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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Impact of electrolyte solutions on carbon dioxide fixation in single chamber Al–CO 2 battery

Governments and research & development (R&D) organizations are actively initiating various programs and research strategies for CO 2 capture, its utilization, and integration with long duration energy storage from renewable sources worldwide. In line with the carbon capture goals, here we report a novel electrochemical Al-CO 2 battery cell, that can simultaneously capture CO 2 and convert it into value-added products, in addition to long-duration energy generation and storage. Here, this innovative approach employs cost-effective Al metal as an anode and an in-house synthesized Ni–Fe based bimetallic double hydroxide catalyst as the cathode, with meticulously optimized compositions and morphologies. We explore the impact of different aqueous electrolyte solutions compositions on the cell performance, demonstrating up to 10 h of stable long duration energy storage with a stable voltage profile. The cell exhibits low polarization even at high current densities of up to 12 mA cm -2 and maintains stable cycling over 500 h. Through Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray Diffraction and X-ray photoelectron spectroscopy (XPS) analysis, we determined that the discharge product is either NaAlCO 3 (OH) 2 or KAlCO 3 (OH) 2 , distinct from the Al 2 (CO 3 ) 3 typically reported in conventional Al–CO 2 batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

Fast Fourier transform evaluation of the Fresnel integral for gravitational-wave lensing

Gravitational waves (GWs) exhibit wave-optics effects when their wavelength is comparable to the scale of the gravitational lens. This may occur in lensing from galactic subhalos in GWs emitted by binary black-hole mergers and is gaining interest as a novel probe of dark matter. Predictions for observables in these cases ultimately rely on evaluating a Fresnel integral that quantifies the effect of lensing on the amplitude of a GW at a given frequency. However, numerical evaluation of this Fresnel integral is tricky, and several algorithms and publicly available codes that implement it have been developed. Here, we show that the dependence of this integral on the lens position can be written as a two-dimensional Fourier transform. Modern FFT techniques then enable rapid evaluation at all-sky positions simultaneously for general lenses without symmetry. Vectorization of FFT routines allows for derivatives with respect to model parameters to be obtained with only incremental additional computational cost. If the lens is axisymmetric, further speedups can be achieved with recently developed techniques for nonuniform fast Hankel transforms. To demonstrate, we make available Fresnel Integral Optimization with Nonuniform Transforms (fiona), an efficient and accurate code that is significantly faster than current methods for dense source grids, reaching 2 orders of magnitude speedups for ∼10 6 GW-emitting points. As part of FIONA , we developed code that provides vectorized nonuniform fast Hankel transforms that may have other uses (e.g., calculation of cosmological two-point correlation functions) beyond those considered here.

dark matter↗
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2023

Magnetic design of the commutational magnet and quadrupoles for PERLE accelerator

ER@CEBAF is an effort to demonstrate multi-GeV multipass energy recovery with a low beam current in CEBAF. The CEBAF race-track-shaped geometry allows its linacs to accommodate multiple energy beams simultaneously. However, five energy recovery passes complicate the beamline optics design process. Individual recirculating arcs each transport one beam energy, and are shared between accelerating/decelerating beams. This paper presents a CEBAF optics redesign to accommodate the ER@CEBAF multi-pass ER scheme. Isochronous arcs were retuned to match into optics solutions for optimized 10-pass linacs. In this paper, we discuss the optics re-design process for ER@CEBAF based on the existing CEBAF layout.

Neththikumara, I.↗
DOE OSTI2021

Demonstration of Dimensional and Microstructural Postirradiation Examination Capabilities on MiniFuel Disk Specimens

