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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Optimizing Heat Recovery with Storage: Control Validation and Sensitivity Analysis of the Time-Independent Energy Recovery Plant Using Modelica

Heat recovery in large building central plants saves energy but traditionally requires simultaneous heating and cooling. The Time-Independent Energy Recovery (TIER) plant shifts this paradigm by integrating thermal energy storage (TES) to enable heat recovery regardless of concurrent demand, offering a highly efficient, space-saving solution to achieve California’s energy goals. However, its integration of heat recovery chillers, cooling-only chillers, cooling towers, and trim air-source heat pumps (ASHPs) creates growing control and sizing complexity. To overcome this, this study employs high-fidelity Modelica dynamic simulation to validate TIER control sequences and optimize equipment sizing. We translated the written Sequences of Operation into executable Control Description Language (CDL) to test logic against sub-hourly loads. This verification workflow successfully identified and resolved critical vulnerabilities, such as thermal storage freezing and equipment short-cycling, in a virtual environment prior to physical deployment. Then, the study analyzes TIER plant performance across three simulated building types in three locations, and a real building load profile, ensuring variety in heating and cooling loads, and simultaneity factors and explores sizing rules for the TES and ASHP capacity. The analysis shows that the TIER plant operates equipment efficiently leading to a plant SCOP of around 7.5 across all scenarios, higher than a traditional ASHP plant, and a viable pathway to de-risk complex system design and control through simulation to identify optimal designs that maximize energy efficiency, minimize operational costs, and ensure robust operation in varied environmental conditions, thereby facilitating the broader adoption of such a solution for large buildings.

Zanetti, Ettore↗
DOE OSTI2024

Optimal Zeno Dragging for Quantum Control: A Shortcut to Zeno with Action-Based Scheduling Optimization

The quantum Zeno effect asserts that quantum measurements inhibit simultaneous unitary dynamics when the “collapse” events are sufficiently strong and frequent. This applies in the limit of strong continuous measurement or dissipation. It is possible to implement a dissipative control that is known as “Zeno dragging” by dynamically varying the monitored observable, and hence also the eigenstates, which are attractors under Zeno dynamics. This is similar to adiabatic processes, in that the Zeno-dragging fidelity is highest when the rate of eigenstate change is slow compared to the measurement rate. We demonstrate here two theoretical methods for using such dynamics to achieve control of quantum systems. The first, which we shall refer to as “shortcut to Zeno,” is analogous to the shortcuts to adiabaticity (counterdiabatic driving) that are frequently used to accelerate unitary adiabatic evolution. In the second approach, we apply the Chantasri-Dressel-Jordan stochastic action [PRA 88, 042110 (2013)], and demonstrate that the extremal-probability readout paths derived from this are well suited to setting up a Pontryagin-style optimization of the Zeno-dragging schedule. A fundamental contribution of the latter approach is to show that an action suitable for measurement-driven control optimization can be derived quite generally from statistical arguments. Implementing these methods on the Zeno dragging of a qubit, we find that both approaches yield the same solution, namely, that the optimal control is a unitary that matches the motion of the Zeno-monitored eigenstate. We then show that such a solution can be more robust than a unitary-only operation and we comment on solvable generalizations of our qubit example embedded in larger systems. These methods open up new pathways toward systematically developing dynamic control of Zeno subspaces to realize dissipatively stabilized quantum operations. Published by the American Physical Society 2024

Physics↗
DOE OSTI2026

Enabling Dynamic Probabilistic Risk Assessment of Physical Security Using EMRALD and MAAP (Presentation)

The optimization of physical security in nuclear power plants requires sophisticated methodologies that integrate operator actions and plant behavior through advanced simulation tools. Idaho National Laboratory has developed the Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF) methodology, an approach that integrates force-on-force simulations, dynamic probabilistic risk assessment, and thermal hydraulics modeling to enhance security planning while reducing costs. A reduced order model for thermal hydraulic simulations performed by the Modular Accident Analysis Program (MAAP) was developed to evaluate reactor core behavior during attack scenarios. MAAP simulations are computationally intensive and must be run in a secure environment, complicating analysis and validation. By pre-computed scenario outcomes for a small number of modified parameters, the reduced order model significantly decreases the computational cost and enables offsite review of the results.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

autoGEMM: Pushing the Limits of Irregular Matrix Multiplication on Arm Architectures

