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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Convective shells in the interior of Cepheid variable stars: Overshooting models based on hydrodynamic simulations

Context. Because Cepheid variable stars have long been used as a cosmic benchmark for scaling distances in our Galaxy and beyond, the accuracy of stellar evolution models for Cepheids have wide-reaching effects. However, our understanding of the dynamics in the interiors of these physically complex stars is limited. Aims. Our goal is to provide a detailed multi-dimensional picture of hydrodynamic convection and convective boundary mixing in the interior of Cepheids. Methods. Using the Modules for Experiments in Stellar Astrophysics (MESA), we studied the structure of intermediate-mass stars that cross the instability strip. Then, we performed two-dimensional hydrodynamic simulations of six stars with the fully compressible Multidimensional Stellar Implicit Code (MUSIC). Our simulations did not model the radial pulsations but focused on the interior structure of this family of stars. We developed and applied a new statistical analysis to examine convection and convective boundary mixing in the interior of these stellar simulations. Results. Based on a grid of MESA models, we demonstrated that a common structure for intermediate mass Cepheids includes an interior convective shell as well as a thin outer convective envelope. Using the extreme value theory approach to analyze our MUSIC simulation data, we found that overshooting above the convective shell fills the space between these convectively unstable layers. We developed a new statistical analysis that provides a clearer picture of how overshooting fills this layer; it also allowed us to formulate a detailed comparison between overshooting above and below the convective shell. Our analysis effectively decomposes the overshooting layer into two layers: a weak overshooting layer and a strong overshooting layer. Statistically, this is accomplished by decomposing the strongly non-Gaussian probability density function into a mixture of gamma distributions. Using our mixture model, we showed that the ratio of overshooting lengths above and below the convective shell depends directly on the radial extent of the convective shell as well as its depth in the star. We proposed a new form for the diffusion coefficient that addresses the need for overlapping overshooting layers between convective shells. We introduced the idea of a “super-mixing layer” where overshooting from both the convective shell and the convective envelope results in efficient mixing and could be viewed as merging the two adjacent convective zones.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Non-equilibrium simulations of hydraulic permeation: Role of mechanical boundary conditions in dense membranes

Understanding the mechanisms of water transport in reverse osmosis membranes is critical for improving membrane performance and guiding material design. While classical models describe transport as either solution-diffusion (SD)—involving concentration-driven diffusion through a homogeneous medium—or pore-flow (PF)—involving pressure-driven convection through percolated water channels—their applicability to crosslinked polyamide membranes remains debated. Here, using non-equilibrium molecular dynamics simulations, we investigate the impact of mechanical support conditions on pressure-driven water transport in polyamide membranes across varying crosslink densities and pressure differentials (1000–5000 bar). Two support conditions are considered: graphene-restrained, representing experimentally relevant supported membranes, and freeze-restrained, mimicking a self-supported structure. In graphene-restrained systems, water concentration gradients and constant pressure profiles emerge, consistent with SD theory and incompatible with PF assumptions due to the absence of percolated pores and sub-nanometer voids. In contrast, freeze-restrained systems display uniform water concentration and linearly decreasing pressure at 1000 bar, and exhibit compressibility-induced water gradients and partial percolation at 5000 bar, resembling PF-like behavior. However, the underlying assumptions of PF theory—continuous solvent pathways and pressure transmission through water-filled pores—are not met under most conditions. Our results demonstrate that accurate modeling of reverse osmosis membranes must incorporate realistic mechanical boundary conditions to distinguish between transport mechanisms. For dense polyamide membranes supported by porous substrates, graphene-restrained simulations best reflect experimental setups and support the SD model as the dominant mechanism of water permeation.

molecular dynamics↗
DOE OSTI2025

Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control

On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.

Connected and automated vehicles↗
DOE OSTI2026

Pythia8 Quark and Gluon Jets (float16)

