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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Soil minerals affect taxon-specific bacterial growth

Abstract Secondary minerals (clays and metal oxides) are important components of the soil matrix. Clay minerals affect soil carbon persistence and cycling, and they also select for distinct microbial communities. Here we show that soil mineral assemblages—particularly short-range order minerals—affect both bacterial community composition and taxon-specific growth. Three soils with different parent material and presence of short-range order minerals were collected from ecosystems with similar vegetation and climate. These three soils were provided with 18O-labeled water and incubated with or without artificial root exudates or pine needle litter. Quantitative stable isotope probing was used to determine taxon-specific growth. We found that the growth of bacteria varied among soils of different mineral assemblages but found the trend of growth suppression in the presence of short-range order minerals. Relative growth of bacteria declined with increasing concentration of short-range order minerals between 25–36% of taxa present in all soils. Carbon addition in the form of plant litter or root exudates weakly affected relative growth of taxa (p = 0.09) compared to the soil type (p < 0.01). However, both exudate and litter carbon stimulated growth for at least 34% of families in the soils with the most and least short-range order minerals. In the intermediate short-range order soil, fresh carbon reduced growth for more bacterial families than were stimulated. These results highlight how bacterial-mineral-substrate interactions are critical to soil organic carbon processing, and how growth variation in bacterial taxa in these interactions may contribute to soil carbon persistence and loss.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Microbially induced precipitation of silica by anaerobic methane-oxidizing consortia and implications for microbial fossil preservation

Authigenic carbonate minerals can preserve biosignatures of microbial anaerobic oxidation of methane (AOM) in the rock record. It is not currently known whether the microorganisms that mediate sulfate-coupled AOM—often occurring as multicelled consortia of anaerobic methanotrophic archaea (ANME) and sulfate-reducing bacteria (SRB)—are preserved as microfossils. Electron microscopy of ANME-SRB consortia in methane seep sediments has shown that these microorganisms can be associated with silicate minerals such as clays [Chen et al., Sci. Rep. 4, 1–9 (2014)], but the biogenicity of these phases, their geochemical composition, and their potential preservation in the rock record is poorly constrained. Long-term laboratory AOM enrichment cultures in sediment-free artificial seawater [Yu et al., Appl. Environ. Microbiol. 88, e02109-21 (2022)] resulted in precipitation of amorphous silicate particles (~200 nm) within clusters of exopolymer-rich AOM consortia from media undersaturated with respect to silica, suggestive of a microbially mediated process. The use of techniques like correlative fluorescence in situ hybridization (FISH), scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS), and nanoscale secondary ion mass spectrometry (nanoSIMS) on AOM consortia from methane seep authigenic carbonates and sediments further revealed that they are enveloped in a silica-rich phase similar to the mineral phase on ANME-SRB consortia in enrichment cultures. Like in cyanobacteria [Moore et al., Geology 48, 862–866 (2020)], the Si-rich phases on ANME-SRB consortia identified here may enhance their preservation as microfossils. Finally, the morphology of these silica-rich precipitates, consistent with amorphous-type clay-like spheroids formed within organic assemblages, provides an additional mineralogical signature that may assist in the search for structural remnants of microbial consortia in rocks which formed in methane-rich environments from Earth and other planetary bodies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Opportunities for enhancing MLCommons efforts while leveraging insights from educational MLCommons earthquake benchmarks efforts

MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need for benchmark carpentry for scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection called cloudmesh with its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).

58 GEOSCIENCES↗
DOE OSTI2023

Chapter 14: Surface plasmon resonance enhanced artificial photosynthesis of chemical fuels for energy storage