This paper presents an open-source library that pushes the limits of performance portability for irregular General Matrix Multiplication (GEMM) on the widely-used Arm architectures. Our library, autoGEMM, is designed to support a wide range of Arm processors: from edge devices to HPC-grade CPUs. autoGEMM generates optimized kernels for various hardware configurations by auto-combining fragments of autogenerated micro-kernels that employ hand-written optimizations to maximize computational efficiency. We optimize the kernel pipeline by tuning the register reuse and the data load/store overlapping. In addition, we use a dynamic tiling scheme to generate balanced tile shapes. Finally, we position autoGEMM on top of the TVM framework where our dynamic tiling scheme prunes the search space for TVM to identify the optimal combination of parameters for code optimization. Evaluations on five different classes of Arm chips demonstrate the advantages of autoGEMM. For small matrices, autoGEMM achieves 98% of peak and up to 2.0x speedup over state-of-the-art libraries such as LIBXSMM and LibShalom. For irregular matrices (i.e. tall skinny and long rectangles), autoGEMM is 1.3-2.0x faster than widely-used libraries such as OpenBLAS and Eigen. autoGEMM is publicly available at: https://github.com/wudu98/autoGEMM.

Wu, Du↗
DOE OSTI2025

Computational Fluid Dynamics Analysis of the Molten Salt Tritium Transport Experiment Test Section

Tritium, a radionuclide produced through neutron capture by lithium and other elements (beryllium and fluoride) in molten salts, presents unique challenges to radionuclide release. This is true for both fusion energy breeder blankets and molten salt fission reactors. The fundamental understanding of tritium transport is crucial to the safe design and operation of these reactors. The Molten Salt Tritium Transport Experiment (MSTTE), currently under construction at Idaho National Laboratory, aims to investigate tritium transport phenomena using a forced-convection fluoride salt loop. This loop is designed to study various transport mechanisms, such as permeation through metals and gas-liquid interactions, and is intended to support future research on tritium extraction units. A critical aspect of the MSTTE loop design is ensuring a fully developed velocity profile before the fluid reaches the permeation test section where measurements are made. This study employs computational fluid dynamics to model the salt flow behavior within the MSTTE permeation test section. A realizable k-ε turbulent model with enhanced wall treatment is used to simulate the single-phase, vertical upward flow of molten salt FLiNaK under isothermal conditions. The simulation results indicated flow distortion and underdeveloped profiles at all planned flow rates within the test section due to the 85-deg sharp bend. To address this issue, a reduced diameter with a reducer and expander and a flow conditioner are investigated to achieve fully developed flow. The analysis showed that the flow conditioner successfully corrected the flow profile, achieving fully developed behavior at a flow rate of 50 liters per minute (LPM). In conclusion, this research enhances our understanding of flow dynamics in molten salt systems and contributes to optimizing tritium transport control technologies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Fast methods for multisite charge transfer. Processes II. Analytic nuclear gradients and nonadiabatic dynamics for cCASSCF(1,n) and cCASSCF(2n-1,n) wavefunctions

In this work we derive and implement analytic nuclear gradients and derivative couplings for a constrained complete active space self-consistent field with a small active space designed to model electron or hole transfer. Using a Lagrangian formalism, we are able to differentiate both the CASSCF energy and the constraint (which is required for smooth surfaces over a wide range of parameter space), and the resulting efficient algorithm can be immediately applied to nonadiabatic dynamics simulations of charge transfer processes. Here, we run initial surface-hopping simulations of a proton coupled electron transfer event for a phenoxyl–phenol system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantum Adiabatic Optimization with Rydberg Arrays: Localization Phenomena and Encoding Strategies

Quantum adiabatic optimization seeks to solve combinatorial problems using quantum dynamics, requiring the Hamiltonian of the system to align with the problem of interest. However, these Hamiltonians are often incompatible with the native constraints of quantum hardware, necessitating encoding strategies to map the original problem into a hardware-conformant form. While the classical overhead associated with such mappings is easily quantifiable and typically polynomial in problem size, it is much harder to quantify their overhead on the quantum algorithm, e.g., the transformation of the adiabatic timescale. In this work, we address this challenge on the concrete example of the encoding scheme proposed in [Nguyen , PRX Quantum , 010316 (2023)], which is designed to map optimization problems on arbitrarily connected graphs into Rydberg atom arrays. We consider the fundamental building blocks underlying this encoding scheme and determine the scaling of the minimum gap with system size along adiabatic protocols. Even when the original problem is trivially solvable, we find that the encoded problem can exhibit an exponentially closing minimum gap. We show that this originates from a quantum coherent effect, which gives rise to an unfavorable localization of the ground-state wave function. On the QuEra Aquila neutral atom machine, we observe such localization and its effect on the success probability of finding the correct solution to the encoded optimization problem. Finally, we propose quantum-aware modifications of the encoding scheme that avoid this quantum bottleneck and lead to an exponential improvement in the adiabatic performance. This highlights the crucial importance of accounting for quantum effects when designing strategies to encode classical problems onto quantum platforms. Published by the American Physical Society 2025

