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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ultrathin Boron Growth onto Nanodiamond Surfaces via Electrophilic Boron Precursors

Diamond as a templating substrate is largely unexplored, and the unique properties of diamond, including its large bandgap, thermal conductance, and lack of cytotoxicity, makes it versatile in emergent technologies in medicine and quantum sensing. Surface termination of an inert diamond substrate and its chemical reactivity are key in generating new bonds for nucleation and growth of an overlayer material. Oxidized high-pressure high temperature (HPHT) nanodiamonds (NDs) are largely terminated by alcohols that act as nucleophiles to initiate covalent bond formation when an electrophilic reactant is available. In this work, we demonstrate a templated synthesis of ultrathin boron on ND surfaces using trigonal boron compounds. Boron trichloride (BCl 3 ), boron tribromide (BBr 3 ), and borane (BH 3 ) were found to react with ND substrates at room temperature in inert conditions. BBr 3 and BCl 3 were highly reactive with the diamond surface, and sheet-like structures were produced and verified with electron microscopy. Surface-sensitive spectroscopies were used to probe the molecular and atomic structure of the ND constructs’ surface, and quantification showed the boron shell was less than 1 nm thick after 1–24 h reactions. Observation of the reaction supports a self-terminating mechanism, similar to atomic layer deposition growth, and is likely due to the quenching of alcohols on the diamond surface. X-ray absorption spectroscopy revealed that boron-termination generated midgap electronic states that were originally predicted by density functional theory (DFT) several years ago. DFT also predicted a negative electron surface, which has yet to be confirmed experimentally here. The boron-diamond nanostructures were found to aggregate in dichloromethane and were dispersed in various solvents and characterized with dynamic light scattering for future cell imaging or cancer therapy applications using boron neutron capture therapy (BNCT). The unique templating mechanism based on nucleophilic alcohols and electrophilic trigonal precursors allows for covalent bond formation and will be of interest to researchers using diamond for quantum sensing, additive manufacturing, BNCT, and potentially as an electron emitter.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Polydentate ligands and their complexes for molecular catalysis

Embodiments of the present disclosure relate generally to novel achiral and chiral sulfur-, nitrogen- and phosphorus-containing ligands, designated as NNS-type, P(O)NS-type, PNS-type, SNNS-type, SNNP(O)-type, or SNNP-type polydentate ligands and transition metal complexes of these ligands, including iridium complexes having PNS-type and NNS-type ligands. The catalysts derived from these ligands and transition metal complexes may be used in a wide range of catalytic reactions, including hydrogenation and transfer hydrogenation of unsaturated organic compounds, dehydrogenation of alcohols and boranes, various dehydrogenative couplings, chemoselective hydrogenation of α,β-unsaturated alcohols, and other catalytic transformations.

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

Activated magnesium boride materials for hydrogen storage

Some embodiments described herein provide for methods for synthesizing magnesium borohydride from hydrogenation of magnesium boride at moderate temperature and pressure in the presence of a modifier. The modifier may be in form of hydrides, liquid hydrogen carriers, ammonia borane, metallic species, croconate anion based materials, ethers, amines or imines, metal carbides, borides, graphene, arenes, magnesium, aluminum, calcium or ionic liquids. Some embodiments provide for charging magnesium boride in presence of a modifier at high pressure hydrogen while simultaneously heating the material. The modification in some instances may lead to an improved magnesium boride product with enhanced properties for application in other hydrogen storage systems.

Severa, Godwin↗
DOE OSTI2025

Electrostatically Enhanced Buried Interface Binding of Self‐Assembled Monolayers for Efficient And Stable Inverted Perovskite Solar Cells

Inverted p‐i‐n structure perovskite solar cells (PSCs) have outperformed traditional n‐i‐p PSCs in recent years. A key advancement is the use of self‐assembled monolayers (SAMs) as hole transport layers. One class of widely used SAMs is carbazole‐based phosphonic acids. However, it is found that these SAMs lack strong binding with transparent conducting oxides (TCO) and perovskite. The weak binding strength results in suboptimal interfacial adhesion of the buried interface, which limits the device's stability. Here, interfacial binding is enhanced by increasing the dipole moment that creates a strong interfacial electric field that enhances electrostatic interactions at the TCO/perovskite interface, while incorporating tailored functional groups in SAMs to improve chemical anchoring to TCO and binding to perovskite. Specifically, the donor‐acceptor SAM molecule 4‐(7‐(4‐(bis(4‐methoxyphenyl)amino)‐2,5‐difluorophenyl)benzo[c][1,2,5]thiadiazol‐4‐yl)benzoic acid (PAFTB) is employed, which features an enhanced dipole moment along with electron‐donating and electron‐withdrawing functional groups to optimize interfacial interactions. Compared to extensively used [2‐(9H‐carbazol‐9‐yl)ethyl]phosphonic acid (2PACz), PAFTB enhances total interfacial adhesion by 2.8 times, thereby improving the thermal stability of the layer. Using this approach, PSCs are demonstrated with a certified quasi‐steady‐state power conversion efficiency of 24.9% and maintain 80% of the initial efficiency after 900 h of maximum power point tracking at 85 °C.

