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DOE OSTI2026

Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation

Fibrillated cellulose derived from forestry feedstocks represents a renewable and high-strength materials platform for circular bioeconomies. However, its practical implementation is hindered by the irreversible aggregation of nanocellulose architectures, including cellulose nanofibers (CNFs). Solvent-based dispersion offers a simple and practical route to prevent CNF aggregation. Here, in this work, we integrate classical and enhanced sampling molecular dynamics (MD) simulations with experimental suspension rheology and atomic force microscopy (AFM) to elucidate how solvent environments tune CNF–CNF interactions and dispersion stability. CNF–CNF contact free energies computed from MD simulations reveal reduced aggregation in acetone/water, γ-valerolactone (GVL)/water, and tetrahydrofuran (THF)/water and pure acetone compared with pure water, reflecting stronger CNF-solvent relative to inter-CNF interactions. Correspondingly, CNF-solvent suspensions in these solvent systems exhibit stronger inter-fibril network structures and enhanced recovery compared to water, indicating improved CNF-solvent affinity. Liquid cell AFM imaging in acetone–water mixtures and in pure acetone further confirm the presence of well-dispersed CNFs. By combining multiscale computation with targeted experiments, this study establishes a rational framework for solvent design to achieve stable nanocellulose dispersions for high-strength biobased materials and efficient bioenergy conversion.

cellulose↗

DOE OSTI2025

A solvent-targeted recovery and precipitation scheme for the recycling of up to ten polymers from post-industrial mixed plastic waste

Solvent-Targeted Recovery and Precipitation (STRAP TM ) separates polymers within a plastic waste stream by selective dissolution. In this work, the STRAP framework, which combines computational modeling and experiments, was applied to develop a series of steps to separate up to 10 polymers from post-industrial mixed plastic waste (MPW) and the main components recovered were LDPE, HDPE, and PET. The STRAP steps were initially demonstrated with a physical polymer mixture containing LDPE, HDPE, PS, PVC, EVOH, PET, PP, PA6, PA66, and PA66/6, in which recoveries of 89% or higher were achieved for each polymer. Furthermore, this paper demonstrates a solvent selection approach that can be applied to separate unknown plastics materials into purer components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Extension of Solvent-Assisted Linker Exchange to Supported Metal–Organic Framework Thin Films

Solvent-assisted linker exchange (SALE) is a common postsynthetic approach used to accommodate metal–organic frameworks (MOFs) for targeted applications. As this technique is relatively untested in immobilized MOF structures, we repeated the generalized SALE approach with ZIF-8 deposited directly onto solid substrates and assessed the results with a variety of techniques, including time-of-flight secondary-ion mass spectrometry (ToF-SIMS). Our methods confirm that the supported films maintain their key structural properties while allowing for linker substitution with foreign species. Alongside experiments, we used computational modeling to quantify the relative energetics of SALE reactions with a variety of linkers. These techniques together reveal that SALE is translatable to immobilized MOFs. Additionally, we recognize how various aspects of the postsynthetic approach can bear consequences for the material’s final micro- and macroscopic properties. Furthermore, the results uncover basic yet critical details needed to inform future efforts to engineer MOF films with functional properties.

