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DOE OSTI2024

A General Approach to Activate Second‐Scale Room Temperature Photoluminescence in Organic Small Molecules

Organic small molecules that exhibit second‐scale phosphorescence at room temperature are of interest for potential applications in sensing, anticounterfeiting, and bioimaging. However, such materials systems are uncommon—requiring millisecond to second‐scale triplet lifetimes, efficient intersystem crossing, and slow rates of nonradiative recombination. Here, a simple and scalable approach is demonstrated to activate long‐lived phosphorescence in a wide variety of molecules by suspending them in rigid polymer hosts and annealing them above the polymer's glass transition temperature. This process produces submicron aggregates of the chromophore, which suppresses intramolecular motion that leads to nonradiative recombination and minimizes triplet–triplet annihilation that quenches phosphorescence in larger aggregates. In some cases, evidence of excimer‐mediated intersystem crossing that enhances triplet generation in aggregated chromophores is found. In short, this approach circumvents the current design rules for long‐lived phosphors, which will streamline their discovery and development.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Control-Theory-Informed Feature Selection for Detecting Malicious Tampering in Additive Layer Manufacturing Processes

Additive layer manufacturing (ALM) is rapidly becoming an appealing solution to the low-volume manufacturing of metal, polymer, or composite parts. However, ALM’s reliance on digital part specifications, microcontrollers, and modern networking makes these devices vulnerable to malicious tampering by cyber attackers, which can negatively affect part performance and even result in catastrophic failure. We present a hybrid analytic approach to feature discovery using control theoretic techniques and linear modelling on input-output data collected from a representative controller system. Employing this approach, we design, train, and test an anomaly detection system. The preliminary results show that the proposed approach effectively discovers useful input-output relationships for anomaly detection in a simulated ALM process. Application to larger and more complex systems are discussed.

Dawson, Joel↗

DOE OSTI2025

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design

Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.

Han, Ming [Univ. of Chicago, IL (United States)]↗

DOE OSTI2024

Nanoporous Materials Genome Center Final Technical Report

Nanoporous materials (NPMs), including zeolites/zeotypes, metal-organic frameworks (MOFs), covalent organic frameworks, polymers with intrinsic microporosity, and molecular cages, possess enormous potential in diverse areas relevant to the DOE Office of Science Basic Energy Sciences (BES) mission and objectives. The Nanoporous Materials Genome Center (NMGC) has developed exascale-ready software, computational/theoretical chemistry methods, and data-driven science approaches that enable (i) the de-novo design of functional NPMs for chemical separation and catalysis tasks of increasing complexity, (ii) the discovery of the most promising functional NPMs from databases of synthesized and hypothetical adsorbent structures and the optimization of process conditions for specific applications, and (iii) the microscopic-level understanding of the fundamental interactions underlying the function of NPMs including hierarchical architectures, composite materials, responsive frameworks that may undergo phase transitions or post-synthetic modifications, and materials containing defects, partial disorder, or interfaces. A pivotal part of the NMGC project has been a tight collaboration between leading experimental groups for synthesis and characterization of NPMs and of computational groups that allowed for iterative feedback. The NMGC project has resulted in the publication of more than 290 research and review articles including more than 60 publications in high-impact journals and more than 15 journal covers. NMGC publications have already received more than 20,000 citations (with more than 3,000 citations per year in 2021, 2022, and 2023) and contribute to an h-index of more than 72. The NMGC award has supported collaborative research involving 28 research groups and contributed to the training of more than 40 postdocs, more than 60 graduate students, and more than 20 undergraduate students with broad expertise in data-driven science approaches, computational chemistry methods, and high-performance computing, in addition to the skills to thrive in an integrated experimental and computational research environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

