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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modulating the Molecular Geometry and Solution Self-Assembly of Amphiphilic Polypeptoid Block Copolymers by Side Chain Branching Pattern

Solution self-assembly of coil-crystalline diblock copolypeptoids has attracted increasing attention due to its capability to form hierarchical nanostructures with tailorable morphologies and functionalities. While the N-substituent (or side chain) structures are known to affect the crystallization of polypeptoids, their roles in dictating the hierarchical solution self-assembly of diblock copolypeptoids are not fully understood. Herein, we designed and synthesized two types of diblock copolypeptoids, i.e., poly(N-methylglycine)-b-poly(N-octylglycine) (PNMG-b-PNOG) and poly(N-methylglycine)-b-poly(N-2-ethyl-1-hexylglycine) (PNMG-b-PNEHG), to investigate the influence of N-substituent structure on the crystalline packing and hierarchical self-assembly of diblock copolypeptoids in methanol. With a linear aliphatic N-substituent, the PNOG blocks pack into a highly ordered crystalline structure with a board-like molecular geometry, resulting in the self-assembly of PNMG-b-PNOG molecules into a hierarchical microflower morphology composed of radially arranged nanoribbon subunits. By contrast, the PNEHG blocks bearing bulky branched aliphatic N-substituents are rod-like and prefer to stack into a columnar hexagonal liquid crystalline mesophase, which drives PNMG-b-PNEHG molecules to self-assemble into symmetrical hexagonal nanosheets in solution. A combination of time-dependent small/wide-angle X-ray scattering and microscopic imaging analysis further revealed the self-assembly mechanisms for the formation of these microflowers and hexagonal nanosheets. These results highlight the significant impact of the N-substituent architecture (i.e., linear versus branched) on the supramolecular self-assembly of diblock copolypeptoids in solution, which can serve as an effective strategy to tune the geometry and hierarchical structure of polypeptoid-based nanomaterials.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗
DOE OSTI2026

The landscape of regulatory element evolution in a C4 perennial grass

Gene regulatory evolution is a well-known source of phenotypic diversity and adaptive evolution. Although cis-regulatory elements (CREs) play a vital role in gene expression evolution, the molecular evolution of CREs remains mostly unknown due to the difficulty in identifying and characterizing these functional elements. Comparative genomic analyses of noncoding DNA can be leveraged to identify conserved noncoding sequences (CNS), many of which may harbor functional CREs conserved by purifying selection. However, purely computational inference of CREs from putative CNS can be erroneous due to the complex genomic architecture in plants. One promising experimental approach to identify CREs is by profiling accessible chromatin regions (ACRs) that are often associated with the location of CREs. In this study, we use comparative genomics along with the profiling of ACRs to study the molecular evolution of putative functional noncoding regulatory regions in Panicoid grasses. We identified sets of CNS that varied in relationship to the degree of evolutionary divergence among the studied taxa, including identifying core-Panicoid-CNS. We augmented this analysis by profiling ACRs in Panicum hallii ecotypes using ATAC-seq. ACRs had low SNP density at the summit, harbored a high frequency of core-Panicoid-CNS, and were enriched with expression QTL. These data help to annotate the P. hallii genome for putative functional elements and suggest that a large proportion of these ACRs are evolving under purifying selection. Turnover in CNS and ACR between ecotypes of P. hallii identifies a small set of putatively divergent CREs that may underlie differences in gene regulation between genotypes from inland and coastal habitats. In summary, we profiled ACRs in Panicoid grasses and integrated this data with our putative CNS prediction framework, which provides unique insight into patterns of polymorphism and divergence in CREs in C4 perennial grasses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modeling root system growth around obstacles

State-of-the-Art models of Root System Architecture (RSA) do not allow simulating root growth around rigid obstacles. Yet, the presence of obstacles can be highly disruptive to the root system. We grew wheat seedlings in sealed petri dishes without obstacle and in custom 3D-printed rhizoboxes containing obstacles. Time-lapse photography was used to reconstruct the wheat root morphology network. We used the reconstructed wheat root network without obstacle to calibrate an RSA model implemented in the R-SWMS software. The root network with obstacles allowed calibrating the parameters of a new function that models the influence of rigid obstacles on wheat root growth. Experimental results show that the presence of a rigid obstacle does not affect the growth rate of the wheat root axes, but that it does influence the root trajectory after the main axis has passed the obstacle. The growth recovery time, i.e. the time for the main root axis to recover its geotropism-driven growth, is proportional to the time during which the main axis grows along the obstacle. Qualitative and quantitative comparisons between experimental and numerical results show that the proposed model successfully simulates wheat RSA growth around obstacles. Our results suggest that wheat roots follow patterns that could inspire the design of adaptive engineering flow networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Revealing Deformation Mechanisms in Polymer-Grafted Thermoplastic Elastomers via In Situ Small-Angle X-ray Scattering