Bombieri, Lisa (ORCID:0009000950422897)↗
DOE OSTI2024

A windowed mean trajectory approximation for condensed phase dynamics

We propose a trajectory-based quasi-classical method for approximating dynamics in condensed phase systems. Building upon the previously developed optimized mean trajectory approximation that has been used to compute linear and nonlinear spectra, we borrow some ideas from filtering trajectory methods to obtain a novel semiclassical method for the dynamical propagation of density matrices. This new approximation is tested rigorously against standard multistate electronic models, spin-boson models, and models of the Fenna–Matthews–Olson complex. For dissipative systems, the current method is significantly better or as good as many other semiclassical methods available, especially at low temperatures and for off-diagonal density matrix elements, whereas for scattering models, the current method bears similar limitations as mean-field propagation schemes. All results are tested against the numerically exact hierarchical equations of motion method. In conclusion, the new method shows excellent agreement across various parameter regimes with numerically exact results, highlighting the robustness and accuracy of our approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Utilization of Existing Pipelines in Hydrogen Transport: Literature Review Report

This report critically reviews the flow behavior of hydrogen-natural gas (H 2 -NG) mixtures in pipelines and examines the critical factors of hydrogen integration into existing natural gas infrastructure. It addresses the choking behavior characterized by velocity increase and pressure drop, as well as the effects of flow restrictions and pressure losses during hydrogen transport. Computational and analytical models are used to investigate these effects, and their effects on thermodynamic properties and system performance are evaluated. The study also reviews the energy efficiency and flow dynamics of hydrogen and methane-hydrogen mixtures and optimizes the hydrogen flow rate. In addition, the effects of these mixtures on the flow characteristics are discussed in detail, with special emphasis on the compressibility factor (z factor) and fluid properties based on equations of state for hydrogen-natural gas mixtures. The study also analyzes the mixture ratios and highlights the thermophysical properties, flow dynamics, and hydrogen-blended natural gas application potential. These investigations assess flow stability, material interactions, and operational feasibility of transporting hydrogen mixtures through natural gas pipelines, which contribute to developing sustainable and efficient energy systems.

08 HYDROGEN↗
DOE OSTI2024

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid–base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH 3 ) is one of the most important industrial chemicals. The conventional NH 3 synthesis method-the Haber–Bosch process-converts atmospheric nitrogen (N 2 ) into NH 3 using H 2 with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH 3 yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH 3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N 2 , *H 2 , *NH, *NH 2 , and *NH 3 . Here, we generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH3 synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH 3 . While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH 3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Advanced Modeling of Beam Physics and Performance Optimization for Nuclear Physics Colliders

High energy colliders provide a critical tool in nuclear physics study by probing the fundamental structure and dynamics of matter. To maximize the potential of scientific discovery in nuclear physics study, it is important to optimize the parameters of these colliders to attain the best performance. The performance of a collider is typically measured by its integrated luminosity of colliding beams since the probability of a new event is proportional to the integrated luminosity. However, the achievable luminosity is limited by the electromagnetic interactions (beam-beam effects) of two colliding beams at higher energy, and the interplay between the space-charge effects and the beam-beam effects at lower energy. To achieve the best performance of a collider means to attain the highest luminosity of the collider with optimized collider parameters. Optimizing the collider’s machine parameters is both computationally and experimentally expensive. A fast and robust computational framework including beam-beam and space-charge effects will be critical to attaining the best performance of the collider. In this project, we will study the beam dynamics challenges, specifically the interplay of the space-charge and the beam-beam effects, and the machine tuning models for maximizing the performance of RHIC experiments. We will develop an advanced modeling framework based on first-principles physical simulations, lattice models and the state-of-the-art machine learning methods and apply this framework to performance improvement of the RHIC in operation. We will build data manipulation packages to connect the simulation data and the experimental data with the framework, develop a self-consistent hybrid model of space-charge and beam-beam effects, study underlying physics mechanisms, build surrogate models using the labeled data, integrate the models into the advanced modeling framework, and apply the framework to RHIC luminosity (STAR and sPHENIX) optimization. The success of this project would substantially improve the performance of existing and future colliders and increase the opportunity for scientific discovery.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Gramicidin and chlorhexidine encapsulated in bicontinuous microemulsions: Antimicrobial activity performance and their impact on self‐assembly