A float16 (half-precision) version of the quark and gluon jet dataset originally published by Komiske, Metodiev, and Thaler (Zenodo record 3164691). Only the 20-file subset without charm and bottom quark jets is included here. All simulation parameters and jet selection criteria are identical to the original: Pythia 8.226, √s = 14 TeV Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos FastJet 3.3.0, anti-k_t jets with R = 0.4 p_T^jet ∈ [500, 550] GeV, |y^jet| < 1.7 There are 20 files, each in compressed NumPy format (QG_jets_fp32_0.npz through QG_jets_fp32_19.npz). Each file contains two arrays: X: (100000, M, 4) — 50k quark and 50k gluon jets, randomly sorted, padded to max multiplicity M, with particle features (pt, rapidity, azimuthal angle, pdgid) stored as float32 y: (100000,) — jet labels, gluon = 0, quark = 1 The original dataset stores X in float64. Here X has been cast to float16, approximately halving file size. The y labels are unchanged. Users should be aware that float16 has limited dynamic range and precision. If you use this dataset, please cite the original Zenodo record and its associated paper: Komiske, Metodiev, Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165

energyflow↗
DOE OSTI2025

Spectral broadening and pulse shaping in the deep ultraviolet

Here, we report the generation and shaping of ∼20 femtosecond pulses in the deep ultraviolet (DUV). Our approach combines spectral broadening in a stretched hollow-core fiber with compression and shaping via an acousto-optic modulator-based pulse shaper. The pulse shaper allows for automatic compression, useful for in-air delivery of pulses to two-dimensional (2D) spectroscopy and other time-resolved experiments. Unlike previous DUV pulse generation schemes that lack phase stability, our method enables programmable generation of phase-locked pulse-pairs with attosecond-level stability. We demonstrate pulse characterization via self-diffraction frequency-resolved optical gating and dispersion scan methods in the DUV and validate performance through a pump-probe experiment resolving electronic dynamics on attosecond timescales.

Codere, Julia [Stony Brook Univ., NY (United State↗
DOE OSTI2025

Quantum Computing Strategy 2026

Quantum computing (QC) is a rapidly maturing technology with the potential for revolutionary impacts on stockpile stewardship science and national security. Recent developments in fault-tolerant architectures have compressed vendor roadmaps, and predictions of a production-ready quantum computer by the mid-2030s are becoming increasingly credible. This strategy provides a roadmap for integrating QC into the Advanced Simulation and Computing (ASC) program by investing in four strategic focus areas: 1. Develop Capabilities in Mission-Relevant Quantum Applications: ASC will prioritize developing quantum-ready applications in mission areas that have shown significant promise for quantum advantage, including simulations of materials in extreme environments, nuclear dynamics, solving linear and nonlinear partial differential equations, and uncertainty quantification. These applications directly support stockpile stewardship science and modernization objectives. 2. Conduct R&D in Algorithms, Software, and Hardware: Sustained research into quantum algorithms, robust software tools, and quantum hardware is essential. ASC will develop efficient quantum algorithms; invest in quantum compilers, debuggers, and performance tools; and explore specialized quantum hardware tailored to NNSA’s unique requirements. 3. Engage with Vendors and Partners: Early and active collaboration with commercial quantum hardware vendors and academic partners is critical. Through testbeds, co-design agreements, and quantum demonstration facilities, ASC will influence hardware design, gain early access to emerging technologies, and ensure that quantum platforms evolve to meet mission needs. 4. Build Knowledge, Experience, and Workforce: Expanding and upskilling the quantum-trained workforce is essential to long-term success. This includes hiring, internal training, university outreach, and postdoctoral support to ensure ASC maintains the expertise required to operate, program, and integrate quantum systems as they become available. While quantum computing will never replace classical computing, it has the potential to solve certain problems with speed and accuracy that would be unachievable using any conceivable classical high-performance computing (HPC) system. By investing strategically in QC, ASC will help propel the emergent QC industry, maintain U.S. technological leadership, ensure mission readiness, and position itself to rapidly adopt quantum technologies as they mature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Development, Verification, and Validation of an OpenFOAM-Based Solver for Modeling Inertial Fusion Energy Chambers

Our work seeks to introduce a computational tool tailored to the physics of inertial fusion energy chambers, in particular, those concepts based on thick liquid walls. In this approach, the structural materials are protected by several neutron mean-free-paths of renewable liquid and thus will be able to survive much longer than un-shielded walls, with virtually all structures lasting for the life of the plant and enabling the use of commercially available and qualified materials. The OpenFOAM-based solver named rhoCentralFoam has been used as a starting point. rhoCentralFoam belongs to the standard OpenFOAM solver toolset. It is a high-speed, explicit compressible flow solver with shock-capturing capability. While the main features have been retained, the solver had to be restructured to make use of tabular data for equations of states, a necessary addition to model the complex thermo-physical properties of ionized gasses. This entailed the need to change the independent state variables used by the solver, resulting in a new thermodynamic library and slightly different solution algorithm. Moreover, a radiation heat transfer model based on the P-1 approximation was added to the solver. The solver is verified against an analytical solution from the Sedov-Taylor-Neumann test problem to showcase the ability of the hydrodynamic solvers to handle strong shocks, whereas the P-1 model was verified using a simple one-dimensional problem with an analytical solution. Additionally, a validation case involving shock-wave propagation through jet array is presented, and the results are compared with experimental data from the open literature. Lastly, in order to showcase the utility of the solver for practical cases, we applied the refined solver to two representative scenarios: gas venting within the HYLIFE-II chamber and the compression of the gas following the partial ablation of the liquid wall.