Nanostructured noble metals such as Au, Ag, and Cu have interesting optical properties because of the oscillation motions of their surface electrons upon strong coupling with light under resonance conditions. This resonant oscillation motion of conduction electrons refers to surface plasmon resonance (SPR) and localized SPR (LSPR) when localized near the surface of a nanoparticle. The extinction spectrum of a solution of plasmonic nanoparticles has tunable wavelength responses from UV to NIR due to strong light scattering and absorption which are highly sensitive to the permittivity of the nanoparticles, their sizes and shapes, and chemical environment. Strong light scattering due to the LSPR of plasmonic nanoparticles creates a strong localized and far-field intensity capable of enhancing light absorption characteristics of a chromophore near a plasmonic surface. Engineering the chromophores’ radiative decay dynamics can be done by 1) increasing its radiative decay rate to increase its photoluminescence intensity and 2) increasing its nonradiative decay rates associated to direct charge transfer to the metal surface. Such interesting photophysical properties of a chromophore can be extended to other light-absorbing materials such as semiconductor thin films and nanostructures. This plasmonic effect on the photophysics of a light-absorbing material can be theoretically and experimentally validated. The phenomenon has also been applied to advanced optoelectronic devices such as organic light-emitting diodes (OLED)1 and organic photovoltaics (OPV).2-4 The local field created by the SPR can provide an intense EM field to enhance photoluminescence emission of an organic chromophore5-8 and Raman scattering of an organic molecule, and single-molecule Raman9-10 can be detected on specially designed LSPR substrate. (Figure 1 on SPR for energy) Recent studies suggest that LSPR can be incorporated in light-harvesting and conversion systems to increase energy conversion in a solar cell and photoelectrochemical cell and chemical transformations of CO2 to chemical fuels.11-12 Plasmonic active metals naturally exhibit catalytic activities for electrochemical fuel conversion that can be enhanced by engineering their structures to form unique catalytic structures such as symmetry-broken Au-Cu Janus nanocrystals.13 These studies are critical to addressing the global challenges of energy14-15 and CO2 emission from nonrenewable sources such as coal, petroleum, and natural gas.16-17 Electrochemical systems comprised of unique photonic structures and functions that enable efficient and affordable energy harvesting/conversion/storage are highly desired for providing safe and environment-friendly energy sources. This chapter reviews our recent work of LSPR enabled photoelectrochemical water splitting and recent advances in LSPR-enabled CO2 reduction and photochemical reactions reported in the literature. Scientific and technical challenges of applying LSPR to enhance these energy conversion and storage systems are discussed at the conclusion of this chapter.

Pan, Shanlin↗
DOE OSTI2023

Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence

With the advent of faster computer processors and especially graphics processing units (GPUs) over the last few decades, the use of data-intensive machine learning (ML) and artificial intelligence (AI) has increased greatly, and the study of crystal nucleation has been one of the beneficiaries. In this study, we outline how ML and AI have been applied to address four outstanding difficulties of crystal nucleation: how to discover better reaction coordinates (RCs) for describing accurately non-classical nucleation situations; the development of more accurate force fields for describing the nucleation of multiple polymorphs or phases for a single system; more robust identification methods for determining crystal phases and structures; and as a method to yield improved course-grained models for studying nucleation.

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

Jets and Jet Substructure at Future Colliders

Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy Frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy Frontier physics program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Bayesian optimization for chemical products and functional materials

The design of chemical-based products and functional materials is vital to modern technologies, yet remains expensive and slow. Artificial intelligence and machine learning offer new approaches to leverage data to overcome these challenges. Herein this review focuses on recent applications of Bayesian optimization (BO) to chemical products and materials including molecular design, drug discovery, molecular modeling, electrolyte design, and additive manufacturing. Numerous examples show how BO often requires an order of magnitude fewer experiments than Edisonian search. The essential equations for BO are introduced in a self-contained primer specifically written for chemical engineers and others new to the area. Finally, the review discusses four current research directions for BO and their relevance to product and materials design.