This work demonstrates two new MiniFuel postirradiation examination (PIE) capabilities, laser profilometry and serial sectioning, for future use on alpha-U specimens for the upcoming Mini-99 irradiation campaign. Representative U–Mo disk specimens—3 mm in diameter and approximately 0.75 mm in thickness—were subjected to dimensional measurements using standard calipers, as well as a new Keyence CL-3000 series laser profilometry system designed to measure the surface of both sides of the disk simultaneously. The laser profilometry system captured thickness gradients across the specimens as large as 0.2 mm. Additionally, because of the limitations inherent to the use of calipers, caliper measurements consistently overestimated the initial volume of MiniFuel disk specimens compared to the laser profilometry method by up to 5%. However, the results show that optimal data collection and analysis procedures are crucial to avoiding significant artifacts during data collection that can lead to large errors (approximately 10%). Serial sectioning using a combination of ion milling and electron imaging was also demonstrated for the analysis of micron-sized control volumes. Nanometer- to micron-scale porosity was resolved on a representative U–Mo specimen before irradiation, even when the surface of the specimen appeared free of porosity. Future efforts to relate microstructural features (porosity, fission product distributions, and so on) to macroscale effects (dimensional changes, thermal conductivity degradation) require accurate characterization methods such as those demonstrated in this work. These advanced PIE capabilities will be applied to optimized alpha-U specimens for the upcoming Mini-99 neutron irradiation campaign.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗
DOE OSTI2025

Enhancing The Thermal Resistivity of Rigid Polyisocyanurate Foam Insulation

The development of rigid polyurethane foam insulation has garnered considerable attention because of its promising applications in the buildings and construction industry. Its low thermal conductivity makes it an attractive choice for improving energy performance in buildings. Current Rigid Polyurethane foams have thermal resistivity (R-value/in.) 5.5 to 6.5 h.ft2F/BTU/in.· that could be further improved by diminishing the heat transfer through the foam matrix. Nevertheless, minimizing both conduction (through gas and solid) and radiation simultaneously in porous solids is a significant challenge due to the trade-off between these two mechanisms. This study focuses on improving the R-value of the insulation foams via several strategies: such as type and the amount of the surfactants, blowing agent content and precooling and premixing polyol mixture. These methods optimized thermal properties of the PIR foams, achieving R/in. as high as 8.3. This excellent R/in. is anticipated to be a critical factor in significantly advancing the thermal insulation performance of rigid polyurethane cellular foams, thereby enhancing their efficacy in energy-efficient building applications.

Wanasinghe Mudiyanselage Pahala Gedara, Shiwanka V↗
DOE OSTI2025

Estimation of intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method

Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.

Indicator↗
DOE OSTI2023

A Multi-objective Microgrid Assessment and Sizing Framework for Economic and Resilience Benefits

This paper presents an innovative multi-objective assessment and sizing framework for battery energy storage system paired with photovoltaics for a cost-effective and resilient microgrid. In the proposed framework, we simultaneously consider i) economic benefits from bundling grid services in grid-connected mode and ii) enhanced resilience by operating the microgrid in island mode upon outages of the main grid. Instead of simply treating resilience as another monetary value stream, this paper models resilience and economic benefits as two separate objectives. Multi-objective optimization is developed to explore different sizing and dispatch options and generate the Pareto front to assist decision-making in system design. Case studies on a real-world microgrid project in Puget Sound Energy system are presented to illustrate the proposed modeling and sizing methods.

Battery energy storage, Microgrid, Optimal Sizingp↗
DOE OSTI2022

Parallel Multigrid in Time and Space for Extreme-Scale Computational Science: Chaotic and Hyperbolic Problems

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallelin-time methods, with the most common method, parareal, diverging in many cases. The current stateof-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, we take inspiration from the Least Squares Shadowing (LSS by Wang) approach for solving chaotic problems. Here, an optimization approach is able to find “well-conditioned” shadow trajectories/solutions to the original “ill-conditioned” chaotic problem. Thus, the new multigrid methods researched here also arise in an optimization context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multigrid Reduction in Time for Chaotic and Hyperbolic Problems (Final Report)

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallelin-time methods, with the most common method, parareal, diverging in many cases. The current state-of-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, this work shall continue researching the directions from the current collaboration with Dr. Falgout, which are laid out in the work Toward Parallel in Time for Chaotic Dynamical Systems and showed the first known results of a parallel-in-time speedup for a chaotic problem. This work outlines two key improvements to XBraid for chaotic problems, the so-called “theta” and “delta-correction” methods. Here, these two improvements will be implemented in a high-performance but general way in XBraid and explored for more complicated problems. We will additionally research, as time allows, improvements to these techniques, as well as multigrid relaxation techniques based on Least Squares Shadowing (LSS by Wang) and a nonintrusive block tridiagonal solver based on MGRIT, called TriMGRIT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multigrid Reduction in Time for Chaotic and Hyperbolic Problems (Final Report)

