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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Classifying topology in photonic crystal slabs with radiative environments

Abstract In the recent years, photonic Chern materials have attracted substantial interest as they feature topological edge states that are robust against disorder, promising to realize defect-agnostic integrated photonic crystal slab devices. However, the out-of-plane radiative losses in those photonic Chern slabs has been previously neglected, yielding limited accuracy for predictions of these systems’ topological protection. Here, we develop a general framework for measuring the topological protection in photonic systems, such as in photonic crystal slabs, while accounting for in-plane and out-of-plane radiative losses. Our approach relies on the spectral localizer that combines the position and Hamiltonian matrices of the system to draw a real-picture of the system’s topology. This operator-based approach to topology allows us to use an effective Hamiltonian directly derived from the full-wave Maxwell equations after discretization via finite-elements method (FEM), resulting in the full account of all the system’s physical processes. As the spectral FEM-localizer is constructed solely from FEM discretization of the system’s master equation, the proposed framework is applicable to any physical system and is compatible with commonly used FEM software. Moving forward, we anticipate the generality of the method to aid in the topological classification of a broad range of complex physical systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

A curated benchmark for cofolding models on kinase conformational states

Abstract Protein kinases are critical drug targets, requiring therapeutics that can modulate their active and inactive conformational states. While cofolding models can generate global folds directly from kinase sequences and ligand SMILES strings, these models have not yet been tested on their ability to recover ligand-induced-fit conformational states of the kinase proteins. Here, we introduce KinConfBench, a curated benchmark of 2225 high-quality human kinase chains to evaluate the ability of four state-of-the-art cofolding models—Boltz-2, Chai-1, Protenix, and RoseTTAFold-All-Atom—to recover both canonical and rare conformational states. We show that geometric success metrics of a ligand pose in the active site do not correlate strongly with the correct kinase conformational state, motivating a new set of dynamical benchmarks for assessing cofolding models. While all four cofolding models achieve ~60–80% prediction accuracy for kinase conformational classification, they exhibit severe mode collapse when performing multiple inferences, show negligible structural diversity in sampling induced-fit motions, and display a prevalent “apo-drift” in which most cofolding models predominantly predict the kinase to be in its ligand-free state. Our results highlight that capturing ligand-induced protein conformational diversity, not just geometric fit, is critical for next-generation structure-based drug discovery.

Sun, Kunyang↗
DOE OSTI2020

Li-rich cathodes for rechargeable Li-based batteries: reaction mechanisms and advanced characterization techniques

Due to their high specific capacities beyond 250 mA h g -1 , lithium-rich oxides have been considered as promising cathodes for the next generation power batteries, bridging the capacity gap between traditional layered-oxide based lithium-ion batteries and future lithium metal batteries such as lithium sulfur and lithium air batteries. However, the practical application of Li-rich oxides has been hindered by formidable challenges. To address these challenges, the understanding of their electrochemical behaviors becomes critical and is expected to offer effective guidance for both materials and cell development. This work aims to provide fundamental insights into the reaction mechanisms, electrochemical challenges and modification strategies of lithium-rich oxides. We first summarize the research history, the pristine structures, and the classification of lithium-rich oxides. Then we review the critical reaction mechanisms that are closely related to their electrochemical features and performances, such as lattice oxygen oxidation, oxygen vacancy formation, transition-metal migration, layered to spinel transitions, ‘two-phase mechanism’, and lattice evolution. These discussions are coupled with state-of-the-art characterization techniques. As a comparison, the anionic redox reactions of layered sodium transition metal oxides are also discussed. Finally, after a brief overview of the correlation among the aforementioned mechanisms, we provide perspectives on the rational design of lithium-rich oxides with high energy densities and long-term cycling stability.

25 ENERGY STORAGE↗
DOE OSTI2020

Solvent selection for biphasic extraction of 5-hydroxymethylfurfural via multiscale modeling and experiments

We introduce a comprehensive conceptual framework for selecting solvents for reactive extraction in biphasic organic-water systems and demonstrate it for the separation of HMF (5-hydroxylmethylfurfural), a platform chemical produced in the acid-catalyzed dehydration of hexoses. We first perform in silico screening of ~2500 solvents, from the ADFCRS-2018 database using the ADF COSMO-RS implementation, and classification, based on the solvent partition coefficient. We then determine experimentally the partition coefficients for HMF, fructose, and products of HMF rehydration (levulinic acid (LA), and formic acid (FA)), the mutual water-organic solvent solubilities, and the separation factors in >50 select solvents spanning multiple homologous series at room temperature and a typical reaction temperature with in situ sampling. We find that COSMO-RS is excellent for screening purposes (typical error in most cases within a factor of ~2). Increased temperatures lead to significant reduction in partitioning, and room temperature measurements are clearly inadequate for solvent selection. Upon down selecting classes of solvents based on separation performance, we perform experimental thermal stability and reaction compatibility studies of a small set of solvents at relevant reactive-extraction temperatures. We discover that many substituted phenols exhibit an order-of-magnitude increase in partitioning compared to conventional solvents due chiefly to hydrogen bond interactions and show the necessary stability but retain a significant fraction of water and LA, factors that need to be considered in technoeconomic analysis. In contrast, anilines, aldehydes, and acids are good to excellent regarding separation but incompatible with this specific reaction medium. Furthermore, this multifaceted framework can be extended to other biomass-derived products and processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Understanding, quantifying, and controlling the molecular ordering of semiconducting polymers: from novices to experts and amorphous to perfect crystals

