review paper presents comprehensive knowledge of black flower regulation as affected by high temperature stress, and the molecular regulators of anthocyanin for black color in ornamental and food crops. It also discusses the black color-pollination interaction pattern affected by heat stress for food and ornamental crops.

Ahmad, Sagheer↗

DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗

DOE OSTI2022

Ultra-high SERS detection of consumable coloring agents using plasmonic gold nanostars with high aspect-ratio spikes

Solution-based SERS detection by using a portable Raman instrument has emerged as an important tool due to its simplicity, and flexibility for rapid and on-site screening of analyte molecules. However, this method has several shortcomings, including poor sensitivity especially for weak-affinity analyte molecules, where there is no close contact between the plasmonic metal surface and analyte molecule. Examples of weak-affinity molecules include pigment molecules that are commonly used as a consumable coloring agent, such as allura red (AR), and sunset yellow (SY). As high consumption of colorant agents has been shown to cause adverse effects on human health, there is a strong need to develop a simple and practical sensing system with high sensitivity for these agents. Here we present a novel, highly sensitive solution-based SERS detection method for AR, and SY by using CTAC capped gold nanostars (GNS) having different aspect ratios (GNS-2, GNS-4, and GNS-5) without utilizing any aggregating agents which can enhance SERS signal however it reduces batch to batch reproducibility. The influence of the aspect ratio of GNS on SERS properties was investigated. We have achieved a limit of detection (LOD) of AR and SY as low as 0.5 and 1 ppb, respectively by using GNS-5 with the advantages of minimal sample preparation by just mixing the analyte solution into a well plate containing GNS solution. In addition, excellent colloidal stability and reproducibility have further enhanced the applicability in real-world samples. Altogether, our results evidence that the solution-based SERS detection platform using high aspect-ratio GNS can be applied for practical application to detect pigment molecules in real samples with satisfactory results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

AI-Ready Semantic Infrastructure for CEBAF: From CED to PALS Knowledge Graphs

JLab and PNNL are jointly developing an AI-ready data ecosystem that exposes the Continuous Electron Beam Acceleration Facility’s (CEBAF’s) operational configuration, lattice description, and control-system channels to agentic optimization frameworks through a standards-based semantic layer. The effort integrates the existing facility-specific CEBAF Element Database (CED) with extensions of the emerging facility-agnostic Particle Accelerator Lattice Standard (PALS) to produce a knowledge graph (KG) containing coherent, machine-interpretable views of devices, signals, and regions. With this KG, CEBAF’s setpoints, readbacks, and device hierarchies become queryable using a uniform declarative graph query language (e.g., Neo4j Cypher), providing intents and inspectable semantics suitable for agentic control. The resulting graph-backed interfaces will allow autonomous agents to retrieve authoritative machine configurations, reason over device- and signal-level relationships, and execute tuning and diagnostic workflows without bespoke CEBAF-specific logic, thereby delivering a scalable pathway from operational data to trustworthy agentic accelerator tuning frameworks.

Zhang, He [Thomas Jefferson National Accelerator F↗

DOE OSTI2025

Automatic building energy model development and debugging using large language models agentic workflow

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. Here, we developed a generic LLM-planning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Dispersing Agents Impact Performance of Protonated Phosphonic Acid High-Temperature Polymer Electrolyte Membrane Fuel Cells

The microstructure of ionomers plays a significant role in the performance of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). Here, we establish direct correlations between the properties of dispersing agents and the microstructure of a protonated phosphonic acid ionomer. Most importantly, the formation of the porous structure of a protonated phosphonic acid ionomer depends on the pK a of the liquid media. Namely, the acid–base interaction between the charged polymer and dispersing agents determines the porosity in the ionomer thin films. The HT-PEMFC performance increases with the level of porosity of the ionomer, as the pores enable fast reactant gas accessibility. Overall, this study concludes the importance of the pK a of ionomers’ dispersing media for the HT-PEMFC performance.

25 ENERGY STORAGE↗

DOE OSTI2025

Entanglement engineering of optomechanical systems by reinforcement learning

Entanglement is fundamental to quantum information science and technology, yet controlling and manipulating entanglement—so-called entanglement engineering—for arbitrary quantum systems remains a formidable challenge. There are two difficulties: the fragility of quantum entanglement and its experimental characterization. We develop a model-free deep reinforcement-learning (RL) approach to entanglement engineering, in which feedback control together with weak continuous measurement and partial state observation is exploited to generate and maintain desired entanglement. We employ quantum optomechanical systems with linear or nonlinear photon–phonon interactions to demonstrate the workings of our machine-learning-based entanglement engineering protocol. In particular, the RL agent sequentially interacts with one or multiple parallel quantum optomechanical environments, collects trajectories, and updates the policy to maximize the accumulated reward to create and stabilize quantum entanglement over an arbitrary amount of time. The machine-learning-based model-free control principle is applicable to the entanglement engineering of experimental quantum systems in general.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Porous Colloidal Nanoparticles as Injectable Multimodal Contrast Agents for Enhanced Geophysical Sensing