Chamber dynamics↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Review of Modeling Approaches for Predicting Frost Growth and Defrosting on Tube-Fin Heat Exchangers: Preprint

Frost formation and growth on the evaporator surface is a common process that deteriorates the air-refrigerant heat transfer and restricts airflow. This degrades the performance of the vapor compression system by increasing temperature lift and air-side pressure drop. To accurately predict these effects during coil frosting, as well as the energy use and duration of the defrost process, there is a need to estimate the heat and mass transfer, momentum transport, and solid-liquid and liquid-vapor phase change. Therefore, in the past few decades, continuous effort has been made to model frosting and defrosting processes using approaches ranging from empirical correlations to computational fluid dynamic models. To provide a clearer overview for researchers, engineers, and manufacturers in this field, this paper provides a comprehensive literature review for frosting and defrosting models. The paper begins with theoretical background of frost formation and defrost processes, and then reviews the common modeling approaches in literature and their underlying assumptions when trying to account for various physical phenomenon. Based on the literature review, the most critical modeling effort for frost formation is the determination of frost densification rate and frost growth rate. Various methods to predict these two parameters are reviewed. Empirical correlations commonly used for frost density and thermal conductivity are presented and compared. For the defrost process, various multi-stage models have been proposed with different assumptions. Some assume the presence of air gap between the tube wall and the frost, while others consider the melted frost flow due to gravity. We also review physics-based and empirical approaches to integrate defrost models into heat pump models. We conclude by identifying research gaps and providing recommendations.

defrost↗
DOE OSTI2025

Unraveling the role of temperature on the onset of ejecta formation at atomic scales

Shock wave interactions with perturbations on a free surface can lead to the inversion and growth of the perturbation and eventual ejection of a jet of material, referred to as an ejecta microjet, from shocked surfaces. Here, this study carries out large-scale molecular dynamics (MD) simulations using single-crystal Cu and Sn systems with a pre-existing groove to characterize the localized gradients in temperatures and pressures generated that render the microjet formation. MD simulations are carried out for three loading orientations (along the [001], [011], and [111] directions) and shock pressures ranging from 16 GPa to 100 GPa to understand the role of temperatures generated at the groove vertex on the formation of ejecta. The simulations suggest that the interaction of the shock wave with the groove results in a localized increase in temperatures, leading to localized softening at the groove vertex and the generation of an ejecta microjet. For Cu systems, the simulations suggest that the orientation effects on shock wave structures, velocities, and localized softening affect the ejecta formation at low pressures. Jetting is only observed when temperatures at the groove vertex are high enough to induce localized softening (close to melting temperature), and the jet velocity increases with shock pressure. In contrast, the loading orientation is rendered inconsequential for Sn systems due to the melting of the material during shock compression at the pressures chosen. The jet velocities are similar, regardless of crystal orientation in Sn systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conductive Liquid Metal Vitrimer Composites for Reconfigurable and Recyclable Flexible Electronics

Liquid metal (LM) elastomer composites exhibit excellent functionality for stretchable electronics and wearables, but limited recycling and reuse pathways constrain their sustainable use. Here, to address these challenges amid growing concerns over electronic waste, a conductive LM–vitrimer composite is presented that enables recyclable and reconfigurable electronics. This soft and stretchable composite features uniformly distributed LM inclusions that enhance thermal conductivity by 6.53× and enable the formation of conductive traces with electrical self-healing, while the vitrimer provides structural restoration. The dynamic covalent bonds of the vitrimer matrix are leveraged for both reprocessing the composite and chemically recovering 94% of the LM. This liquid-state filler slightly reduces the vitrimer's stiffness to 2.63 MPa (≈20% lower), while maintaining its high stretchability (>135% strain) and thermal stability. It is further examined how ultrasonicated LM inclusions interact with the vitrimer matrix and demonstrate the composite's self-healing and recyclability through two distinct approaches: 1) thermomechanical reprocessing, which restores fragmented composites under heat and compression for circuit reconfiguration; and 2) chemical recycling, which recovers the embedded LM for reuse in fabricating new composites and redesigned circuitry. With the integration of recyclability and diverse functional capabilities, LM–vitrimer composites emerge as a promising material platform for sustainable, flexible electronics.