The utility of bicontinuous microemulsions (BMEs) as carriers of the antimicrobial peptide (AMP) gramicidin D and antiseptic chlorhexidine was investigated for possible topical delivery to chronic wounds. The two water-insoluble solutes dissolved in pre-formed one-phase BMEs of Water/Polysorbate 80/Limonene/Ethanol/Glycerol and Water/Aerosol-OT (AOT)/Polysorbate 85/Isopropyl Myristate and an AOT/Polysorbate 85 Winsor-III system, achieving gramicidin and chlorhexidine concentrations of 1.0 wt% and 0.5% individually and 0.5% and 0.3% in mixtures at 22°C, respectively. Small-angle neutron scattering measurements demonstrated that both solutes decreased surfactant interfacial activity and increased interfacial fluidity for the Polysorbate 80 system. For the AOT/Polysorbate 85 systems, ellipsoidal aggregates consisting of gramicidin and likely adsorbed surfactant and oil formed, while chlorhexidine enhanced the surface activity of surfactants. According to bioassays performed on artificial skin, the incorporation of melittin, gramicidin, and chlorhexidine in general enhanced the bioactivity of Polysorbate 80 BMEs for 24 h treatment against relevant antibiotic-resistant bacteria found on skin relative to controls. Yet, BME treatments were less effective than aqueous melittin control, in contrast to well diffusion bioassays performed previously. The results reflect the strong impact of AMPs and antiseptics on BME structure and dynamics and the complexity of formulating BMEs for optimal antimicrobial activity.

antimicrobial peptides↗
DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗
DOE OSTI2025

In situ high-temperature Raman spectroscopy for online EAF slag analysis

Real-time monitoring of slag chemistry is critical for optimizing Electric Arc Furnace (EAF) steelmaking operations, where dynamic variations in slag composition directly influence slag foaming, refractory degradation, and thermal efficiency. Conventional techniques such as X-ray fluorescence (XRF), Fourier-transform infrared (FTIR), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) are commonly used to analyze slag composition, but their offline nature and equipment constraints limit their applicability for online monitoring in harsh industrial environments. To address this challenge, we present an in situ, high-temperature analytical approach that integrates Raman spectroscopy with a custom designed fiber-optic probe for real-time slag characterization at 1550 °C. The system enables non destructive spectral acquisition from molten slags, providing molecular-level insights into silicate polymerization and iron oxidation states. Eight synthetic slag samples were evaluated, and key Raman features—such as Q n silicate units and FeO₄/FeO₆ coordination environments—were identified and quantitatively correlated with slag basicity and Fe₂O₃ content. The results demonstrate agreement between Raman spectral ratios and bulk slag chemistry, validating the method’s capability to track compositional and structural changes under molten temperature. This work establishes the feasibility of deploying fiber-optic Raman probe for online EAF slag monitoring and highlights their potential to support closed-loop control strategies, thereby enhancing process stability, refractory protection, and steel quality in industrial steelmaking applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Dual-Bed Radioiodine Capture from Complex Gas Streams with Zeolites: Regeneration and Reuse of Primary Sorbent Beds for Sustainable Waste Management

Dual-sorbent systems are proposed for radioiodine management with a regenerated primary bed for multiple cycles of use in complex conditions and a secondary bed for disposal with higher waste loadings. Sorbent approaches for the effective capture of gaseous radioiodine (isotopes 129 I and 131 I) produced from a range of nuclear processes have been studied for over half a century. (1−5) Whether or not a sorbent (e.g., molecular sieve) is required to physically screen/trap or chemically bind a radionuclide of interest through chemisorption, the complexity of the gas stream has a large impact on the performance (e.g., loading capacity, selectivity) and active life of a sorbent bed. (3) Silver mordenite (AgZ), the U.S. Department of Energy baseline sorbent for radioiodine capture from nuclear processes, performs well within acidic conditions and at elevated temperatures (6) and can be consolidated into a chemically durable waste form for long-term disposal. (7,8) However, new sorbents are being sought because optimal capture performance of AgZ significantly decreases in dynamic oxidizing environments with competing species, and it is expensive and it contains Ag (a toxic metal). (9) Until a new sorbent is found to replace AgZ, the regeneration and reuse of AgZ is an attractive alternative to a single-use primary sorbent bed. In this regard, a primary sorbent could be designed for enhanced capture in complex gas streams and the ability to be regenerated for reuse. Here, a secondary sorbent could then be tailored for maximum iodine loading in the gas stream and chemical durability within a disposal facility.

chemisorption↗