Injecting fluids into underground geologic structures is crucial for the development of long-term strategies for managing captured carbon and facilitating sustainable energy extraction operations. Here, we have previously reported that the injection of metal–organic frameworks (MOFs) into the subsurface can enhance seismic monitoring tools to track fluids and map complex structures, reduce risk, and verify containment in carbon storage reservoirs because of their absorption capacity of low-frequency seismic waves. Here, we demonstrate that water-based Cr/Zn/Zr MOF colloidal suspensions (nanofluids) are multimodal geophysical contrast agents that enhance near-wellbore logging tools. Based on experimental fluid-only measurements, MIL-101(Cr), ZIF-8, and UiO-66 nanofluids have distinct complex conductivity and/or low-field nuclear magnetic resonance (NMR) signatures that are relevant to field-deployed technologies, implying the potential to enhance near-wellbore monitoring of CO 2 injection and associated processes with downhole logging tools. Small- and wide-angle X-ray scattering characterization of ~0.5 wt % MIL-101(Cr) suspensions confirmed phase stability and provided insight into the fractal nature of colloidal nanoparticles. Finally, low-field (2 MHz) NMR measurements of MIL-101(Cr) nanofluid injection into a prototypical Berea sandstone demonstrate how paramagnetic high-surface area MOFs may dominate the relaxation times of hydrogen-bearing fluids in porous geologic matrices, enhancing the mapping of near-surface and near-wellbore transport pathways and advancing sustainable subsurface energy technologies.

NMR↗

DOE OSTI2022

Dynamic Role-Based Access Control Policy for Smart Grid Applications: An Offline Deep Reinforcement Learning Approach

Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats. In this paper, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-to-end proof of concept of our model. Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Quantum Calculations of VX Ammonolysis and Hydrolysis Pathways via Hydrated Lithium Nitride

Recently, lithium nitride (Li 3 N) has been proposed as a chemical warfare agent (CWA) neutralization reagent for its ability to produce nucleophilic ammonia molecules and hydroxide ions in aqueous solution. Quantum chemical calculations can provide insight into the Li 3 N neutralization process that has been studied experimentally. Here, we calculate reaction-free energies associated with the Li3N-based neutralization of the CWA VX using quantum chemical density functional theory and ab initio methods. We find that alkaline hydrolysis is more favorable to either ammonolysis or neutral hydrolysis for initial P-S and P-O bond cleavages. Reaction-free energies of subsequent reactions are calculated to determine the full reaction pathway. Notably, products predicted from favorable reactions have been identified in previous experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Unsupported and carbon-supported silver catalysts for oxygen reduction reaction in alkaline media

Quick and easy Ag catalysts preparation via wet chemical synthesis method using only reducing agent (pure-Ag); reducing agent and citric acid as the capping agent (Ag-CA); and carbon support (KetjenBlack 600J), capping agent, and the reducing agent (Ag/C) is demonstrated. The Ag-based electrocatalysts are characterized by high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) with energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, and X-ray photoelectron spectroscopy (XPS). The electrocatalytic activity of Ag catalysts for O 2 reduction reaction (ORR) in 1 M KOH is evaluated using the rotating (ring)-disc electrode method. SEM and HAADF-STEM results show that the unsupported pure-Ag and Ag-CA catalysts consist mainly of big agglomerates, and Ag/C has the smallest agglomerates and some sub-3 nm Ag nanoparticles. The XPS results reveal that Ag in all the catalysts is in the metallic form (Ag 0 ). Despite consisting of big agglomerates, the Ag-CA catalyst exhibits similar ORR electrocatalytic activity to that of Ag/C. Ag-CA (unsupported) shows the lowest hydrogen peroxide yield. These results are of great importance for the development of Ag-based catalysts that can be prepared in a fast, simple and easily up scalable fashion, for anion exchange membrane fuel cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Development of a chemical-free process utilizing ozone oxidative precipitation for the recovery of cobalt and manganese from acid mine drainage

Acid mine drainage (AMD) has been identified as a viable secondary resource of Co, and Mn. However, recovery of these elements while treating AMD is challenging due to the unfavorable pH and Eh conditions for Co-Mn precipitation. This study investigated the effects of various ligands (hydroxide, carbonate, ammonium, sulfate, and phosphate) and oxidizers (sodium persulfate, potassium permanganate, and ozone) on the precipitation of Co-Mn from AMD through experimental and solution chemistry studies. Ozone was found to be the most effective agent for the recovery of these elements. Based on the results, an AMD treatment process was formulated for selective recovery of multiple critical elements, including Al, REEs, Co, and Mn. More than 95 % of Co and Mn were recovered through the proposed process utilizing chemical-less ozone oxidative precipitation. In conclusion, a precipitate containing 0.9 % Co and 54.6 % Mn was obtained from an AMD source containing 0.9 ppm Co and 41.8 ppm Mn.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