Han, Youngshang [Univ. of Washington, Seattle, WA ↗
DOE OSTI2025

DEDUPKV: A Space-Efficient and High-Performance Key-Value Store via Fine-Grained Deduplication

Log-Structured Merge Tree (LSM-tree) based key-value stores excel in write-intensive environments but suffer from data duplication, consuming up to 49% of storage space in LSM-tree-based key-value store deployments. Traditional solutions like compression and coarse-grained file system-level deduplication introduce overhead or have limited effectiveness. In this study, we propose DedupKV, a fine-grained deduplication framework tailored for LSM-tree, maximizing data reduction efficiency while minimizing write stalls and read overheads. DedupKV features three key innovations: (1) FLUSH-integrated inline deduplication, which removes duplicates during memory-to-storage writes; (2) WAL file-based offline deduplication, repurposing write-ahead logs to avoid double writes; and (3) elastic execution, dynamically balancing inline and offline deduplication based on memory pressure and workload intensity. Additionally, dynamic granularity management reduces deduplication metadata overhead. We implemented these four ideas in RocksDB for the first time and conducted experiments in a Linux environment. Our evaluation shows that WAL file-based offline deduplication and DedupKV outperform BlobDB by 33% and 23%, respectively, in write-heavy workloads, while reducing write amplification by 1.2 ×, 2 ×, and 1.6 × for real KV datasets.

Jamil, Safdar [Sogang University]↗
DOE OSTI2026

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.

deep learning↗
DOE OSTI2025

Quantum Information for Fusion Energy Sciences (Final Technical Report)

The simulation of plasma dynamics is a critical area of Fusion Energy Sciences (FES) due to it’s usefulness in predicting, controlling, and confining plasmas in the context of potential fusion reactors. The simulation of plasmas is a computationally difficult problem in both classical and quantum physics, motivating investigation into the potential of quantum computers to simulate these systems. This project took several concrete steps towards this goal by developing tools for improving the control, characterization, and calibration of quantum gates on a superconducting quantum computer, developing error suppression and mitigation tools to reduce errors on the quantum computer, and utilizing these advancements to simulate reduced models of plasma dynamics on the quantum computer. In order to efficiently simulate plasma physics, an optimal control method which synthesizes, directly at the pulse level, any quantum gate on qubit and qutrit systems was developed. Using four superconducting transmon quantum processors at Rigetti and LLNL, it was demonstrated that any arbitrary quantum gate on qubits and qutrits could be implemented with high fidelity, leading to a significantly reduced length of a gate sequence. A problem of interest in FES is the nonlinear optical process of laser pulse compression within a plasma. Since quantum physics is linear, simulating nonlinear operations is not naturally feasible on a quantum computer, however it is possible to simulated a quantized version of the nonlinear process. A quantization approach to convert nonlinear wave-wave interaction problems to Hamiltonian simulation problems was developed and demonstrated using two qubits on a Rigetti device. In this experiment, a number of error suppression and mitigation techniques were investigated to determine how best to utilize the finite quantum resources. This study provides an example of how plasma problems may be solved on near-term, noisy quantum computing platforms and identified a promising set of techniques. Building on the insights of these experiments, the investigation turned to linear electron-plasma wave physics. A connection was identified between a local one-dimensional lattice spin model and linear wave phenomena, allowing a plasma physics problem to be efficiently mapped to the quantum computer. In this framework, reflection and transmission of plasma waves at a sharp boundary was studied, as well as the propagation of waves through an inhomogeneous plasma medium. In addition to the suite of error suppression and mitigation techniques developed, this experiment introduced the use of a digital-analog gate scheme designed to efficiently simulate the plasma Hamiltonian. With hardware available at the conclusion of the project, simulation at the scale of 9 qubits and 15 timesteps (60 entangling layers) was achieved.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Multi-messenger dynamic imaging of laser-driven shocks in water using a plasma wakefield accelerator