Molecular packing and texture of semiconducting polymers are often critical to the performance of devices using these materials. Although frameworks exist to quantify the ordering, interpretations are often just qualitative, resulting in imprecise use of terminology. Here, we reemphasize the significance of quantifying molecular ordering in terms of degree of crystallinity (volume fractions that are ordered) and quality of ordering and their relation to the size scale of an ordered region. We are motivated in part by our own imprecise and inconsistent use of terminology in the past, as well as the need to have a primer or tutorial reference to teach new group members. We strive to develop and use consistent terminology with regards to crystallinity, semicrystallinity, paracrystallinity, and related characteristics. To account for vastly different quality of ordering along different directions, we classify paracrystals into 2D and 3D paracrystals and use paracrystallite to describe the spatial extent of molecular ordering in 1–10 nm. We show that a deeper understanding of molecular ordering can be achieved by combining grazing-incidence wide-angle X-ray scattering and differential scanning calorimetry, even though not all aspects of these measurements are consistent, and some classification appears to be method dependent. We classify a broad range of representative polymers under common processing conditions into five categories based on the quantitative analysis of the paracrystalline disorder parameter (g) and thermal transitions. A small database is presented for 13 representative conjugated and insulating polymers ranging from amorphous to semi-paracrystalline. Lastly, we outline the challenges to rationally design more perfect polymer crystals and propose a new molecular design approach that envisions conceptual molecular grafting that is akin to strained and unstrained hetero-epitaxy in classic (compound) semiconductors thin film growth.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep learning for characterizing the self-assembly of three-dimensional colloidal systems

Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems’ stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. In this work, we first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Microfluidic synthesis of multilayered lipid–polymer hybrid nanoparticles for the formulation of low solubility drugs

Hybrid phospholipid/block copolymer membranes where polymers and lipids are molecularly mixed or phase-separated into polymer-rich and lipid-rich domains are promising drug delivery materials. Harnessing the chemical diversity of polymers and the biocompatability of lipids is a compelling approach to design the next generation of drug carriers. Here, we report on the development of a microfluidics-based strategy analogous to produce lipid nanoparticles (LNPs) for the nanomanufacturing of multilayered hybrid nanoparticles (HNPs). In conclusion, using X-ray scattering, Cryo-electron, and polarized microscopy we show that phosphatidylcholine (PC) and PBD-b-PEO (poly(butadiene-block-ethylene oxide)) hybrid membranes can be nanomanufactured by microfluidics into HNPs with dense and multilayered cores which are ideal carriers of low-solubility drugs of the Biopharmaceutical Classification System (BCS) II and IV such as antimalarial DSM265 and Paclitaxel, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Advances in metal–organic framework-based hydrogel materials: preparation, properties and applications

Although metal–organic frameworks (MOFs) with intriguing physiochemical properties have recently attracted increasing attention of researchers, MOFs have not adequately achieved various applications due to their instability and unsatisfactory processability. Hence, MOF-based composites as well as structurally novel MOFs have been established to solve this challenge. Recently, some efforts have been made for the development of MOF-based hydrogels. They exhibit excellent properties compared to initial MOFs in many ways (e.g., mechanical strength, absorption capacity and total pore volume). In this review, the up-to-date research progress of MOF-based hydrogels will be briefly summarized. Here, we mainly focus on expounding the classification of MOF-based hydrogels and their related synergistic effects. As to details, we will provide a conspectus of laconic and latest approaches for the synthesis of MOF-based hydrogels, focusing on composite systems, mechanism research, and stability. In addition, the applications of MOF-based hydrogels have been widely discussed, particularly in the environmental, energy and medical aspects. It is hoped that the detailed description of this review can help researchers working on MOF-based hydrogels to develop more novel composite materials and investigate their wider application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Review of low-cost self-driving laboratories in chemistry and materials science: the “frugal twin” concept

This review proposes the concept of a “frugal twin,” similar to a digital twin, but for physical experiments. Frugal twins range from simple toy examples to low-cost surrogates of high-cost research systems. For example, a color-mixing self-driving laboratory (SDL) can serve as a low-cost version of a costly multi-step chemical discovery SDL. Frugal twins already provide hands-on experience for SDLs with low costs and low risks. They can also offer as test beds for software prototyping (e.g., optimization, data infrastructure), and a low barrier to entry for democratizing SDLs. However, there is room for improvement. The true value of frugal twins can be realized in three core areas. Firstly, hardware and software modularity; secondly, purpose-built design (human-inspired vs. hardware-centric vs. human-in-the-loop); and thirdly state-of-the-art (SOTA) software (e.g., multi-fidelity optimization). We also describe the ethical benefits and risks that come with the democratization of science through frugal twins. For future work, we suggest ideas for new frugal twins, SDL educational course outcomes, and a classification scheme for autonomy levels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Metal–support interactions in metal oxide-supported atomic, cluster, and nanoparticle catalysis