We report the design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2006

MISSE PEACE Polymers Atomic Oxygen Erosion Results

Forty-one different polymer samples, collectively called the Polymer Erosion and Contamination Experiment (PEACE) Polymers, have been exposed to the low Earth orbit (LEO) environment on the exterior of the International Space Station (ISS) for nearly 4 years as part of Materials International Space Station Experiment 2 (MISSE 2). The objective of the PEACE Polymers experiment was to determine the atomic oxygen erosion yield of a wide variety of polymeric materials after long term exposure to the space environment. The polymers range from those commonly used for spacecraft applications, such as Teflon (DuPont) FEP, to more recently developed polymers, such as high temperature polyimide PMR (polymerization of monomer reactants). Additional polymers were included to explore erosion yield dependence upon chemical composition. The MISSE PEACE Polymers experiment was flown in MISSE Passive Experiment Carrier 2 (PEC 2), tray 1, on the exterior of the ISS Quest Airlock and was exposed to atomic oxygen along with solar and charged particle radiation. MISSE 2 was successfully retrieved during a space walk on July 30, 2005, during Discovery s STS-114 Return to Flight mission. Details on the specific polymers flown, flight sample fabrication, pre-flight and post-flight characterization techniques, and atomic oxygen fluence calculations are discussed along with a summary of the atomic oxygen erosion yield results. The MISSE 2 PEACE Polymers experiment is unique because it has the widest variety of polymers flown in LEO for a long duration and provides extremely valuable erosion yield data for spacecraft design purposes.

deGroh, Kim, K.↗

DOE OSTI2025

In-Silico Design of Next Generation Cellulose-Derived Packaging Materials (CRADA Final Report)

Developing sustainable solutions for single-use packaging is an important objective to combat the environmental crisis of plastics pollution. Most embodiments of cellulose-based packaging materials, including CellophaneTM, are completely biodegradable in both terrestrial and marine environments. However, petroleum-derived alternatives offer some performance advantages for metrics such as moisture barriers and mechanical properties. This project leverages molecular dynamics simulation to investigate how molecular modifications to cellulose-based polymer assemblies impact their material properties. An important performance criterion for the modified materials was to retain biodegradability; thus, modifications by naturally occurring, biodegradable additives were the focus of this study. Specifically, we developed models with xylan and lignin of varying monomeric compositions into the cellulose matrix. The mechanical properties were investigated by performing stress-strain simulations, and the water barrier and hydrophobicity were investigated by simulating the water contact angle. Our findings indicate that the incorporation of xylan into the cellulose matrix tends to increase the mechanical properties with an optimal loading of ~27 wt%. We also predict that orienting the nanoscale directionality of the xylan chains such that they are perpendicular to the cellulose fibrils will dramatically increase mechanical strength. In contrast, the incorporation of lignin tends to weaken the composite at all loadings investigated. Simulations of water contact angle predicted that coating polymers on the surface of the cellulose assembly creates a more hydrophobic surface than incorporating them throughout the matrix. Of the coatings investigated, lignin resulted in the most hydrophobic surface, followed by pectin and keratin, which both imparted modest increases in hydrophobicity. Future experimental work done by Futamura will focus on designing material prototypes to capitalize on the predictions of performance enhancement obtained from molecular modeling. While substantial progress was made by the simulations performed in this project, there still exists a vast parameter space that we were unable to investigate, including branching, functional group decoration, and degree of polymerization of polymer additives. However, the methods developed in this initial investigation will facilitate more rapid evaluation of the impact of molecular characteristics on the performance of biopolymer composite materials and thereby accelerate future materials discovery efforts in this area.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Understanding the Structure and Dynamics of Conjugated Polymers by Advancing Deuteration Chemistry and Neutron Scattering (Final Report)