The tunable properties of thermoplastic elastomers (TPEs), through polymer chemistry manipulations, enable these technologically critical materials to be employed in a broad range of applications. The need to “dial-in” the mechanical properties and responses of TPEs generally requires the design and synthesis of new macromolecules. In these designs, TPEs with nonlinear macromolecular architectures outperform the mechanical properties of their linear copolymer counterparts, but the differences in the deformation mechanism providing enhanced performance are unknown. Here, in situ small-angle X-ray scattering (SAXS) measurements during uniaxial extension reveal distinct deformation mechanisms between a commercially available linear poly(styrene)–poly(butadiene)–poly(styrene) (SBS) triblock copolymer and the grafted SBS version containing grafted poly(styrene) (PS) chains from the poly(butadiene) (PBD) midblock. The neat SBS (φ SBS = 100%) sample deforms congruently with the macroscopic dimensions, with the domain spacing between spheres increasing and decreasing along and transverse to the stretch direction, respectively. At high extensions, end segment pullout from the PS-rich domains is detected, which is indicated by a disordering of SBS. Conversely, the PS-grafted SBS that is 30 vol % SBS and 70% styrene (φ SBS = 30%) exhibits a lamellar morphology, and in situ SAXS measurements reveal an unexpected deformation mechanism. During deformation, there are two simultaneous processes: significant lamellar domain rearrangement to preferentially orient the lamellae planes parallel to the stretch direction and crazing. The samples whiten at high strains as expected for crazing, which corresponds with the emergence of features in the 2D SAXS pattern during stretching consistent with fibril-like structures that bridge the voids in crazes. The significant domain rearrangement in the grafted copolymers is attributed to the new junctions formed across multiple PS domains by the grafting of a single chain. In conclusion, the in situ SAXS measurements provide insights into the enhanced mechanical properties of grafted copolymers that arise through improved physical cross-linking that leads to nanostructure domain reorientation for self-reinforcement and craze formation where fibrils help to strengthen the polymer.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ICED: An Integrated CGRA Framework Enabling DFVS-Aware Acceleration

oarse-grained reconfigurable arrays (CGRAs) are a promising solution to enable energy-efficient acceleration of applications from different domains. By leveraging reconfiguration at the functional level, they can adapt to significantly different computational patterns. Existing CGRA mapping approaches extract instruction-level parallelism, exploit loop-pipelining opportunities, guarantee the data dependency, and target high throughput of a given loop. However, the recurrence data-dependency in the DFG and the mismatch between required and available computing/communication resources complicate the mapping, and might lead to significant unbalances in the utilization of the CGRA's tiles. This results in wasted power for tiles with low utilization. Applying dynamic voltage and frequency scaling (DVFS) can potentially solve this challenge and improve energy efficiency by adjusting voltage and frequency of different tiles independently. CGRAs have also been successful in accelerating data-dependent streaming applications. However, in these applications, the execution time of each kernel in the pipeline might dynamically vary depending on the characteristics of the input. This also leads to under-utilization of resources for the dynamically changing kernels that do not limit the application throughput. DVFS can also improve energy efficiency for these applications by dynamically changing the voltage and frequency levels of tiles that host non performance-constraining kernels. This paper proposes ICEDTEA -- an integrated DVFS-aware framework to map applications on CGRAs that support power islands. ICEDTEA proposes a CGRA architecture supporting DVFS islands at varying granularity (from a single tile to a group of tiles) and the related DVFS-aware compilation and mapping toolchain. ICEDTEA is the first work that introduces DVFS support for spatio-temporal CGRAs at power-island levels. The experimental evaluation shows that ICEDTEA improves average utilization by 2.3$\times$ and energy-efficiency by 1.32$\times$ over a conventional CGRA. With streaming applications, ICEDTEA improves energy efficiency by 1.12$\times$ over a state-of-the-art CGRA that introduces partial dynamic reconfiguration to adapt to variations in kernels' throughput.