Understanding dense matter hydrodynamics is critical for predicting plasma behavior in environments relevant to laser-driven inertial confinement fusion. Traditional diagnostic sources face limitations in brightness, spatiotemporal resolution, and in their ability to detect relevant electromagnetic fields. In this work, we present a dual-probe, multi-messenger laser wakefield accelerator platform combining ultrafast X-rays and relativistic electron beams at 1 Hz, to interrogate a free-flowing water target in vacuum, heated by an intense 200 ps laser pulse. This scheme enables high-repetition-rate tracking the evolution of the interaction using both particle types. Betatron X-rays reveal a cylindrically symmetric shock compression morphology assisted by low-density vapor, resembling foam-layer-assisted fusion targets. The synchronized electron beam detects time-evolving electromagnetic fields, uncovering charge separation and ion species differentiation during plasma expansion – phenomena not captured by photons or hydrodynamic simulations. We show that combining both probes provides complementary insights spanning kinetic to hydrodynamic regimes, highlighting the need for hybrid physics models to accurately predict fusion-relevant plasma behavior.

imaging techniques↗
DOE OSTI2025

Phenomena Identification and Ranking Table (PIRT) for heat pipes

Heat pipes are advanced passive thermal management devices that utilize phase change and capillary action to achieve efficient heat transfer. However, due to the complexity of the phenomena coupled in heat pipes, including capillary, phase change, turbulence, and compressibility effects, there are high uncertainties in the predictability of their operational regimes and performance. This PIRT exercise, conducted as a collaborative effort involving the Department of Energy (DOE) Microreactor Program (MRP), the Nuclear Regulatory Commission (NRC), and university partners systematically identifies, reviews, and prioritizes critical phenomena affecting the operation of heat pipes based on their importance and knowledge levels. Additional analyses and discussion are provided for phenomena with high importance and low knowledge, such as wick de-wetting, critical heat flux, contact angles, and pressure dynamics. The discussions included the recognizing challenges and proposing future research directions for both modeling and simulation and experimental efforts. Additionally, the report addresses phenomena with medium importance and low knowledge that could impact heat pipe operation during non-normal or transient operation, including frozen startup, laminar to turbulent transition, geyser boiling, wick priming, underfilling conditions, surface roughness of the wick, NCGs trapped in the wick, and the timescales of startup and shutdown. In conclusion, this comprehensive evaluation serves as a valuable resource for guiding future research and development efforts, supporting the successful integration of heat pipes into critical applications such as nuclear reactors, and contributing to the advancement of heat pipe technologies in safety-critical industries.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Pressure-induced structural phase transitions in CrSBr

There is growing interest in combining chemical complexity with external stimuli like pressure, field, and light for property control in van der Waals solids. This is because extreme conditions trigger the development of new states of matter and functionality. In this work, we bring together synchrotron-based infrared absorption, Raman scattering, and diamond anvil cell techniques with first-principles calculations of the lattice dynamics and energy landscape to reveal the series of structural phase transitions in CrSBr. By tracking how the phonons change under pressure, we uncover a remarkable chain of complex symmetry modifications, interlayer interactions, and chemical reactions. A group-subgroup analysis suggests that CrSBr undergoes an orthorhombic Pmmn → monoclinic P2/m transition at 7.6 GPa, and based upon a comparison with model oxychlorides like FeOCl and CrOCl, we propose that changes in the pendant halide groups drive the system to a P2 1 /m-like space group above 15.3 GPa. Compression above 20.2 GPa is irreversible, resulting in the formation of an entirely new compound that is metastable for months. This work opens the door to the use of pressure and possibly strain to control the properties of CrSBr.

Microscopy↗
DOE OSTI2026

Effects of confinement and pressure on the structure and dynamics of carbon dioxide in silica slit pores

An understanding of carbon dioxide fluid properties within geological mesopores is important in applications ranging from carbon sequestration to shale oil recovery. Here, a molecular dynamics study is presented that aims to shed light on these systems by simulation of CO 2 fluid confined in β-cristobalite silica slit pores with different pore widths and pressure conditions. The weakly associating nature of carbon dioxide leads to little difference in structural and interfacial dynamical properties for different pore sizes and pressures. Rather, the behavior is found to be dominated by the entropic effects, namely, how the CO 2 organizes next to the silica surface. The CO 2 self-diffusion coefficient shows the strongest pore size dependence. It is strongly diminished in small pores and does not reach the bulk fluid value even in 6 nm pores. It also decreases with pressure, yielding an activation volume that increases with pore size. These results provide new insight into the behavior of a compressible fluid in nanoscale confinement.

Godahewa, Sahan M. [Univ. of Kansas, Lawrence, KS ↗