Ligands↗

DOE OSTI2026

Effect of Lithium Doping on MgO Hydroxylation and Carbonation

Recovery of magnesium from brines can potentially be used to source MgO (periclase) as a CO 2 sorbent or for Mg-based cements. However, it is not clear how common impurities in brines, such as lithium, affect the resulting MgO reactivity. Here, to test the effect of lithium incorporation on MgO reactivity for hydration and carbonation, we combined computational simulations with experiments. Experimentally altered (Mg,Li)O with a low dopant concentration (0.012 ± 0.002% w/w Li) was characterized using synchrotron-based X-ray scattering and high-resolution electron microscopy to measure reaction layer formation on (Mg,Li)O. Single-crystal X-ray diffraction analysis of (Mg,Li)O demonstrates that the incorporation of lithium leads to the formation of oxygen vacancies. The presence of vacancies is likely causing faster hydroxylation rates as predicted by ab initio molecular dynamics simulations. However, the faster hydroxylation rates likely lead to faster passivation of the surface because we observe thinner reaction layers on (Mg,Li)O samples both over short time periods (30 days) and over long time periods (28 years). After 28 years, the reaction layer on the (Mg,Li)O sample was less than one-third of the thickness of that of the pure MgO sample. In addition, over 30 days, reaction layers on (Mg,Li)O samples primarily formed at steps rather than on terraces, in contrast to our previous observations on MgO. Based on our results, naturally occurring impurities in MgO modify its reactivity even at very low concentrations and need to be considered for accurate reaction rate prediction for application of MgO as a CO 2 sorbent or in cements.

MgO↗

DOE OSTI2025

Curiosity driven exploration to optimize structure–property learning in microscopy

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Gaussian process based surrogate approach for the optimization of cylindrical targets

Simulating direct-drive inertial confinement experiments presents significant computational challenges, both due to the complexity of the codes required for such simulations and the substantial computational expense associated with target design studies. Machine learning models, and in particular, surrogate models, offer a solution by replacing simulation results with a simplified approximation. In this study, we apply surrogate modeling and optimization techniques that are well established in the existing literature to one-dimensional simulation data of a new cylindrical target design containing deuterium–tritium fuel. These models predict yields without the need for expensive simulations. We find that Bayesian optimization with Gaussian process surrogates enhances sampling efficiency in low-dimensional design spaces but becomes less efficient as dimensionality increases. Nonetheless, optimization routines within two-dimensional and five-dimensional design spaces can identify designs that maximize yield, while also aligning with established physical intuition. Optimization routines, which ignore constraints on hydrodynamic instability growth, are shown to lead to unstable designs in 2D, resulting in yield loss. However, routines that utilize 1D simulations and impose constraints on the in-flight aspect ratio converge on novel cylindrical target designs that are stable against hydrodynamic instability growth in 2D and achieve high yield.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Expansion dynamics of femtosecond laser-induced plasmas: Influence of thermophysical plasma properties

This study investigates the expansion dynamics of femtosecond laser-induced plasmas, emphasizing the impact of plasma thermophysical properties and ambient gas composition. Through shadowgraphy experiments and multiphase computational fluid dynamics (CFD) simulations, the influence of parameters such as heat capacity, molecular weight, and thermal conductivity on plume morphology, shockwave evolution, and energy dissipation mechanisms is examined. A mixture multiphase model is implemented to capture the interaction between the plasma and the surrounding gas. Simulation results reveal that plasma expansion is strongly inertia-driven. Results show that differences in plasma properties and ambient conditions affect the shape and temperature distribution of the expanding plume. The early-stage dynamics are primarily dictated by pressure forces, whereas thermal and viscous effects play a growing role in the plume's behavior during later stages of expansion. The CFD findings show the necessity of accurate initial condition characterization, including crater geometry and plasma pressure and temperature, for reliable modeling of plasma evolution in laser ablation processes.

CFD modeling↗

DOE OSTI2025

Photoselective isotope fractionation dynamics of N 2 with cosmo and atmospheric chemistry perspectives

Stable isotope ratio measurements provide valuable insights into a broad range of natural processes, from planetary atmospheres and climate to interstellar chemistry. Nitrogen, which has two stable isotopes, exhibits varying isotope ratios across the solar system. To model these observations, the isotope fraction as a function of energy is essential. At the Advanced Light Source (ALS), we measured the photodissociation of molecular nitrogen (N 2 ) with vacuum UV photons where a single photon is sufficiently energetic to dissociate the strong bond. The nitrogen atoms produced are scavenged with H 2 to form ammonia, whose isotopic makeup is determined. Blending the experiments with dynamical computations that include the shielding of light, we examine the isotopic composition and electronic atomic states produced. The measured photodissociation of N 2 at a natural isotopic composition with a frequency broad light beam exceptionally strongly favors the formation of the heavier nitrogen isotope, 15 N. Computations concur and suggest that the maximum in the quantum yield reflects significant variations in the specific electronic quantum states of the product N atoms that have quite different reactivities. Our quantum computations show that at similar energies, photodissociation of 14 N 14 N and 15 N 14 N can lead to different product channels. The computed dynamics include extensive state-selective spin–orbit and nonadiabatic couplings affecting the light absorption and dissociation pathways that proceed via the triplet manifold of states. Our results are relevant for future exploration missions, both in situ and sample-return and for other molecules such as O 2 and CO.