Interfacial adhesion↗
DOE OSTI2022

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach

Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects’ neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants’ demographic characteristics. Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

neuropsychological↗
DOE OSTI2022

Direct Fabrication of Vertically Stacked Double Barrier Tunnel Junctions Based on Graphene and h-BN

Direct manufacturing of two-dimensional material-based double barrier (DB) tunnel junctions, based on a lithography-free approach was developed. Graphene/h-BN/Graphene/h-BN/Graphene devices were deposited on Si/SiO2 substrates by employing a plasma enhanced chemical vapor deposition technique in a sequential manner. DB tunneling junctions with varying barrier widths (by varying the thickness of the second graphene layer) were studied. Samples were characterized using Raman, Atomic Force Microscopy and X-ray photoemission spectroscopy. The I–V characteristics of tunneling current showed resonant tunneling behavior at room temperature with a negative differential conductance. The behavior could be explained with quantum mechanical double barrier tunneling model in which analytic solutions to Schrödinger’s equation were obtained in each region of the system. Resonances in transmission probability coefficient for varying barrier widths were evaluated and compared with the experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Robotic High-Throughput Grid-Search Platform for Mapping Phase Behavior in Triblock Copolymer–Homopolymer Blends

We present a high-throughput experimental investigation of the phase behavior in triblock copolymers (PS-b-PB-b-PS and PS-b-PI-b-PS) and polystyrene (PS) homopolymer blends as a function of homopolymer molecular weight (MW) and blend ratio. Using a robotic thin-film processing platform (NOVA) integrated with Grazing Incidence Small-Angle X-ray Scattering (GISAXS) and Atomic Force Microscopy (AFM), we systematically mapped the order–disorder transition (ODT) boundaries and domain spacing evolution across a broad MW range (4.0–101.3 kDa) with varying homopolymer loadings (10% to 90%). The results reveal three distinct regimes: low-MW homopolymers, corresponding to the wet-brush regime produced only gradual domain swelling before disordering at high blend ratios (weight fraction); medium-MW homopolymers, corresponding to thedry-brush regime induced significant domain spacing increase up to 80% followed by earlier disordering, while high-MW homopolymers led to macrophase separation with minimal changes in domain spacing. Additionally, coarse-grained molecular dynamics simulations confirmed our experimental finding that in the low-MW region, the PS homopolymer uniformly distributed in the PS domain. These findings demonstrate that homopolymer molecular weight critically governs both the extent of domain swelling and the onset of disorder in triblock copolymer systems. This high-throughput platform enables the rapid mapping of composition–morphology relationships and can be integrated with AI/ML tools for designing next-generation nanostructured polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2026

Chain entanglements enable regeneration of high-performance thermosets

Thermoset plastics underpin structural materials, electronics and transportation, yet the permanent covalent networks that prevent flow and provide dimensional stability also make them difficult to recycle without sacrificing performance. In this work we show that high-performance thermosets can be built around dense chain entanglements, the physical interlacing of long polymer strands, rather than dense permanent crosslinks, with only a small number of selectively cleavable junctions preserving connectivity. Long, rigid, entangled polyolefin backbones generated by frontal polymerization form glassy polymers with high stiffness, high toughness and excellent creep suppression yet can be fully deconstructed into soluble, linear oligomers. Varying oligomer length and end-group chemistry enables their reuse as re-entangling building blocks that regenerate thermosets with thermal and mechanical properties that remain unchanged across generations. The strategy further extends to high-temperature fibre-reinforced composite matrices and additively manufactured structures, establishing chain entanglement as a design principle for durable, regenerable thermosets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Moiré-driven topological electronic crystals in twisted graphene

In a dilute two-dimensional electron gas, Coulomb interactions can stabilize the formation of a Wigner crystal. Although Wigner crystals are topologically trivial, it has been predicted that electrons in a partially filled band can break continuous translational symmetry and time-reversal symmetry spontaneously, resulting in a type of topological electron crystal known as an anomalous Hall crystal. Here we report signatures of a generalized version of the anomalous Hall crystal in twisted bilayer–trilayer graphene, whose formation is driven by the moiré potential. The crystal forms at a band filling of one electron per four moiré unit cells (ν = 1/4) and quadruples the unit-cell area, coinciding with an integer quantum anomalous Hall effect. The Chern number of the state is exceptionally tunable, and it can be switched reversibly between +1 and −1 by electric and magnetic fields. Several other topological electronic crystals arise in a modest magnetic field, originating from ν = 1/3, 1/2, 2/3 and 3/2. As a result, the quantum geometry of the interaction-modified bands is likely to be very different from that of the original parent band, which enables possible future discoveries of correlation-driven topological phenomena.

Electronic properties and materials↗