The overarching goal of the proposed work was to set up a partnership between the University of Southern Mississippi (USM) and Oak Ridge National Laboratory (ORNL) to develop novel approaches to measure the backbone rigidity of conjugated polymers (CPs) and understand the critical role of sidechains on the backbone conformation and the materials macroscopic property. The backbone rigidity greatly influences the electronic properties of CPs, which ultimately determines the functionality and performance of these materials. Improvements in the electronic properties of CPs would allow for enhanced charge transport in semiconductor devices, improved photovoltaic performance, recycling of waste heat in thermoelectrics, and discovery of new phenomena that will enable the next generation of energy technologies. Although significant progress has been made to optimize the optical and electronic properties of CPs, largely through Edisonian methodologies, it remains a challenge to experimentally characterize conjugated backbone conformation (chain rigidity, torsion, planarity, and short-range order) and relate these to the fundamental optical and electronic properties (electronic coupling, charge transport, etc.). This has left fundamental gaps in our knowledge of the most basic structure/property relationships within these systems, precluded the study of fundamental physical phenomena, and constrained the design and realization of new electronic and device functionalities. Thus, the major goal of this work is to use novel deuteration methodologies via systematic synthetic approaches, and neutron scattering techniques to comprehensively characterize the structural and dynamic properties of CPs in contrast-matching solvents. Our work would, for the first time, elucidate the relationship between backbone rigidity and macroscopic properties. They will also allow a rational formulation of design principles for next-generation CPs that are resilient to disorder through precise control of the delocalized electrons along the polymer backbone. Overall, this project will advance our understanding of the structure, dynamics, and fundamental physics of these materials, which is crucial for enabling the prediction, design, control, and manipulation of current and emerging material electronic properties.

36 MATERIALS SCIENCE↗

NASA NTRS1990

STS-31 Space Shuttle mission report

The STS-31 Space Shuttle Program Mission Report contains a summary of the vehicle subsystem activities on this thirty-fifth flight of the Space Shuttle and the tenth flight of the Orbiter Vehicle Discovery (OV-103). In addition to the Discovery vehicle, the flight vehicle consisted of an External Tank (ET) (designated as ET-34/LWT-27), three Space Shuttle main engines (SSME's) (serial numbers 2011, 2031, and 2107), and two Solid Rocket Booster (SRB) (designated as BI-037). The primary objective of the mission was to place the Hubble Space Telescope (HST) into a 330 nmi. circular orbit having an inclination of 28.45 degrees. The secondary objectives were to perform all operations necessary to support the requirements of the Protein Crystal Growth (PCG), Investigations into Polymer Membrane Processing (IPMP), Radiation Monitoring Equipment (RME), Ascent Particle Monitor (APM), IMAX Cargo Bay Camera (ICBC), Air Force Maui Optical Site Calibration Test (AMOS), IMAX Crew Compartment Camera, and Ion Arc payloads. In addition, 12 development test objectives (DTO's) and 10 detailed supplementary objectives (DSO's) were assigned to the flight. The sequence of events for this mission is shown. The significant problems that occurred in the Space Shuttle Orbiter subsystems during the mission are summarized, and the official problem tracking list is presented. In addition, each of the Space Shuttle Orbiter problems is cited in the subsystem discussion.

Camp, David W.↗

DOE OSTI2020

Foldable semi-ladder polymers: novel aggregation behavior and high-performance solution-processed organic light-emitting transistors

A critical issue in developing high-performance organic light-emitting transistors (OLETs) is to balance the trade-off between charge transport and light emission in a semiconducting material. Although traditional materials for organic light-emitting diodes (OLEDs) or organic field-effect transistors (OFETs) have shown modest performance in OLET devices, design strategies towards high-performance OLET materials and the crucial structure–performance relationship remain unclear. Our research effort in developing cross-conjugated weak acceptor-weak donor copolymers for luminescent properties lead us to an unintentional discovery that these copolymers form coiled foldamers with intramolecular H-aggregation, leading to their exceptional OLET properties. An impressive external quantum efficiency (EQE) of 6.9% in solution-processed multi-layer OLET devices was achieved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

Energy absorbing efficiency is a key determinant of a structure’s ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures.