Komarova, Ksenia [Hebrew Univ. of Jerusalem (Israe↗

DOE OSTI2025

Exploring the relation between transonic dislocation glide and stacking fault width in FCC metals

Theory predicts limiting gliding velocities that dislocations cannot overcome. Computational and recent experiments have shown that these limiting velocities are soft barriers and dislocations can reach transonic speeds in high rate plastic deformation scenarios. In this paper we systematically examine the mobility of edge and screw dislocations in several face centered cubic (FCC) metals (Al, Au, Pt, and Ni) in the extreme large-applied-stress regime using molecular dynamics simulations. Our results show that edge dislocations are more likely to move at transonic velocities due to their high mobility and lower limiting velocity than screw dislocations. Importantly, among the considered FCC metals, the dislocation core structure determines the dislocation’s ability to reach transonic velocities. This is likely due to the variation in stacking fault width due to relativistic effects near the limiting velocities.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Granger causal inference for climate change attribution

Abstract Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and focus on different types of weather and climate events and each of which are conditional to varying extents. Some methods are based on Pearl causality (direct experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses relative to Pearl-causal attribution and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.

Risser, Mark D. (ORCID:0000000319561783)↗

DOE OSTI2024

Future of plasma etching for microelectronics: Challenges and opportunities

Plasma etching is an essential semiconductor manufacturing technology required to enable the current microelectronics industry. Along with lithographic patterning, thin-film formation methods, and others, plasma etching has dynamically evolved to meet the exponentially growing demands of the microelectronics industry that enables modern society. At this time, plasma etching faces a period of unprecedented changes owing to numerous factors, including aggressive transition to three-dimensional (3D) device architectures, process precision approaching atomic-scale critical dimensions, introduction of new materials, fundamental silicon device limits, and parallel evolution of post-CMOS approaches. The vast growth of the microelectronics industry has emphasized its role in addressing major societal challenges, including questions on the sustainability of the associated energy use, semiconductor manufacturing related emissions of greenhouse gases, and others. The goal of this article is to help both define the challenges for plasma etching and point out effective plasma etching technology options that may play essential roles in defining microelectronics manufacturing in the future. The challenges are accompanied by significant new opportunities, including integrating experiments with various computational approaches such as machine learning/artificial intelligence and progress in computational approaches, including the realization of digital twins of physical etch chambers through hybrid/coupled models. These prospects can enable innovative solutions to problems that were not available during the past 50 years of plasma etch development in the microelectronics industry. To elaborate on these perspectives, the present article brings together the views of various experts on the different topics that will shape plasma etching for microelectronics manufacturing of the future.

Engineering↗

DOE OSTI2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment

Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. However, existing simulators suffer from limited scalability, hardwired algorithms, lack of real-time monitoring, and inability to generate datasets suitable for modern machine learning approaches. We present CGSim, a simulation framework for large-scale distributed computing environments that addresses these limitations. Built upon the validated SimGrid simulation framework, CGSim provides high-level abstractions for modeling heterogeneous grid environments while maintaining accuracy and scalability. Key features include a modular plugin mechanism for testing custom workflow scheduling and data movement policies, interactive real-time visualization dashboards, and automatic generation of event-level datasets suitable for AI-assisted performance modeling. We demonstrate CGSim’s capabilities through a comprehensive evaluation using production ATLAS PanDA workloads, showing significant calibration accuracy improvements across WLCG computing sites. Scalability experiments show near-linear scaling for multi-site simulations, with distributed workloads achieving 6 × better performance compared to single-site execution. The framework enables researchers to simulate WLCG-scale infrastructures with hundreds of sites and thousands of concurrent jobs within practical time budget constraints on commodity hardware.