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

40th Eastern Regional Photosynthesis Conference

Photosynthesis has shaped global biochemistry and geochemistry in its production of almost all atmospheric oxygen, and almost every electron that supports the processes of life on Earth was extracted from water by the light-dependent reactions, many of which were then used to fix carbon in the dark reactions. To humans, that fixed carbon is the originator of all fossil fuels and a critical step in conversion of solar photons to human-usable energy. The Eastern Regional Photosynthesis Conference (ERPC) serves as a forum for advancement of science in this field. As a regional-level conference, it specializes in providing developmental opportunities for early-career researchers from undergraduates to pre-tenure professors, as well as broadening the field in terms of participating scholars and research approaches to produce new interdisciplinary collaborations. Funding from the Department of Energy is used to mitigate registration and attendance costs for junior (non-PI) researchers. The ERPC is one of the three regional conferences in the field of photosynthesis research in the United States, alongside the Western Photosynthesis Conference (currently in California) and the Midwestern Photosynthesis Conference (Turkey Run, Indiana). A longstanding incubator for scientific discourse, discovery, and collaborations, this conference has spawned many collaborations which led to Department of Energy-funded projects and brought two full generations of scientists into the field. In 2023 the landmark 40th such conference was held at the Woods Hole Marine Biological Laboratory in Massachusetts. The conference was held April 14th-16th, 2023. The majority of the total attendees are traditionally undergraduates, graduate students, and postdoctoral researchers, whose scientific development greatly benefits from conferences like these. Almost all talks besides the ones given by invited speakers were given by young scientists who benefited from this award. Many posters presented at this conference were also the work of junior scientists. This conference is many young scientists' first exposure to the photosynthesis research community and its primary goal is to provide a positive atmosphere for those attendees, as exemplified by the three poster sessions and dedicated interactions between senior and junior scientists. Additionally, this was the first year of a successful equitable outreach and speaker recruitment plan to provide fair representation at the conference in line with Department of Energy policies and mission. The central focus of the ERPC is the understanding of energy flow from photons to usable chemical products in both natural and artificial photosynthesis. We support the DOE-BES aim of understanding of the biochemical/biophysical processes of photosynthesis to inform new technology development and field applications. This year’s theme, “Photosynthesis Across Scales,” reflected the scope of work being done from the individual exciton to the crop scale. This research encompasses applied biology, chemistry, physics, and materials engineering. The aim of this conference is to present new discoveries, techniques, and questions which can be sourced from any of these disciplines and provide an audience which is broadly qualified to use or respond to these developments.

08 HYDROGEN↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology

Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol–disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol–disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. Furthermore, as an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol–disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.

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

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Design, Synthesis, and Utility of Defined Molecular Scaffolds

A growing theme in chemistry is the joining of multiple organic molecular building blocks to create functional molecules. Diverse derivatizable structures—here termed “scaffolds” comprised of “hubs”—provide the foundation for systematic covalent organization of a rich variety of building blocks. This review encompasses 30 tri- or tetra-armed molecular hubs (e.g., triazine, lysine, arenes, dyes) that are used directly or in combination to give linear, cyclic, or branched scaffolds. Each scaffold is categorized by graph theory into one of 31 trees to express the molecular connectivity and overall architecture. Rational chemistry with exacting numbers of derivatizable sites is emphasized. The incorporation of water-solubilization motifs, robust or self-immolative linkers, enzymatically cleavable groups and functional appendages affords immense (and often late-stage) diversification of the scaffolds. Altogether, 107 target molecules are reviewed along with 19 syntheses to illustrate the distinctive chemistries for creating and derivatizing scaffolds. The review covers the history of the field up through 2020, briefly touching on statistically derivatized carriers employed in immunology as counterpoints to the rationally assembled and derivatized scaffolds here, although most citations are from the past two decades. The scaffolds are used widely in fields ranging from pure chemistry to artificial photosynthesis and biomedical sciences.

Sato, Daisuke (ORCID:0000000305472659)↗
DOE OSTI2023

Photocatalytic water splitting

With the goal of achieving large-scale H 2 production from renewable resources, water splitting into H 2 and O 2 using semiconductor photocatalysts (sometimes called artificial photosynthesis) has been studied for five decades. Unfortunately, the lack of rigour and reproducibility in the data collection and analysis of experimental results has hindered progress in the field. This Primer provides a comprehensive overview of proper characterization and evaluation of photocatalysts for overall water splitting. In particular, the Primer covers various pitfalls in photocatalysis research, best practices for reproducibility and reliable methods for conducting rigorous experiments. As a result, the recommendations are intended to reduce false positives in the literature and to promote progress towards a practical technology for producing H 2 from water by using sunlight.

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

Temperature-Controlled Reversible Formation and Phase Transformation of 3D Nanocrystal Superlattices Through In Situ Small-Angle X-ray Scattering

For decades, the spontaneous organization of nanocrystals into superlattices has captivated the scientific community. However, achieving direct control over the formation of the superlattice and its phase transformations has proven to be a grand challenge, often resulting in the generation of multiple symmetries under the same experimental conditions. Here, we achieve direct control over the formation of the superlattice and its phase transformations by modulating the thermal energy of a nanocrystal dispersion without relying on solvent evaporation. In this study, we follow the temperature-dependent dynamics of the self-assembly process using synchrotron-based small-angle X-ray scattering. When cooled below –24.5 °C, lead sulfide nanocrystals form micrometer-sized three-dimensional phase-pure body-centered cubic superlattices. When cooled below –35.1 °C, these superlattices undergo a collective diffusionless phase transformation that yields denser body-centered tetragonal phases. These structural changes can be reversed by increasing the temperature of the dispersion and may lead to the direct modulation of the optical properties of these artificial solids.