42 ENGINEERING↗

DOE OSTI2023

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors

Abstract Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m 2 /g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm 2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H 2 SO 4 . This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2003

Hydrostatic Stress Effects Incorporated Into the Analysis of the High-Strain-Rate Deformation of Polymer Matrix Composites

Procedures for modeling the effect of high strain rate on composite materials are needed for designing reliable composite engine cases that are lighter than the metal cases in current use. The types of polymer matrix composites that are likely to be used in such an application have a deformation response that is nonlinear and that varies with strain rate. The nonlinearity and strain rate dependence of the composite response is primarily due to the matrix constituent. Therefore, in developing material models to be used in the design of impact-resistant composite engine cases, the deformation of the polymer matrix must be correctly analyzed. However, unlike in metals, the nonlinear response of polymers depends on the hydrostatic stresses, which must be accounted for within an analytical model. An experimental program has been carried out through a university grant with the Ohio State University to obtain tensile and shear deformation data for a representative polymer for strain rates ranging from quasi-static to high rates of several hundred per second. This information has been used at the NASA Glenn Research Center to develop, characterize, and correlate a material model in which the strain rate dependence and nonlinearity (including hydrostatic stress effects) of the polymer are correctly analyzed. To obtain the material data, Glenn s researchers designed and fabricated test specimens of a representative toughened epoxy resin. Quasi-static tests at low strain rates and split Hopkinson bar tests at high strain rates were then conducted at the Ohio State University. The experimental data confirmed the strong effects of strain rate on both the tensile and shear deformation of the polymer. For the analytical model, Glenn researchers modified state variable constitutive equations previously used for the viscoplastic analysis of metals to allow for the analysis of the nonlinear, strain-rate-dependent polymer deformation. Specifically, we accounted for the effects of hydrostatic stresses. An important discovery in the course of this work was that the hydrostatic stress effects varied during the loading process, which needed to be accounted for within the constitutive equations. The model is characterized primarily by shear data, with tensile data used to characterize the hydrostatic stress effects.

Goldberg, Robert K.↗

NASA NTRS1990

STS-41 Space Shuttle mission report

The STS-41 Space Shuttle Program Mission Report contains a summary of the vehicle subsystem activities on this thirty-sixth flight of the Space Shuttle and the eleventh flight of the Orbiter vehicle, Discovery (OV-103). In addition to the Discovery vehicle, the flight vehicle consisted of an External Tank (ET) (designated as ET-39/LWT-32), three Space Shuttle main engines (SSME's) (serial numbers 2011, 2031, and 2107), and two Solid Rocket Boosters (SRB's), designated as BI-040. The primary objective of the STS-41 mission was to successfully deploy the Ulysses/inertial upper stage (IUS)/payload assist module (PAM-S) spacecraft. The secondary objectives were to perform all operations necessary to support the requirements of the Shuttle Backscatter Ultraviolet (SSBUV) Spectrometer, Solid Surface Combustion Experiment (SSCE), Space Life Sciences Training Program Chromosome and Plant Cell Division in Space (CHROMEX), Voice Command System (VCS), Physiological Systems Experiment (PSE), Radiation Monitoring Experiment - 3 (RME-3), Investigations into Polymer Membrane Processing (IPMP), Air Force Maui Optical Calibration Test (AMOS), and Intelsat Solar Array Coupon (ISAC) payloads. The sequence of events for this mission is shown in tabular form. Summarized are the significant problems that occurred in the Orbiter subsystems during the mission. The official problem tracking list is presented. In addition, each Orbiter problem is cited in the subsystem discussion.

Camp, David W.↗

NASA NTRS2010

The Vector Vortex Coronagraph: Sensitivity to Low-Order Aberrations, Central Obscuration, Chromaticism, and Polarization