Tan, Cheng↗
DOE OSTI2025

Agentic AI vs ML-Based Autotuning: A Comparative Study for Loop Reordering Optimization

High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.

Rosas, Miguel Romero↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

TropiRoot 1.0: Database of tropical root characteristics across environments

Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.

FRED↗
DOE OSTI2023

Metaoptics Enabled Multifunctional Imaging

Despite the fact that most microorganisms occupy two- and three-dimensional space in heterogeneous arrangements and in proximity to other microorganisms of different species, much of our current knowledge about their metabolic processes is derived from homogenizing, then analyzing, the chemical content of cultures containing a single microbial species. This project addressed this fundamental dichotomy by developing a set of optical imaging principles combining multiple new technologies. The imaging strategies developed in this project combine metaoptics structures with active in situ nanoscale control of the chemical environment and applied them to a microbial system, Myxococcus xanthus, with particular relevance to the DoE mission. The combination of metaoptical architectures and nanoscale control over the molecular environment enables: (a) precise control over the electromagnetic (EM) field at length scales smaller than the wavelength of light; (b) control of the interaction of the EM field with critical molecular systems in DOE relevant microbes; (c) control over the chemical environment – especially the presence and quantity of reactive oxygen species (ROS) that can affect redox homeostasis; and (d) the ability to ask new kinds of questions not accessible to ‘omics’ approaches or standard methods of biological imaging. These capabilities are applicable to detailed studies of metabolic pathways in microbes and to lignocellulosic biomass deconstruction. To accomplish these objectives, we pursued two over-arching technical goals: (1) the development of new metaoptics-enabled approaches to imaging and spectroscopic characterization; and (2) the development of tools to control the chemical environment of a microbial sample with nanometer-scale precision. Goal 1 was addressed through the design, fabrication, and characterization of new metasurfaces capable of super-resolution imaging through extreme confinement of the optical field. Goal 2 was addressed by controlling the redox potential on the nanoscale in microbial communities and characterizing their effect on intrinsic bacterial fluorophores which act as molecular sentinels and through characterization of soluble factors secreted by Myxococcus xanthus by confocal Raman imaging. The optical imaging/sensing approaches developed here make it possible to use these powerful new imaging and sensing modalities in metabolic studies by making it possible to visualize and track the spatial and temporal expression patterns of natural or engineered pathways in microorganisms.

09 BIOMASS FUELS↗
DOE OSTI2025

Si content in methacrylamide-containing A- b -(B- r -C) block copolymers and its impact on reactive ion etching properties

Block copolymers (BCPs) of an A-block-(B-random-C) architecture have been explored as materials for nanolithography because the composition and chemistry of the random block enables modification of thermodynamic and wetting properties to meet manufacturing criteria. Here, in this study, A-b-(B-r-C) BCPs created by an amidation reaction of polystyrene-block-poly(pentafluorophenyl methacrylate) (PS-b-PPFMA) with controlled amounts of Si add insight to previous conclusions about the dual contributions of BCP chemistry and reactive ion etch (RIE) gas chemistry on etch properties. We focus on two RIE etch characteristics: organosilicon etch resistance in H 2 /N 2 plasma etching and enhanced removal of non-styrenic structures in an Ar/O 2 etch. Consistent with previous studies, higher amounts of Si result in greater etch resistance under H 2 /N 2 RIE, where at least ∼10 wt. % Si is necessary to exhibit sufficient etch resistance. By contrast, Ar/O 2 etching resulted in etch rates independent of Si content. We observe previously unreported surface roughening aligned with morphological domains during the H 2 /N 2 etch of modified PS-b-PPFMA BCPs. Limited in the amount of allowable Si to attain equal surface energy between blocks, these BCPs are further disqualified in forming a Si-containing mask. However, in an Ar/O 2 etch, the same BCPs exhibit suitable etch contrast and smooth domain structures, forming a uniform PS mask. Ultimately, this study uses the chemical flexibility of these materials to demonstrate the mechanisms of interactions between BCP and etch chemistry that must be considered to design effective materials for pattern transfer applications.