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallel-in-time methods, with the most common method, parareal, diverging in many cases. The current state of-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, this work shall continue researching the directions from the current collaboration with Dr. Falgout, which are laid out in the work Toward Parallel in Time for Chaotic Dynamical Systems and showed the first known results of a parallel-in-time speedup for a chaotic problem. This work outlines two key improvements to XBraid for chaotic problems, the so-called “theta” and “delta-correction” methods. Here, these two improvements will be further researched and improved (including with a new relaxation method inspired by on Least Squares Shadowing (LSS)) and explored for more complicated problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Recent advances in nano-scale spatial resolution x-ray microscopy instrumentation at NSLS-II

X-ray microscopy is an invaluable and powerful characterization tool applied in many scientific fields, such as materials science, biology, environmental science, and energy research. In recent years it has been driven by rapid developments of novel technologies and systems resulting in imaging experiments elucidating structural inhomogeneities and chemical reactions at the nanometer scale. To obtain high spatial resolution comprehensive chemical and structural information, an X-ray microscope must be equipped with adequate capabilities and allow for simultaneous acquisition of multiple datasets. In recent years, a number of X-ray microscopes have been designed, constructed, and commissioned at NSLS-II. Here we provide an overview of the microscopy instrumentation development program at NSLS-II and specifically focus on the multilayer Laue lens-based hard X-ray nanoprobe optimized for ~10 nm spatial resolution imaging, its current status, and future upgrades along with recently constructed Kirkpatrick-Baez based scanning microscope designed for ~100 nm spatial resolution experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Alpha-Imaging Detector System Development for Large Area Monitoring

Effective management and disposal of legacy nuclear waste are essential for ensuring safe work environments and minimizing environmental impacts. Monitoring airborne actinide contamination is particularly critical due to the high internal dose potential of alpha-emitting radionuclides. Traditional continuous air monitoring systems (CAMs) used in the industry are limited in the volume of air they can sample, potentially leading to inaccurate radiation detection over large areas. For example, in 2018, elevated levels of airborne Plutonium-239 were detected beyond the controlled areas of the Hanford Plutonium Finishing Plant, highlighting the potential risks to both plant workers and nearby residents. To address these challenges, high-efficiency particulate absorbing (HEPA) air purifiers can enhance air flow by up to 1.5 orders of magnitude, thereby increasing monitoring efficiency and providing a cost-effective solution for large-area surveillance. To quantify the activities of alpha-emitting radionuclides on HEPA filters, the Savannah River National Laboratory is developing an advanced alpha-imaging detection system. This system includes scintillating materials combined with a digital scientific camera. A significant concern in operating such a large-area airborne monitoring system is the handling of HEPA filters, which may be contaminated with radioactive particles. To mitigate these hazards, it is crucial to ensure that any contamination is securely fixed onto the filters. Efforts have been made to optimize the sensitivity of scintillator-epoxy composites and apply them to HEPA filters. These materials were characterized using fluoroscence spectroscopy. These techniques confirmed the purity of the raw materials, the dispersion of scintillators in the epoxy matrix, and the stability of their optical and structural properties post-modification. The optimal scintillator-epoxy composite was selected for use on alpha-spiked HEPA filters to evaluate the efficiency of the sprayer. HEPA filters, embedded with alpha particles collected by an air purifier deployed in an airborne radiation area, have been tested to assess detection efficiency. Future work will focus on employing multiple imaging sensors simultaneously to enhance sensitivity across different regions of the HEPA filter.