The Vector Vortex Coronagraph is a phase-based coronagraph, one of the most efficient in terms of inner working angle, throughput, discovery space, contrast, and simplicity. Using liquid-crystal polymer technology, this new coronagraph has recently been the subject of lab demonstrations in the near-infrared, visible and was also used on sky at the Palomar observatory in the H and K bands (1.65 and 2.2 micrometers, respectively) to image the brown dwarf companion to HR 7672, and the three extasolar planets around HR 8799. However, despite these recent successes, the Vector Vortex Coronagraph is, as are most coronagraphs, sensitive to the central obscuration and secondary support structures, low-order aberrations (tip-tilt, focus, etc), bandwidth (chromaticism), and polarization when image-plane wavefront sensing is performed. Here, we consider in detail these sensitivities as a function of the topological charge of the vortex and design properties inherent to the manufacturing technology, and show that in practice all of them can be mitigated to meet specific needs.

large ground-based telescopes↗

NASA NTRS2007

NASA Tech Briefs, July 2007

Topics covered include: Miniature Intelligent Sensor Module; "Smart" Sensor Module; Portable Apparatus for Electrochemical Sensing of Ethylene; Increasing Linear Dynamic Range of a CMOS Image Sensor; Flight Qualified Micro Sun Sensor; Norbornene-Based Polymer Electrolytes for Lithium Cells; Making Single-Source Precursors of Ternary Semiconductors; Water-Free Proton-Conducting Membranes for Fuel Cells; Mo/Ti Diffusion Bonding for Making Thermoelectric Devices; Photodetectors on Coronagraph Mask for Pointing Control; High-Energy-Density, Low-Temperature Li/CFx Primary Cells; G4-FETs as Universal and Programmable Logic Gates; Fabrication of Buried Nanochannels From Nanowire Patterns; Diamond Smoothing Tools; Infrared Imaging System for Studying Brain Function; Rarefying Spectra of Whispering-Gallery-Mode Resonators; Large-Area Permanent-Magnet ECR Plasma Source; Slot-Antenna/Permanent-Magnet Device for Generating Plasma; Fiber-Optic Strain Gauge With High Resolution And Update Rate; Broadband Achromatic Telecentric Lens; Temperature-Corrected Model of Turbulence in Hot Jet Flows; Enhanced Elliptic Grid Generation; Automated Knowledge Discovery From Simulators; Electro-Optical Modulator Bias Control Using Bipolar Pulses; Generative Representations for Automated Design of Robots; Mars-Approach Navigation Using In Situ Orbiters; Efficient Optimization of Low-Thrust Spacecraft Trajectories; Cylindrical Asymmetrical Capacitors for Use in Outer Space; Protecting Against Faults in JPL Spacecraft; Algorithm Optimally Allocates Actuation of a Spacecraft; and Radar Interferometer for Topographic Mapping of Glaciers and Ice Sheets.

Source record↗

NASA NTRS1992

STS-42 Space Shuttle mission report

The STS-42 Space Shuttle Program Mission Report contains a summary of the vehicle subsystem operations during the forty-fifth flight of the Space Shuttle Program and the fourteenth flight of the Orbiter vehicle Discovery (OV-103). In addition to the Discovery vehicle, the flight vehicle consisted of the following: an External Tank (ET) designated as ET-52 (LWT-45); three Space Shuttle main engines (SSME's), which were serial numbers 2026, 2022, and 2027 in positions 1, 2, and 3, respectively; and two Solid Rocket Boosters (SRB's) designated as BI-048. The lightweight redesigned Solid Rocket Motors (RSRM's) installed in each one of the SRB's were designated as 360L020A for the left SRM and 360Q020B for the right SRM. The primary objective of the STS-42 mission was to complete the objectives of the first International Microgravity Laboratory (IML-1). Secondary objectives were to perform all operations necessary to support the requirements of the following: Gelation of Sols: Applied Microgravity Research (GOSAMR); Student Experiment 81-09 (Convection in Zero Gravity); Student Experiment 83-02 (Capillary Rise of Liquid Through Granular Porous Media); the Investigation into Polymer Membrane Processing (IPMP); the Radiation Monitoring Equipment-3 (RME-3); and Get-Away Special (GAS) payloads carried on the GAS Beam Assembly.

Fricke, Robert W.↗

DOE OSTI2020

Inverse methods for design of soft materials

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

36 MATERIALS SCIENCE↗