Vatsavai, Sairam Sri [Brookhaven National Laborato↗

DOE OSTI2025

Relativistic Magnetic Reconnection in Astrophysical Plasmas: A Powerful Mechanism of Nonthermal Emission

Magnetic reconnection—a fundamental plasma physics process, where magnetic field lines of opposite polarity annihilate—is invoked in astrophysical plasmas as a powerful mechanism of nonthermal particle acceleration, able to explain fast-evolving, bright high-energy flares. Near black holes and neutron stars, reconnection occurs in the relativistic regime, in which the mean magnetic energy per particle exceeds the rest mass energy. This review reports recent advances in our understanding of the kinetic physics of relativistic reconnection (RR): ▪ Kinetic simulations have elucidated the physics of plasma heating and nonthermal particle acceleration in RR. ▪ The physics of radiative RR, with its self-consistent interplay between photons and reconnection-accelerated particles—a peculiarity of luminous, high-energy astrophysical sources—is the new frontier of research. ▪ RR plays a key role in global models of high-energy sources, in terms of both global-scale layers and reconnection sites generated as a by-product of local magnetohydrodynamic instabilities. We summarize themes of active investigation and future directions, emphasizing the role of upcoming observational capabilities, laboratory experiments, and new computational tools.

acceleration of particles↗

DOE OSTI2024

Spatially Resolved Near Field Spectroscopy of Vibrational Polaritons at the Small N Limit

Vibrational polaritons, which have been primarily studied in Fabry–Pérot cavities with a large number of molecules (N ~ 10 6 –10 10 ) coupled to the resonator mode, exhibit various experimentally observed effects on chemical reactions. However, the exact mechanism is elusively understood from the theoretical side, as the large number of molecules involved in an experimental strong coupling condition cannot be represented completely in simulations. This discrepancy between theory and experiment arises from computational descriptions of polariton systems typically being limited to only a few molecules, thus failing to represent the experimental conditions adequately. To address this mismatch, we used surface phonon polariton (SPhP) resonators as an alternative platform for vibrational strong coupling. SPhPs exhibit strong electromagnetic confinement on the surface and thus allow for coupling to a small number of molecules. As a result, this platform can enhance nonlinearity and slow down relaxation to the dark modes. In this study, we fabricated a pillar-shaped quartz resonator and then coated it with a thin layer of cobalt phthalocyanine (CoPc). By employing scattering-type scanning near-field optical microscopy (s-SNOM), we spatially investigated the dependency of vibrational strong coupling on the spatially varying electromagnetic field strength and demonstrated strong coupling with 38,000 molecules only–reaching to the small N limit. Through s-SNOM analysis, we found that strong coupling was observed primarily on the edge of the quartz pillar and the apex of the s-SNOM tip, where the maximum field enhancement occurs. In contrast, a weak resonance signal and lack of coupling were observed closer to the center of the pillar. This work demonstrates the importance of spatially resolved polariton systems in nanophotonic platforms and lays a foundation to explore polariton chemistry and chemical dynamics at the small N limit–one step closer to reconcile with high-level quantum calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Using Gamification to Enhance Mastery of Network Security Concepts