36 MATERIALS SCIENCE↗
DOE OSTI2023

AI-based Detection and Defense Against Cyberattacks in Distributed Energy Resources

This study will provide comprehensive artificial intelligence (AI)-based solution tools for network security, malware prevention, and sensor data anomaly detection for distributed energy resource (DER) research, development, and demonstration. DER technologies are energy systems (e.g., solar panels, wind turbines, and energy storage systems) that are often connected to the internet and thus vulnerable to cyberattacks. Cybersecurity should be of primary concern for DERs, which is why we propose an integrated multi-layer cyber-defense system for DERs. This system encompasses risk assessments, network security, malware prevention, and detection of anomalies in the sensor data. Implementation of a comprehensive risk assessment with an overview of the model architecture should be the primary step, and should include the potential impact of experiencing, at a given time, one or more cyberattacks on the system. The second step is to ensure that the network security includes firewalls, intrusion detection, and malware prevention. The third step is to provide solution tools that enable sensor data anomaly detection for DERs. By incorporating these considerations into DER research, development, and demonstration, organizations can help ensure the safety and security of their systems and protect against potential cyberattacks.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Artificial Intelligence for Conjugated Polymers

Conjugated polymers have garnered significant attention due to their diverse applications in electronics, photonics, and energy storage. However, realizing their full potential poses a formidable challenge, as their design has historically relied on iterative adjustments and continuous inspiration from researchers. Traditional methods often struggle to efficiently navigate their vast chemical landscape. In this work, the application of artificial intelligence (AI), specifically machine learning (ML), needs to be discussed in the realm of conjugated polymers. Our paper emphasizes the importance of understanding the structure–property relationships of these polymers and how ML can facilitate property prediction and inverse-design. We delve into various chemical fingerprints, structural descriptors, and ML algorithms, showcasing their utility across a spectrum of applications, including simulations, glass transition temperature determination, photovoltaics, reorganization energy for charge transport, photocatalysts, and sensors. Finally, we give some outlooks in this filed and propose unexplored areas within the field that hold the potential to benefit from ML techniques.

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

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

3D visualization↗
DOE OSTI2024

Tetranuclear Polypyridylruthenium(II) Complexes as Selective Nucleic Acid Stains for Flow Cytometric Analysis of Monocytic and Epithelial Lung Carcinoma Large Extracellular Vesicles

Selective staining of extracellular vesicles (EVs) is a major challenge for diagnostic and therapeutic applications. Herein, the EV labeling properties of a new class of tetranuclear polypyridylruthenium(II) complexes, Rubb7-TNL and Rubb7-TL, as phosphorescent stains are described. These new stains have many advantages over standard stains to detect and characterize EVs, including: high specificity for EV staining versus cell staining; high phosphorescence yields; photostability; and a lack of leaching from EVs until incorporation with target cells. As an example of their utility, large EVs released from control (basal) or lipopolysaccharide (LPS)-stimulated THP-1 monocytic leukemia cells were studied as a model of immune system EVs released during bacterial infection. Key findings from EV staining combined with flow cytometry were as follows: (i) LPS-stimulated THP-1 cells generated significantly larger and more numerous large EVs, as compared with those from unstimulated cells; (ii) EVs retained native EV physical properties after staining; and (iii) the new stains selectively differentiated intact large EVs from artificial liposomes, which are models of cell membrane fragments or other lipid-containing debris, as well as distinguished two distinct subpopulations of monocytic EVs within the same experiment, as a result of biochemical differences between unstimulated and LPS-stimulated monocytes. Comparatively, the staining patterns of A549 epithelial lung carcinoma-derived EVs closely resembled those of THP-1 cell line-derived EVs, which highlighted similarities in their selective staining despite their distinct cellular origins. This is consistent with the hypothesis that these new phosphorescent stains target RNA within the EVs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