Eom, Christopher J. [University of Chicago, IL (Un↗
DOE OSTI2024

Mitigation for Removal of Building 01-202681 in the Main Storage Yard of the Area 1 Subdock, Nevada National Security Site, Nye County, Nevada

The Main Storage Yard was originally recorded as SHPO Resource No. S2772 in 2020 during an architectural survey of the Area 1 Subdock (Collins et al. 2022). It was recommended individually eligible for listing in the NRHP under Criteria A and C and as contributing to the significance of the potential Area 1 Subdock Historic District. The SHPO concurred with these determinations in a letter dated April 29, 2022 (Reed). The Main Storage Yard retains all seven aspects of integrity. The location, design, materials, workmanship, feeling, and association have not changed since the original construction. Since the purpose of the yard is to maintain storage equipment that is actively being used, the design and feeling will change each time equipment is moved. Such actions reinforce the integrity of design and feeling. Buildings 01-101 and 01-102 were removed (Collins and Menocal 2020), but the other buildings and storage yard remain. Therefore, the setting has been changed, but not to the degree that the Main Storage Yard no longer conveys its significance (see Figures 4 and 5 for comparison). The Main Storage Yard was designed to facilitate the relocation of the Area 3 Subdock to Area 1 in 1985. The design for the overall Subdock provided a centralized cluster of buildings surrounded by two large storage yards. While the North Storage Yard largely serves as overflow storage, the Main Storage Yard holds the majority of drill rigs and drilling equipment needed to support big hole drilling. The Main Storage Yard and its contents were essential elements of the nuclear testing drilling program on the NNSS thus making it eligible for the NRHP under Criterion A for its contribution to broad patterns of history by storing specially designed equipment for underground nuclear testing at the NNSS. It is also eligible under Criterion C as it is an engineered landscape that now serves as an open-air repository of drilling technology and nuclear testing. In addition to its individual significance, the Main Storage Yard also contributes to the significance of the Area 1 Subdock Historic District as it showcases a variety of big-hole drilling equipment, support facilities, and warehouses that were specially designed for nuclear testing at the NNSS. Despite the demolition of two contributing elements to date, the district still contains numerous examples of other facilities with the purpose of supporting big-hole drilling; therefore, the district still remains eligible under Criteria A and C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Flexible CO 2 Sensor Architecture with Selective Nitrogen Functionalities by One‐Step Laser‐Induced Conversion of Versatile Organic Ink

Abstract Nitrogen‐containing carbons (NC) are a class of sustainable materials for selective CO 2 adsorption. A versatile concept is introduced to fabricate flexible NC‐based sensor architectures for room‐temperature sensing of CO 2 in a one‐step laser conversion of primary films cast from abundant precursors. By the unidirectional energy impact in conjunction with depth‐dependent attenuation of the laser beam, a layered sensor heterostructure with a porous transducer and active sensor layer is formed. Comprehensive microscopic and spectroscopic cross‐sectional analyses confirm the preservation of the high content of imidazolic nitrogen in the sensor. The performance is optimized in terms of material morphology, chemical composition, and surface chemistry to achieve a linear relative resistive response of up to Δ R / R 0 = −14.3% (10% of CO 2 ). Thermodynamic analysis yields Δ ads H values of −35.6 and 34.1 kJ·mol −1 for H 2 O and CO 2 , respectively. The sensor is operable even in humid environments (e.g., ∆ R / R 0,RH = 80% = 0.53%) and shows good performance upon strong mechanical deformation.

flexible gas sensors↗
DOE OSTI2024

A gene desert required for regulatory control of pleiotropic Shox2 expression and embryonic survival

Approximately a quarter of the human genome consists of gene deserts, large regions devoid of genes often located adjacent to developmental genes and thought to contribute to their regulation. However, defining the regulatory functions embedded within these deserts is challenging due to their large size. Here, we explore the cis-regulatory architecture of a gene desert flanking the Shox2 gene, which encodes a transcription factor indispensable for proximal limb, craniofacial, and cardiac pacemaker development. We identify the gene desert as a regulatory hub containing more than 15 distinct enhancers recapitulating anatomical subdomains of Shox2 expression. Ablation of the gene desert leads to embryonic lethality due to Shox2 depletion in the cardiac sinus venosus, caused in part by the loss of a specific distal enhancer. The gene desert is also required for stylopod morphogenesis, mediated via distributed proximal limb enhancers. In summary, our study establishes a multi-layered role of the Shox2 gene desert in orchestrating pleiotropic developmental expression through modular arrangement and coordinated dynamics of tissue-specific enhancers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Continuous Counter‐Current Microfluidic Liquid–Liquid Extraction Achieved Using a Pair of Wettable Screen Meshes