Gamification has proven to be effective in engaging and encouraging people to work towards and achieve goals. Many students struggle to focus on schoolwork, due to a lack of interest, lack of understanding, or other factors unique to the student. Applying gamification elements to education can help engage these students in learning their course material and help them excel academically. This study examines the effectiveness of using gamification techniques to enhance the learning experience in college Computer Science courses. A video game application is utilized to review and reinforce cybersecurity concepts that students have already been taught in class. Previous work has been made on a prototype game build that teaches about ARP (Address Resolution Protocol) components. The focus of this study is to refine and develop the structure of the prototype into a more interactive and enjoyable format with non-competitive and captivating activities that allow students to study at their own pace. An updated version of the game was created that focused on reaching a balance between education and entertainment. The game was used by students enrolled in a cybersecurity class, where pre-survey, post-survey and a focus group interview were conducted to determine how effective the updated version is compared to the current build, in addition to how effective the gamification method is regarding student retention of taught material. The pre-survey and post-survey results revealed an increase in interest and mastery of cybersecurity concepts as a result of playing the game. Students found value in the game as both a method of reviewing material taught in class and an entertaining and engaging game. These results show potential in using gamification in cybersecurity and education.

Hilliard, Kevin↗

DOE OSTI2024

Towards Autonomous Experiments by Connecting High Performance Microscopy with High Performance Computing

The digitization of controls, data, and analysis in microscopy is bringing the idea of autonomous microscopes closer to reality than ever before. Automated transmission electron microscopy (TEM) is already fairly routine for some experiments the only require simple repetitive tasks such as imaging biological macromolecules for single particle cryoEM [1], tilt series for electron tomography [2], and movies for crystallography [3]. The vast majority of TEM experiments are conducted completely by human operators who choose the regions of interest, optimize experimental parameters, and make decisions about data quality visually during an experiment. The field is still a long way from having completely autonomous TEMs that can adapt to sample difficulties and tune experimental parameters based on data quality and desired experimental outcomes. Part of the issue is the lack of capability for feeding information learned from on-line, live data analysis back into the on-going experiment [4]. Furthermore, this presentation will discuss current capabilities for large scale data reduction and analysis using high performance computing (i.e. supercomputing) and progress towards developing a true feed-back loop that places data analysis and theory in the experimental loop.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

PQML: Enabling the Predictive Reproducibility on NISQ Machines for Quantum ML Applications

Quantum computing represents a groundbreaking approach to high-performance computing. In recent years, quantum computers have progressed from single-qubit processors to systems boasting over 400 qubits. The presence of such a large number of qubits offers significant advantages, including enhanced computational speed—a capability beyond classical computing methods. However, the current stage of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era. The existence of noise in this era presents challenges in testing quantum computing applications, leading to considerable variance in application results. Furthermore, the diverse noise characteristics observed across different machines exacerbate this issue, complicating the selection of the appropriate machine for application execution. In response to these challenges, we introduce our Predictive Quantum Machine Learning (PQML) tool. This tool is designed to predict outcomes when executing identical quantum machine learning applications—specifically, a critical suite of variational quantum algorithms—across various quantum computers during the NISQ era. This effort relies on data collected over a 12-month period. To the best of our knowledge, this study represents the first attempt to ensure reproducibility across quantum computers for complex circuits. Additionally, we have developed a model capable of forecasting the accuracy of quantum computers for variational quantum algorithms, with a particular emphasis on quantum machine learning as a case study.

Senapati, Priyabrata [Kent State University]↗

DOE OSTI2024

Non-smooth Bayesian optimization in tuning scientific applications

Tuning algorithmic parameters to optimize the performance of large, complicated computational codes is an important problem involving finding the optima and identifying regimes defined by non-smooth boundaries in black-box functions. Within the Bayesian optimization framework, the Gaussian process surrogate model produces smooth mean functions, but functions in the tuning problem are often non-smooth, which is exacerbated by the fact that we usually have limited sequential samples from the black-box function. Here, motivated by these issues encountered in tuning, we propose a novel Gaussian process model called a clustered Gaussian process (cGP), where the components are dynamically updated by clustering. In our studies, the performance of cGP can be better than stationary GPs in nearly 90% of the experiments and better than non-stationary GPs in nearly 70% of the repeated experiments while requiring less computational cost. cGP provides a novel approach for dynamic GP, computes more efficiently than recursive partitioning, and discovers non-smoothness regimes. We provide extensive experiments including high-performance computing (HPC) and industrial simulation functions to show the effectiveness of our methods.

97 MATHEMATICS AND COMPUTING↗