Pham, Phuong [Savannah River National Laboratory (↗
DOE OSTI2023

Fine‐Tuning Alkyl Chains on Quinoxaline Nonfullerene Acceptors Enables High‐Efficiency Ternary Organic Solar Cells with Optimizing Molecular Stacking and Reducing Energy Loss

Abstract Material design of guest acceptor is always a big challenge for improving the efficiency of ternary organic solar cells (OSCs). Here, a pair of isomeric nonfullerene acceptors based on quinoxaline core, Qx–p‐C 7 H 8 O and Qx–m‐C 7 H 8 O, is designed and synthesized. By moving the alkoxy chain attached on side phenyl frommeta‐position topara‐position, both π–π stacking distance and crystallinity are enhanced simultaneously. They obtain the uplifted lowest unoccupied molecular orbital level. Compared to Qx–m‐C 7 H 8 O, Qx–p‐C 7 H 8 O exhibits wider absorption spectrum and higher extinction coefficient. Using D18‐Cl:N3 as host materials, the addition of guest acceptor Qx–p‐C 7 H 8 O significantly improves the power conversion efficiency (PCE) from 17.61% to 18.49% because of higher open‐circuit voltage (0.875 V) and short‐circuit current density (27.85 mA cm −2 ). This can be attributed to the faster exciton dissociation, more balanced carrier mobility, fine fiber morphology, and lower energy loss in the ternary devices. However, Qx–m‐C 7 H 8 O‐based ternary device achieves relatively low PCE of 17.17% because this device shows extremely low electron mobility. The results indicate that molecular stacking, film morphology, etc., can be effectively modulated by fine‐tuning the side chains of guest materials, which may be an effective design rule for further improving the PCE of OSCs.

Chemistry↗
DOE OSTI2023

Ferroelectric Domain Wall Engineering Enables Thermal Modulation in PMN–PT Single Crystals

Acting like thermal resistances, ferroelectric domain walls can be manipulated to realize dynamic modulation of thermal conductivity ( k ), which is essential for developing novel phononic circuits. Despite the interest, little attention has been paid to achieving room-temperature thermal modulation in bulk materials due to challenges in obtaining a high thermal conductivity switching ratio ( k high / k low ), particularly in commercially viable materials. Here, room-temperature thermal modulation in 2.5 mm-thick Pb(Mg 1/3 Nb 2/3 )O 3 – x PbTiO 3 (PMN– x PT) single crystals is demonstrated. With the use of advanced poling conditions, assisted by the systematic study on composition and orientation dependence of PMN– x PT, a range of thermal conductivity switching ratios with a maximum of ≈1.27 is observed. Further, simultaneous measurements of piezoelectric coefficient (d 33 ) to characterize the poling state, domain wall density using polarized light microscopy (PLM), and birefringence change using quantitative PLM reveal that compared to the unpoled state, the domain wall density at intermediate poling states (0< d 33 33,max ) is lower due to the enlargement in domain size. At optimized poling conditions (d 33,max ), the domain sizes show increased inhomogeneity that leads to enhancement in the domain wall density. This work highlights the potential of commercially available PMN– x PT single crystals among other relaxor-ferroelectrics for achieving temperature control in solid-state devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor-critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection-diffusion field. As a result, we provide explanation for the policy behaviors using knowledge of the underlying physical process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Metal-facilitated, sustainable nitroarene hydrogenation under ambient conditions

Hydrogenation is a critical reaction in the chemical industry, yielding a range of important compounds such as fine chemicals, pharmachemicals and agrochemicals. However, conventional hydrogenation typically requires pressurized hydrogen, high temperatures and involves noble metal catalysts. Here, we proposed a two-step hydrogenation process, utilizing water as the hydrogen source for the industrially important reduction of nitroarenes to anilines. A metal or reduced metal oxide, which can be obtained from solar thermal or electrochemical reduction, acts as the active site for nitrobenzene adsorption, H 2 O dissociation and in-situ hydrogen generation. Among the 15 metal and reduced metal oxides investigated, Zn and Sn emerged as highly efficient catalysts for the reduction of a broad range of organic nitro compounds under mild conditions, with H 2 utilization efficiency 1-2 orders of magnitude above the state-of-the-art. The presented protocol provides extra dimensions for designing and optimizing conventional hydrogenation process with an alternative pathway. The reactive hydrogen atoms generated in-situ effectively overcome the barriers associated with hydrogen gas dissolution and its subsequent dissociation on the catalyst surface, thereby greatly enhancing the overall effectiveness for the hydrogenation reaction. This research potentially establishes a sustainable, generally applicable alternative to conventional hydrogenation methods, simultaneously presenting a viable solution for renewable energy storage.

25 ENERGY STORAGE↗