Continuous counter‐current microfluidic liquid–liquid extraction performs separations by flowing immiscible liquids in opposing directions within a single flow channel. In principle, this flow arrangement enables a large number of theoretical separation units in a small footprint, without using interstage valving, pumping, and phase separation. Despite its potential for excellent separation performance, this microfluidic scheme rarely appears in literature due to the requirement for capillary forces to be greater than hydrodynamic forces for stable flow. We present a novel microfluidic device and flow approaches that overcome this force‐balance challenge, enabling stable, long‐duration continuous counter‐current flow. Additionally, we cover a suite of methodologies for quantifying the performance of the microfluidic device, revealing the number of theoretical equilibrium stages achieved. The enabling technologies include a woven mesh screen‐based microfluidic device architecture that is easily fabricated outside of a clean room, surface functionalization strategies to promote conjugate (organic/aqueous) wettability, flow approaches to eliminate bubbles and carryover, and computer‐aided flow automation with optical measurement of extraction performance. The reported experiments lasted for over 36 h, terminated only at experiment conclusion, where the device still exhibited good performance. Automated Raman spectroscopy was used for solute quantitation of the ternary system tert‐butanol in a toluene/water matrix, a ternary system that was specifically chosen to analyze the device's performance with a small solute partition ratio and to enable in‐line Raman measurements of solute concentrations in both phases. The microfluidic device possessed a 55 mm contact length and a 38.5 µL internal volume. During counter‐current flow, we observed approximately 37 equilibrium stages (37 ± 13) based on a best‐fit of the solute fraction remaining in the aqueous phase using a Kremser Group Method analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Confinement and Processing Can Alter the Morphology and Periodicity of Bottlebrush Block Copolymers in Thin Films

Bottlebrush block copolymers (BBCPs) are intriguing architectural variations on linear BCPs with highly tunable structure. Confinement can have a significant impact on polymer assembly, giving rise to changes in morphology, assembly kinetics, and properties like the glass transition. Given that confinement leads to significant changes in the persistence length of bottlebrush homopolymers, it is reasonable to expect that BBCPs will see significant changes in their structure and periodicity relative to the bulk morphology. Understanding how confinement influences assembly will be important for designing BBCPs for thin film applications including membranes, integrated photonic structures, and potentially BCP lithography. In this work, in order to study the effects of confinement on BBCP conformation and morphology, a blade coating was used to prepare films with continuous variation in film thickness. Unlike thin films of linear BCPs, islands/holes were not observed, and instead mixtures of parallel and perpendicular morphologies emerge after annealing. The lamellar periodicity ($L_0$) of the morphologies is found to be thickness dependent, increasing $L_0$ with decreasing film thickness for blade coated films. Films coated out of tetrahydrofuran (THF) resulted in a single well-defined lamellar periodicity, verified through atomic force microscopy (AFM) and grazing incidence small-angle X-ray scattering (GISAXS), which increases dramatically from the bulk value (30.6 nm) and continues to increase as the film thickness decreases. The largest observed $L_0$ was 65.5 nm, and this closely approaches the estimated upper limit of 67 nm corresponding to a fully extended backbone in a bilayer arrangement. Films coated out of propylene glycol methyl ether acetate (PGMEA) resulted in a mixture of perpendicular lamellae and a smaller, likely cylindrical morphology. The lamellar portion of the film shows the same thickness dependence as the lamellae observed in the THF coated films. The scaling of the lamellar $L_0$ with respect to film thickness follows predictions for confined semiflexible polymers with weak excluded volume interactions and can be related to models for confinement of DNA. Spin coated films shows the same reduction in periodicity, although at very different film thicknesses. This result suggests that the material has shallow free-energy barriers to transitioning between different $L_0$ and morphologies, a property that could be taken advantage of for patterning diverse structures with a single material.

36 MATERIALS SCIENCE↗