Supported metal catalysts are essential to a plethora of processes in the chemical industry. The overall performance of these catalysts depends strongly on the interaction of adsorbates at the atomic level, which can be manipulated and controlled by the different constituents of the active material (i.e., support and active metal). The description of catalyst activity and the relationship between active constituent and the support, or metal–support interactions (MSI), in heterogeneous (thermo)catalysts is a complex phenomenon with multivariate (dependent and independent) contributions that are difficult to disentangle, both experimentally and theoretically. So-called “strong metal–support interactions” have been reported for several decades and summarized in excellent review articles. However, in recent years, there has been a proliferation of new findings related to atomically dispersed metal sites, metal oxide defects, and, for example, the generation and evolution of MSI under reaction conditions, which has led to the designation of (sub)classifications of MSI deserving to be critically and systematically evaluated. These include dynamic restructuring under alternating redox and reaction conditions, adsorbate-induced MSI, and evidence of strong interactions in oxide-supported metal oxide catalysts. Here, we review recent literature on MSI in oxide-supported metal particles to provide an up-to-date understanding of the underlying physicochemical principles that dominate the observed effects in supported metal atomic, cluster, and nanoparticle catalysts. Critical evaluation of different subclassifications of MSI is provided, along with discussions on the formation mechanisms, theoretical and characterization advances, and tuning strategies to manipulate catalytic reaction performance. We also provide a perspective on the future of the field, and we discuss the analysis of different MSI effects on catalysis quantitatively.

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

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2024

Assessment of fine-tuned large language models for real-world chemistry and material science applications

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens doors to a wider chemical audience, as field-specific featurization techniques can be omitted. In this work, we explore the potential and limitations of this approach. We studied the performance of fine-tuning three open-source LLMs (GPT-J-6B, Llama-3.1-8B, and Mistral-7B) for a range of different chemical questions. We benchmark their performances against “traditional” machine learning models and find that, in most cases, the fine-tuning approach is superior for a simple classification problem. Depending on the size of the dataset and the type of questions, we also successfully address more sophisticated problems. The most important conclusions of this work are that, for all datasets considered, their conversion into an LLM fine-tuning training set is straightforward and that fine-tuning with even relatively small datasets leads to predictive models. These results suggest that the systematic use of LLMs to guide experiments and simulations will be a powerful technique in any research study, significantly reducing unnecessary experiments or computations.

Van Herck, Joren↗
DOE OSTI2022

Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security

Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber-threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real-time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross-layer approach. In this paper a cross-layered strategy is presented, called Cross-Layer Ensemble CorrDet with Adaptive Statistics(CECD-AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter-arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD-AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1-score compared to current approaches that only use SG measurement data for detection such as the traditional physics-based State Estimation, Ensemble CorrDet with Adaptive Statistics strategy and other machine learning classification-based detection schemes.

cyber-physical security↗
DOE OSTI2020

Strong dependence of Type Ia supernova standardization on the local specific star formation rate

As part of an on-going effort to identify, understand and correct for astrophysics biases in the standardization of Type Ia supernovae (SN Ia) for cosmology, we have statistically classified a large sample of nearby SNe Ia into those that are located in predominantly younger or older environments. This classification is based on the specific star formation rate measured within a projected distance of 1 kpc from each SN location (LsSFR). This is an important refinement compared to using the local star formation rate directly, as it provides a normalization for relative numbers of available SN progenitors and is more robust against extinction by dust. We find that the SNe Ia in predominantly younger environments are Δ Y = 0.163 ± 0.029 mag (5.7 σ ) fainter than those in predominantly older environments after conventional light-curve standardization. This is the strongest standardized SN Ia brightness systematic connected to the host-galaxy environment measured to date. The well-established step in standardized brightnesses between SNe Ia in hosts with lower or higher total stellar masses is smaller, at Δ M = 0.119 ± 0.032 mag (4.5 σ ), for the same set of SNe Ia. When fit simultaneously, the environment-age offset remains very significant, with Δ Y = 0.129 ± 0.032 mag (4.0 σ ), while the global stellar mass step is reduced to Δ M = 0.064 ± 0.029 mag (2.2 σ ). Thus, approximately 70% of the variance from the stellar mass step is due to an underlying dependence on environment-based progenitor age. Also, we verify that using the local star formation rate alone is not as powerful as LsSFR at sorting SNe Ia into brighter and fainter subsets. Standardization that only uses the SNe Ia in younger environments reduces the total dispersion from 0.142 ± 0.008 mag to 0.120 ± 0.010 mag. Overall, we show that as environment-ages evolve with redshift, a strong bias, especially on the measurement of the derivative of the dark energy equation of state, can develop. Fortunately, data that measure and correct for this effect using our local specific star formation rate indicator, are likely to be available for many next-generation SN Ia cosmology experiments.

79 ASTRONOMY AND ASTROPHYSICS↗